Construction Projects Status Tracking:  
A Real-Time Data-Driven Framework for Delay 

Management and Analysis 
 

 
 

Kambiz Radman 
 
 
 
 
 
 
A thesis submitted in fulfilment of the requirements for the degree of Doctor of 
Philosophy in Building and Construction 
 
Field of Building and Construction 
 
 
 
Supervised by: 
 
Associate Professor Mostafa Babaeian Jelodar 
Professor Ruggiero Lovreglio 
Professor Suzanne Wilkinson  
 
 
 
 
School of Built Environment, College of Science 
Massey University 
New Zealand 
 

 

 

February 2025



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Summary 

Construction delays remain one of the most critical challenges in project delivery, often 

resulting in cost overruns, schedule slippages, and weakened stakeholder confidence. Traditional 

delay management methods are largely reactive, relying on periodic reporting and fragmented 

communication across project teams. In contrast, the increasing availability of digital tools offers the 

opportunity to adopt more proactive, data-driven approaches. This study introduces a framework that 

centralises and analyses real-time project data from multiple stakeholders, including head contractors, 

subcontractors, consultants (via Building Information Modelling—BIM), and on-site teams. By 

integrating these diverse inputs into a unified Power BI dashboard, the framework enhances early 

delay detection, improves coordination, and supports timely decision-making. Earned Value (EV) 

metrics are embedded as key control points, providing early signals of deviations and potential risks. 

Despite these advances, several research gaps remain. Existing systems are often costly and 

complex, highlighting the need for simple, inexpensive, and user-friendly solutions. Real-time data 

acquisition and centralisation are still underdeveloped, limiting the speed and reliability of insights. 

Current practice focuses heavily on retrospective reporting, with limited capability for real-time 

analytics or predictive forecasting. Stakeholder communication and coordination continue to be 

fragmented, while systematic early notification systems for emerging delays are rarely implemented. 

Finally, there is a need to harness historical and live data together to enable predictive delay analytics. 

Addressing these gaps would help shift construction delay management from reactive intervention 

towards proactive risk mitigation. 

Guided by these gaps, the research is shaped around three central questions: (1) What causes 

delays in major construction projects, and how do these delays affect stakeholder collaboration? (2) 



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How are digital technologies currently being deployed to improve project performance in relation to 

delays and risks? (3) How can a new framework be designed and evaluated to strengthen early delay 

detection and enhance project outcomes? To answer these questions, five objectives are established. 

First, to identify and analyse the key project stakeholders and the principal causes of delay. Second, 

to review and assess the role of digital technologies in construction projects. Third, to develop a 

framework that integrates real-time data for enhanced monitoring, reporting, and early detection of 

delays. Finally, to evaluate this framework in practice, assessing its effectiveness in improving 

transparency, stakeholder coordination, and overall project performance. 

In doing so, this research contributes to the advancement of digital construction management 

by embedding real-time analytics into live project environments. The proposed framework not only 

improves transparency and resource allocation but also lays the foundation for predictive delay 

management, thereby aligning construction practices with the broader ambitions of Industry 4.0. 



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Acknowledgements 

I am deeply grateful to the many individuals, friends, and organizations who have helped me 

along the way on this research journey. I would especially like to thank Associate Professor Mostafa 

Babaeian Jelodar for his outstanding support as my supervisor and academic mentor. His 

enlightened guidance and careful consideration have been instrumental in completing this thesis. 

Moreover, Dr Mostafa has also been a great friend and supporter throughout my time at Massey 

University. I would also like to extend my appreciation to Professor Ruggiero Lovreglio (Rino), 

Professor Suzanne Wilkinson and Dr Eghbal Ghazizadeh ( Cybersecurity Manager – Mercury New 

Zealand) for their advice and support as my co-supervisor. Their invaluable insights and immense 

care have been extremely reassuring and an anchor of encouragement. 

I am genuinely grateful for the support of all of those who have helped me along the way. 

Without their help, this achievement would not have been possible. I would like to express my sincere 

respect and appreciation to all of those who participated in this study, including those who took part 

in the expert interviews and the surveys. Your insights and feedback were invaluable to the success 

of this project.  

I would also like to thank my dear son, Parsa (Jeff_Boi ������gooda ��) for his love and patient. 

he has always been there for me, no matter what. He has sacrificed many of his fun times because I 

was busy in this journey, but he has cheered me on, offered me lovely advice and words. I am so 

grateful for his love and support. He is always my “BFF” as he always loudly says “Best Friend 

Forever” ����� 

Also, thanks to my partner, Tina G. Bakhshayesh, for her constant support, for all the late 

nights and early mornings. She gave me support and help, taught me how to be patient, discussed 



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ideas and prevented several wrong turns. She also supported the family during much of my graduate 

studies. She could be my best friend, and my biggest fan. 

    Finally, I would like to thank all my friends for their love and support. You have all played 

a role in my journey, and I am so grateful for your presence in my life. Also, I would like to 

acknowledge my late friend, Dr Ayuba Jerry Likita (RIP), for his friendship and inspiration. Ayuba 

was a brilliant scholar and a kind soul. He was always willing to help others and had a passion for 

learning. I am so grateful for the time we spent together, and I will never forget his friendship. 

    I am truly blessed to have such a loving and supportive family. I could not have done this 

without you. Thank you. 

 

 

 

 

 

 

 

 

 

 

 



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Table of Contents 

Summary…… ....................................................................................................................................... i 

Acknowledgements ............................................................................................................................. iii 

List of Tables ....................................................................................................................................... x 

List of Figures ..................................................................................................................................... xi 

Glossary…… .................................................................................................................................... xiv 

Chapter 1: INTRODUCTION .......................................................................................................... 1 

1.1 OVERVIEW ...................................................................................................................................... 2 
1.2 BACKGROUND ............................................................................................................................... 6 
1.3 PROBLEM STATEMENT AND RESEARCH QUESTIONS ....................................................... 12 
1.4 RESEARCH OBJECTIVES ............................................................................................................ 15 
1.5 RESEARCH SCOPE ....................................................................................................................... 15 
1.6 RESEARCH METHODOLOGY .................................................................................................... 17 

1.6.1 Research philosophy - Overview ............................................................................................. 19 
1.6.2 Research philosophy - This doctoral thesis ............................................................................. 21 
1.6.3 Research conceptual framework .............................................................................................. 28 
1.6.4 Adopted methods for the research ........................................................................................... 30 
1.6.5 Ethics ....................................................................................................................................... 42 
1.6.6 Data Collection ........................................................................................................................ 43 

1.7 THESIS ORGANISATION ............................................................................................................ 47 
1.8 THESIS KEY DEFINITIONS ......................................................................................................... 50 

Chapter 2: DELAY CAUSES IN SMART AND COMPLEX CONSTRUCTION PROJECTS ... 51 

2.1 SUMMARY .................................................................................................................................... 51 
2.2 INTRODUCTION ........................................................................................................................... 52 
2.3 BACKGROUND ............................................................................................................................. 54 
2.4 LITERATURE REVIEW ................................................................................................................ 57 
2.5 RESEARCH METHODOLOGY AND DATA COLLECTION..................................................... 60 
2.6 RESULTS AND DISCUSSION ...................................................................................................... 64 
2.7 CONCLUSION ............................................................................................................................... 68 

Chapter 3: A SYSTEMATIC REVIEW OF DIGITAL TECHNOLOGIES IN CONSTRUCTION 

PROJECTS 70 

3.1 SUMMARY .................................................................................................................................... 70 



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3.2 INTRODUCTION ........................................................................................................................... 71 
3.3 RESEARCH METHODOLOGY .................................................................................................... 75 
3.4 FINDINGS AND RESULTS ........................................................................................................... 78 
3.5 RESEARCH APPROACHES ......................................................................................................... 80 
3.6 DISCUSSION .................................................................................................................................. 84 
3.7 PROPOSED FRAMEWORK .......................................................................................................... 90 
3.8 CONCLUSIONS ............................................................................................................................. 94 

Chapter 4: Real-Time Tracking and Analysis in Construction Projects: A RealCONs Framework

 97 

4.1 SUMMARY .................................................................................................................................... 97 
4.2 INTRODUCTION ........................................................................................................................... 98 
4.3 RESEARCH METHODOLOGY .................................................................................................. 102 
4.4 CASE STUDY SELECTION AND DATA PROCESSING ......................................................... 105 
4.5 THE PROPOSED CONCEPTUAL FRAMEWORK .................................................................... 110 
4.6 EXISTING PROCESS IMPROVEMENT WITH THE PROPOSED REALCONS FRAMEWORK
 119 
4.7 Results: Analysis and Discussion .................................................................................................. 132 
4.7.1. Data Collection: Existing Approach Vs RealCONs Approach ................................................. 134 
4.7.2. Early Delay Notification and Analysis Impact on Cost and Time ............................................ 138 
4.8 Discussion ...................................................................................................................................... 145 
4.9 Conclusions ................................................................................................................................... 149 

Chapter 5: RealCONs: A Digital Framework for Construction Reporting Accuracy and Early 

Delay Detection................................................................................................................................ 152 

5.1 SUMMARY .................................................................................................................................. 152 
5.2 INTRODUCTION ......................................................................................................................... 153 
5.3 BACKGROUND ........................................................................................................................... 155 
5.4 PROBLEM STATEMENT............................................................................................................ 158 
5.5 RESEARCH GOALS AND OBJECTIVES .................................................................................. 159 
5.6 RESEARCH METHODOLOGY .................................................................................................. 160 
5.7 MODELLING: RATIONAL UNIFIED PROCESS (RUP) .......................................................... 161 
5.8 RealCONs FRAMEWORK IN DETAIL ...................................................................................... 163 
5.9 CASE STUDY ............................................................................................................................... 170 
5.10 DISCUSSION AND RESULTS .................................................................................................... 177 

5.10.1 Site Data Collection Approach .............................................................................................. 177 
5.10.2 Implications of early delay detection and project performance analysis ............................... 183 

5.11 CONCLUSION AND FUTURE RESEARCH ............................................................................. 190 

Chapter 6: PRODUCTIVITY TRACKING IN CONSTRUCTION PROJECTS ........................ 192 



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6.1 SUMMARY .................................................................................................................................. 192 
6.2 INTRODUCTION ......................................................................................................................... 193 
6.3 RESEARCH AIM AND OBJECTIVES ....................................................................................... 194 
6.4 RESEARCH METHODOLOGY .................................................................................................. 194 
6.5 PRELIMINARY FINDINGS ........................................................................................................ 197 
6.6 RESEARCH SIGNIFICANCE ...................................................................................................... 197 

6.6.1 Time Efficiency and Cost-Effectiveness ............................................................................... 197 
6.6.2 Impact on Decision-Making .................................................................................................. 198 
6.6.3 Strategic Advantages ............................................................................................................. 198 
6.6.4 Visual Comparison of Time Allocation ................................................................................. 199 

6.7 ANALYTICAL DISCUSSION ..................................................................................................... 199 
6.7.1 Efficiency in Data Collection and Reporting ......................................................................... 200 
6.7.2 Enhanced Decision-Making .................................................................................................. 200 
6.7.3 Strategic and Financial Implications ..................................................................................... 201 
6.7.4 Technological Integration and Future Potential .................................................................... 201 

6.8 CONCLUSION ............................................................................................................................. 201 

Chapter 7: AUTOMATED CONSTRUCTION DELAY MANAGEMENT MECHANISM ..... 203 

7.1 SUMMARY .................................................................................................................................. 203 
7.2 INTRODUCTION ......................................................................................................................... 204 
7.3 BACKGROUND ........................................................................................................................... 208 
7.4 RESEARCH METHODOLOGY .................................................................................................. 210 
7.5 PROJECT ANALYSIS.................................................................................................................. 211 

7.5.1 Case Study: Introduction ....................................................................................................... 211 
7.5.2 Case study: problem statement .............................................................................................. 213 
7.5.3 Case study: EV analysis ........................................................................................................ 213 

7.6 CONCLUSION ............................................................................................................................. 219 

Chapter 8: A Digital Monitoring, Delay Detection and Visualisation Framework for Construction 

Projects: RealCONs ......................................................................................................................... 221 

8.1 SUMMARY .................................................................................................................................. 221 
8.2 INTRODUCTION ......................................................................................................................... 222 
8.3 STUDY BACKGROUND ............................................................................................................. 225 

8.3.1 Smartphone-Based QR Tracking for On-Site Progress Monitoring ...................................... 225 
8.3.2 Visual Analytics and Performance Monitoring Using Power BI .......................................... 225 
8.3.3 Comparative Contribution of RealCONs............................................................................... 226 

8.4 RESEARCH METHODOLOGY .................................................................................................. 227 
8.4.1 Proposed Framework Design ................................................................................................. 228 



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8.4.2 QR CODE DESIGN .............................................................................................................. 234 
8.4.3 Smartphone and Database Connection Design ...................................................................... 236 
8.4.4 Power BI Design .................................................................................................................... 237 
8.4.5 Case Study ............................................................................................................................. 239 
8.4.6 Test and Validation of the Proposed Framework  (RealCONs) ............................................ 244 

8.5 Discussion ...................................................................................................................................... 251 
8.6 Research Limitations and Solutions .............................................................................................. 253 
8.7 Conclusion and Future Research ................................................................................................... 254 

Chapter 9: OPTIMISING DELAY MANAGEMENT IN CONSTRUCTION PROJECTS ........ 256 

9.1 SUMMARY .................................................................................................................................. 256 
9.2 INTRODUCTION ......................................................................................................................... 257 
9.3 RESEARCH BACKGROUND ..................................................................................................... 258 
9.4 METHODOLOGY ........................................................................................................................ 265 

9.4.1 Delay Analysis Method Selection Approach ......................................................................... 265 
9.4.2 The Proposed Framework-RealCONs ................................................................................... 268 
9.4.3 Verification via case study .................................................................................................... 274 

9.5 DATA ANALYSIS AND RESULT .............................................................................................. 278 
9.5.1 AHP decision-making method ............................................................................................... 278 
9.5.2 Case study results-test and verification ................................................................................. 282 

9.6 DISCUSSION ................................................................................................................................ 298 
9.6.1 Research Key Findings .......................................................................................................... 299 
9.6.2 Reduced Delays and Cost Savings via Early Delay Notification .......................................... 301 
9.6.3 AHP-Multi-Criteria Decision Analysis (MCDA) .................................................................. 301 
9.6.4 Time Overrun Impact ............................................................................................................ 301 
9.6.5 Generalisation of Results ....................................................................................................... 302 

9.7 CONCLUSION ............................................................................................................................. 303 

Chapter 10: CONCLUSION AND RECOMMENDATIONS ....................................................... 305 

10.1 RESEARCH OVERVIEW ............................................................................................................ 305 
10.2 RESEARCH OBJECTIVES DEVELOPMENT ........................................................................... 310 
10.3 RESEARCH CONTRIBUTION AND RECOMMENDATIONS ................................................ 314 

10.3.1 Integration of Saunders’ Onion Model .................................................................................. 314 
10.3.2 Theoretical Contributions ...................................................................................................... 316 
10.3.3 Practical Implications ............................................................................................................ 317 

10.4 RESEARCH LIMITATIONS ........................................................................................................ 319 
10.5 RECOMMENDATIONS FOR FUTURE RESEARCH STUDIES .............................................. 320 

Appendices 321 



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Appendix 1: Ethics Approval and Documentation .......................................................................... 323 

Appendix 2: Facilitated Workshops Questions ................................................................................ 324 

Appendix 3: Statement of Contribution form .................................................................................. 327 

Bibliography 328 

 



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List of Tables 
Table 1.1: Research phases translation ............................................................................................................ 44 
Table 1.2: Doctoral thesis methodology according to each chapter ................................................................ 46 
Table 2.1: Delay factors and methods into five classes ................................................................................... 59 
Table 2.2: List of delay coded causes and classes ........................................................................................... 63 
Table 2.3: Relative Importance Indexes and rankings..................................................................................... 65 
Table 2.4: General ranking list of essential factors and classes ...................................................................... 66 
Table 2.5: Sample of RII calculation ............................................................................................................... 67 
Table 2.6: Spearman importance rank correlations between classes and stages ............................................. 67 
Table 2.7: Spearman importance rank correlations between classes ............................................................... 67 
Table 3.1: Research Selection Criteria: Inclusion and Exclusion. ................................................................... 77 
Table 3.2: Research strategy- Strings .............................................................................................................. 77 
Table 3.3: key advantages and disadvantages: single digital technologies ..................................................... 89 
Table 3.4: key advantages and disadvantages: combined digital technologies ............................................... 89 
Table 4.1: Selected Projects Details: E&I Value and Scale per Project ........................................................ 107 
Table 4.2: Key Factors of ESM and EVM [45] ............................................................................................. 127 
Table 4.3: Risk and Delay Early-Notification Factors .................................................................................. 128 
Table 4.4: Daily Data Collection Status: A Snapshot of 30 Days Received Reports .................................... 134 
Table 4.5: A Snapshot of Table 4.4 ............................................................................................................... 138 
Table 4.6: Earned Value Management (EVM) Matrix .................................................................................. 140 
Table 4.7: Earned Schedule Management (ESM) Matrix ............................................................................. 142 
Table 4.8: Behaviors of P-A and P-G through ESM analysis ....................................................................... 143 
Table 4.9: Time Extension Using SPI Method .............................................................................................. 144 
Table 4.10: Time Extension Using ES Method ............................................................................................. 144 
Table 4.11: Comparative Evaluation of RealCONs with Existing Digital Tools/Frameworks ..................... 147 
Table 4.12: Analytical Table for S1 Vs S2 Through ESM and EVM ........................................................... 147 
Table 5.1: Earned Value Metrics (PMI, 2021) .............................................................................................. 166 
Table 5.2: Research database SQL script ...................................................................................................... 169 
Table 5.3: Cabling and Cable Tray total quantity (must be installed) ........................................................... 171 
Table 5.4: Indexes analysis: Project A Vs Project B ..................................................................................... 178 
Table 5.6: Actual Average Time per Report (Minutes) ................................................................................. 179 
Table 5.5: Three-month data collection: Project A Vs Project B .................................................................. 180 
Table 5.7: Types of Errors in Report Accuracy Analysis: RealCONs vs. Existing System .......................... 182 
Table 5.8: A snipped of 90 Days early delay identification via EV metrics ................................................. 185 
Table 5.9: RealCONs Vs Existing Digital Tools/Frameworks ...................................................................... 189 
Table 6.1: Before and after DFD ................................................................................................................... 196 
Table 6.2: Average of Time Comparison between Current and proposed methods ...................................... 198 
Table 7.1: Earned Value Parameters ............................................................................................................. 209 
Table 7.2: Total Planned QTY: Cabling, Containment and Fittings ............................................................. 213 
Table 7.3: Earned Value Metrics ................................................................................................................... 214 
Table 8.1: Comparative overview of previous studies and RealCONs framework ....................................... 226 
Table 8.2: RealCONs Core and Supporting Components ............................................................................. 229 
Table 8.3: QR Code Data key fields and an example .................................................................................... 234 
Table 8.4: QR Code Generation .................................................................................................................... 234 
Table 8.5: Selected Projects Details: E&I Scope........................................................................................... 240 
Table 8.6: Earned Value Metrics [81] ........................................................................................................... 242 



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Table 8.7: Power BI Data Tables Grouped By Source Type ......................................................................... 244 
Table 8.8: Missing Data ................................................................................................................................ 245 
Table 8.9: Earned Value Parameters on the “KIT” Project ........................................................................... 246 
Table 8.10: Descriptive Statistics for CPI and SPI ........................................................................................ 247 
Table 8.11: Mann-Whitney U Test on CPI (After Missing vs. All Other Days) ........................................... 248 
Table 8.12: Wilcoxon Signed-Rank Test on 51 Paired Days ........................................................................ 248 
Table 8.13: Managerial benefits of applying statistical techniques to S1 data .............................................. 251 
Table 8.14: RealCONs Framework Vs Current Digital Tools ...................................................................... 252 
Table 9.1: TIA popular methods at the glance .............................................................................................. 262 
Table 9.2: Descriptions of Criteria ................................................................................................................ 263 
Table 9.3: Earned Value Metrics ................................................................................................................... 270 
Table 9.4: General View of  Methodology .................................................................................................... 273 
Table 9.5: Depicts the details of the case study ............................................................................................. 274 
Table 9.6: Pairwise Comparisons of Criteria ................................................................................................. 279 
Table 9.7: Normalisation ............................................................................................................................... 280 
Table 9.8: Weights ......................................................................................................................................... 281 
Table 9.9: Each Method Priority Vectors ...................................................................................................... 281 
Table 9.10: Delay Event Overview ............................................................................................................... 285 
Table 9.11: Comparison between SMART and Traditional approaches ....................................................... 288 
Table 9.12: Case Study SPI and CPI ............................................................................................................. 292 
Table 10.1: Summary of doctoral thesis objectives according to each chapter ............................................. 306 
 

List of Figures 
Figure 1.1: Research onion model (Fellows & Liu, 2021) .............................................................................. 20 
Figure 1.2: This doctoral thesis onion research model .................................................................................... 28 
Figure 1.3: Thesis Mixed-Method Onion Model ............................................................................................. 30 
Figure 1.4: Overview of Objectives, Chapters, Phases, and Methodologies Relationships ............................ 31 
Figure 1.5: Research Road Map ...................................................................................................................... 45 
Figure 3.1: Search strategy flowchart .............................................................................................................. 76 
Figure 3.2: A number of publications Vs. published years ............................................................................. 79 
Figure 3.3: A number of publications Vs. Study Theme. ................................................................................ 79 
Figure 3.4: Publication distribution by research method and technologies. .................................................... 80 
Figure 3.5: The Proposed Framework ............................................................................................................. 93 
Figure 4.1: Methodology’s Steps................................................................................................................... 102 
Figure 4.2: Existing Drawings/3D Data Flow ............................................................................................... 108 
Figure 4.3: Existing Project Data Flow Interaction ....................................................................................... 108 
Figure 4.4: Existing Process Activity Diagram ............................................................................................. 109 
Figure 4.5: Existing Process Sequence Diagram ........................................................................................... 109 
Figure 4.6: The Conceptual Framework of RealCONs ................................................................................. 111 
Figure 4.7: Type and Direction of Data Flow Throughout RealCONs Framework ...................................... 111 
Figure 4.8: Section 4.1 Mapping ................................................................................................................... 113 
Figure 4.9: GUI On Smart Devices ............................................................................................................... 113 
Figure 4.10: Interaction Languages ............................................................................................................... 113 



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Figure 4.11: SQL_Database Mapping ........................................................................................................... 114 
Figure 4.12: BIM_Consultant Mapping ........................................................................................................ 115 
Figure 4.13: Oracle_P6 Mapping .................................................................................................................. 116 
Figure 4.14: Oracle_Aconex Mapping .......................................................................................................... 117 
Figure 4.15: Power BI Modelling Cutoff View ............................................................................................. 118 
Figure 4.16: Power BI Mapping .................................................................................................................... 118 
Figure 4.17: Technical View of RealCONs ................................................................................................... 121 
Figure 4.18: Steps of RealCONs framework process .................................................................................... 121 
Figure 4.19: Data Analysis and Visualisation Mapping ................................................................................ 127 
Figure 4.20: Data-Driven Architecture of Real-Time Decision-Making ...................................................... 129 
Figure 4.21: A Simple View of Data Flow Via Existing and RealCONs Approaches .................................. 133 
Figure 4.22: Daily Data Collection: Planned Vs Existing Vs Proposed ........................................................ 135 
Figure 4.23: Daily Variance Percentage in Data Collection .......................................................................... 135 
Figure 4.24: Cost Efficiency .......................................................................................................................... 141 
Figure 4.25: Schedule Efficiency .................................................................................................................. 141 
Figure 4.26: Cost Variance ............................................................................................................................ 141 
Figure 4.27: Schedule Variance .................................................................................................................... 141 
Figure 4.28: Schedule Variance (SVc) .......................................................................................................... 143 
• Figure 4.29: Time Extension: RealCONs Vs Existing Approach ......................................................... 145 
Figure 5.1: Overview: existing data collection process for data presentation ............................................... 159 
Figure 5.2: Research Methodology ............................................................................................................... 161 
Figure 5.3: Research RUP Structure.............................................................................................................. 163 
Figure 5.4: RealCONs framework's use case diagram between different users ............................................ 164 
Figure 5.5: ReaLCONs framework' sequence diagram: Collected site data stage to reporting stage ........... 166 
Figure 5.6: RealCONs integrated mapping approach .................................................................................... 166 
Figure 5.7(a)- Site Actual Data from Smartphone to Construction_Area ..................................................... 167 
Figure 5.8: UML's classes and attributes ....................................................................................................... 168 
Figure 5.9: Use case: existing reporting approach ........................................................................................ 172 
Figure 5.10: SIPOC model diagram for P-A Project ..................................................................................... 174 
Figure 5.11: Project P-A (existing approach)' Activity Diagram .................................................................. 175 
Figure 5.12: Project P-A (existing approach)' Sequence Diagram ................................................................ 176 
Figure 5.13: Data Accuracy: RealCONs Vs Existing Approach ................................................................... 181 
Figure 5.14: Data Accuracy variance: RealCONs Vs Existing approach ...................................................... 182 
Figure 5.15: Technical view: earned value (EV) metrics analysis ................................................................ 184 
Figure 5.16: Cost Efficiency .......................................................................................................................... 186 
Figure 5.17: Schedule Efficiency .................................................................................................................. 186 
Figure 5.18: Data Flow Sequence of Figure 5.6 ............................................................................................ 187 
Figure 5.19: Project Main Dashboard ( Digital Report) ................................................................................ 188 
Figure 5.20: Earned value tracking................................................................................................................ 188 
Figure 5.21: Breakdown Analysis ................................................................................................................. 188 
Figure 5.22: Delay trend ................................................................................................................................ 188 
Figure 5.23: Cost variance trend ................................................................................................................... 188 
Figure 5.24: Building sketch monitoring ....................................................................................................... 188 
Figure 6.1: The proposed method process ..................................................................................................... 197 
Figure 6.2: Time Comparison among used Apps .......................................................................................... 199 
Figure 7.1: Earned Value parameters interaction .......................................................................................... 210 
Figure 7.2: Research Methodology ............................................................................................................... 211 
Figure 7.3: Schematic view of MHP (2x buildings and 1x walkway in middle) .......................................... 212 
Figure 7.4: 3D view of MHP ......................................................................................................................... 212 
Figure 7.5: PV vs EV vs AC ......................................................................................................................... 216 
Figure 7.6: CPI vs SPI ................................................................................................................................... 217 



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Figure 7.7: CV vs SV .................................................................................................................................... 218 
Figure 8.1: Research Method Framework ..................................................................................................... 228 
Figure 8.2: Research Conceptual Model ....................................................................................................... 228 
Figure 8.3: Proposed Research Framework and Its Classes .......................................................................... 229 
Figure 8.4: Sequence Diagram: Site to Insight .............................................................................................. 232 
Figure 8.5: Database Schema and Relationships ........................................................................................... 232 
Figure 8.6: QR Codes display locations ........................................................................................................ 235 
Figure 8.7: point to point ............................................................................................................................... 235 
Figure 8.8: point to the threshold .................................................................................................................. 236 
Figure 8.9: GUI on Smartphone .................................................................................................................... 237 
Figure 8.10: Power BI Overview Model ....................................................................................................... 238 
Figure 8.11: Data Model ................................................................................................................................ 242 
Figure 8.12: ................................................................................................................................................... 243 
Figure 8.13: Daily Received Reports ............................................................................................................ 245 
Figure 8.14: Regression analysis of SPI (S1) ................................................................................................ 249 
Figure 8.15: Forecasted Duration (TEAC) .................................................................................................... 250 
Figure 8.16: CPI and SPI Trends (S1 vs S2) ................................................................................................. 250 
Figure 9.1: Delay Analysis Methods between 2010 and 2024 ...................................................................... 261 
Figure 9.2: Mind map of facilitated workshop scenario ................................................................................ 268 
Figure 9.3: RealCONs' framework' uses case diagram between different users ........................................... 271 
Figure 9.4: RealCONs' framework' sequence diagram .................................................................................. 272 
Figure 9.5: 3D view of MH1 (2x buildings and 1x walkway in middle) ...................................................... 276 
Figure 9.6 (A): As planned programme ......................................................................................................... 276 
Figure 9.7: Case Study diagram .................................................................................................................... 277 
Figure 9.8: Pairwise Comparison of Alternatives.......................................................................................... 284 
Figure 9.9: Normalisation .............................................................................................................................. 284 
Figure 9.10: SPI Chart ................................................................................................................................... 293 
Figure 9.11: CPI Chart .................................................................................................................................. 294 
Figure 9.12: Project progress over time: traditional and SMART approaches .............................................. 295 
Figure 9.13: Total delay comparison on individual tasks .............................................................................. 296 
Figure 9.14: Cost Overrun and Time Overrun .............................................................................................. 297 
Figure 9.15: Key Criteria Radar Chart .......................................................................................................... 298 
Figure 10.1: Doctoral research objectives, methodologies and outcomes ..................................................... 311 
 



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Glossary 

AD Actual Duration 

API Application Programming Interface 

BIM Building information modelling 

CAD Computer-Aided Design 

CPI Cost Performance Index 

CPM Critical Path Method 

CR Change request 

CV Cost Variance 

DPI Duration Performance Index technique 

DSS Decision support system 

EVA Earned Value Analysis 

EVM Earned Value Management 

EVM Earned Value Management 

GIS Geographic Information System 

ICT Information and Communication Technology 

IFC Issue For Construction (Design) 

IoT Internet of Things 

IT Information Technology 

MBIE Ministry of Business, Innovation and Employment 

NZD New Zealand Dollar 

PV Planned Value 

RealCONs Real-Time Construction Project Analysis Framework 

RFI  Request For Information 

RFID Radio Frequency Identification 

RFQ  Request For Quotation 

SPI Schedule Performance Index 

SQL Structured Query Language  

SV Schedule Variance 
 



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Chapter 1: INTRODUCTION 

This opening chapter sets the foundation for the thesis by outlining the research problem, 

aims, objectives, and guiding questions. It introduces the methodological approach adopted in the 

study and explains the overall structure of the thesis. The central focus of the research is the 

development of a framework for real-time delay detection and management in construction projects, 

with particular attention to how digital technologies can support stakeholder coordination and 

performance monitoring. 

The research is guided by three key questions: (1) What are the primary causes of delays in 

major construction projects, and how do they affect stakeholder collaboration? (2) How are digital 

technologies currently being used to improve project performance, particularly in relation to delays 

and risks? (3) How can a proposed framework be designed and evaluated to strengthen early delay 

detection and project outcomes? To address these questions, five objectives are established: 

identifying stakeholders, analysing the causes of delays, reviewing digital technologies, developing 

the proposed framework, and evaluating its effectiveness. A mixed-methods research design is 

employed, combining qualitative and quantitative approaches to generate both depth and breadth of 

insight. Real-time data analytics, visualisation through Power BI, and earned value (EV) performance 

metrics form the analytical backbone of the study. The empirical work is centred on case studies 

drawn from major and smart construction projects in New Zealand, providing a practical and 

contextually relevant testbed for the framework. Definitions of what constitute “major projects” and 

“smart projects” are provided in Section 1.8 to ensure conceptual clarity. 

 



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The remainder of this thesis is structured as follows. Following this introductory chapter, 

Chapter 2 presents the literature review, covering the concepts of delay, real-time digital technologies 

in construction management, and international best practices. Chapter 3 outlines the research 

methodology, including the rationale for the mixed-methods approach. Chapter 4 presents the case 

study findings, while Chapter 5 details the development and application of the proposed framework. 

Chapter 6 provides an evaluation of the framework and its implications for construction management 

practice. Finally, Chapter 7 concludes the thesis by summarising the contributions, highlighting 

limitations, and identifying directions for future research. 

1.1 OVERVIEW 

Understanding delays in major construction projects and identifying technological solutions 

for their management requires a clear grasp of both the underlying causes of delays and the tools 

available to address them. Delays remain one of the most common and disruptive challenges in large-

scale projects, often resulting in cost inflation, schedule overruns, contractual disputes, and 

reputational damage (Amini, Rezvani, Tabassi, & Malek Sadati, 2023; Shanmugapriya & 

Subramanian, 2013). They arise from a wide range of sources, including poor planning, design errors, 

unforeseen site conditions, regulatory and contractual constraints, labour shortages, and external 

factors such as extreme weather (Demirkesen & Tezel, 2022; Doloi, Sawhney, Iyer, & Rentala, 2012; 

Durdyev & Hosseini, 2020). As projects grow in scale and complexity, the impacts of such delays 

become even more significant, making it essential to develop effective methods for their analysis, 

mitigation, and reduction (Abdallah et al., 2022; Memon, Rahman, & Azis, 2011; Varajão, 

Magalhães, Freitas, & Rocha, 2022; Williams, Vo, Samset, & Edkins, 2019). Traditional approaches 

such as the Critical Path Method (CPM), Earned Value Management (EVM), risk registers, and 

structured change management procedures have been widely applied to monitor and mitigate delays. 



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While these methods remain valuable, their reliance on periodic reporting and static data limits their 

effectiveness in fast-moving project environments. With increasing project complexity, there is a 

growing need for more dynamic and predictive solutions (Hammad et al., 2019; Pan & Zhang, 2023; 

Piras, Muzi, & Tiburcio, 2024).  

Recent technological developments have transformed the landscape of delay management. 

Digital tools such as Building Information Modelling (BIM), artificial intelligence (AI), the Internet 

of Things (IoT), drones, and advanced data analytics provide powerful capabilities for predicting, 

monitoring, and reducing delays (Amini et al., 2023; Love, Lucas, Kelbert, & Bedrosian, 2018; 

Obakin, Afolami, & Akande, 2024). BIM enables the creation of detailed digital models that can be 

linked with real-time progress data, allowing early identification of clashes or changes likely to cause 

delays (Deacon & Van der Lingen, 2015; Ekanayake, Wong, Fini, & Smith, 2021; Petroutsatou, 

2022), AI algorithms, informed by historical project data and external factors such as weather, can 

forecast potential disruptions. IoT devices and sensors capture site conditions and equipment usage 

in real time, while drones and laser scanners enhance site monitoring (Elshaer, 2013; Gemino, Horner 

Reich, & Serrador, 2021; Reiff & Schlegel, 2022). Together, these technologies improve accuracy, 

enhance predictive capability, and support better resource allocation (Hammad et al., 2019; Pan & 

Zhang, 2023; Piras et al., 2024). Despite their promise, significant challenges remain. High 

implementation costs, technical complexity, fragmented data systems, and concerns around data 

security continue to limit widespread adoption (Azhar, Carlton, Olsen, & Ahmad, 2011; Honnappa 

& Padala, 2022; Parsamehr, Perera, Dodanwala, Perera, & Ruparathna, 2023; Radman, Jelodar, 

Lovreglio, Ghazizadeh, & Wilkinson, 2022). Current studies also show that while real-time 

monitoring technologies are increasingly applied, they are often implemented in isolation rather than 

as part of an integrated, centralised framework. Many existing data fusion models lack the ability to 

unify progress tracking across disciplines, especially in complex multi-stakeholder environments 

(Hassan, Kowalska, & Ashraf, 2023; Keyvanfar, Shafaghat, & Awanghamat, 2021). IoT devices 



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provide real-time data on project progress, equipment usage, and site conditions, enabling 

stakeholders to take proactive measures (Jain et al., 2021; Rao et al., 2022). 

The integration of technology into delay management presents both benefits and challenges. 

The benefits include improved accuracy and efficiency, enhanced predictive capabilities, and better 

resource allocation (Cheng & Ugrinovskii, 2016). However, challenges include high initial costs, 

technical complexity, and concerns about data security (Du, Zou, Shi, & Zhao, 2018). Despite these 

challenges, real-time data analytics, predictive technologies, and collaborative platforms have proven 

crucial in improving project timelines and reducing delays. For this purposes, cost overruns and 

schedule delays are prevalent in construction projects, leading to disputes and claims. Recent studies 

emphasize the importance of real-time tracking and timely progress reporting for smart construction 

management. Technologies like remote sensing, RFID tags, and 3D laser scanners have been used to 

acquire real-time data on construction sites (Moselhi, Bardareh, & Zhu, 2020; Rao et al., 2022). 

Moreover, current data fusion models lack a centralised progress tracking system, particularly in 

multidisciplinary projects (Fadhel et al., 2024). 

These limitations highlight clear research gaps. First, there is a need for simple, cost-effective, 

and user-friendly systems that can be easily adopted by diverse stakeholders. Second, stronger 

mechanisms for real-time data acquisition and centralisation are required to avoid fragmentation. 

Third, existing systems focus more on retrospective analysis than on predictive analytics, leaving a 

gap in proactive delay forecasting. Fourth, real-time early-warning and notification systems are 

underdeveloped, limiting the ability of project teams to act before delays escalate. Finally, greater 

integration is needed to support collaboration and communication across contractors, subcontractors, 

consultants, and clients, especially in large multidisciplinary projects. Addressing these gaps would 

advance delay management from reactive monitoring to proactive, predictive control, offering a 

critical step forward for construction project management. 



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This study investigates the frequent causes and consequences of delays in major construction 

projects. Common factors include errors in planning and design, shortages of resources, regulatory 

hurdles, and external influences like adverse weather. The research evaluates current management 

strategies and identifies a critical gap in the early detection of such delays. To address this, a novel, 

real-time, and data-driven analytical framework named RealCONs is introduced Specifically 

designed for the electrical and instrumentation trades, RealCONs is developed in alignment with 

SMART specifications: Simple, Measurable, Analytical, and Real-Time. The framework leverages 

real-time data to provide proactive solutions, enabling early delay notifications and minimising 

project disruptions. As such, this doctoral research offers significant contributions through its 

innovative approach to real-time delay management: 

1. Seamless Integration with Existing Tools: RealCONs integrates familiar tools used by 

construction project teams, such as Oracle Primavera (P6), MS Project, Oracle Aconex, BIM 

(Revit, Navisworks), QR codes, smartphones, and Power BI. This allows the framework to be 

adopted without requiring additional training or new skills, making it both efficient and cost-

effective. 

2. Data Translation and Visualisation: For this purpose, this doctoral research used Power BI. 

This platform is the core of the framework, connecting the tools through SQL Server, APIs, 

server-side scripting (PHP), Python, and SQL. RealCONs translates data from various sources- 

such as BIM, P6, SQL Server, and Aconex- into visual and analytical insights using Power BI, 

enhancing decision-making. 

3. Improved Communication and Delay Classification: The framework enhances 

communication accuracy and flexibility by automating data exchange and recognizing 

responsible parties across different project sites (e.g., issues reported from site to consultant via 

head contractor). It also categorizes delays based on their nature, improving overall project 

communication and accountability. 



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4. Real-Time Analysis with Earned Value Metrics: RealCONs integrates earned value metrics, 

such as the Schedule Performance Index (SPI) and Cost Performance Index (CPI), to provide 

early identification of delay impacts on time and cost. This helps project managers forecast 

costs (particularly labour) with greater accuracy, reliability, and accessibility. 

The key novelty of RealCONs is that it delivers early-stage delay notifications through a low-

cost, process-based framework that integrates real-time data without imposing additional financial 

burdens. This results in high efficiency in time and cost savings for companies. To validate the 

effectiveness of the framework, both theoretical and experimental methodologies will be employed. 

Six months of data from smart construction projects in New Zealand were analysed to demonstrate 

the framework's ability to track progress and assess productivity. 

1.2 BACKGROUND 

Most of time delays in construction projects are a pervasive issue that can have significant 

financial, contractual, and reputational consequences (Akinsiku & Akinsulire, 2012; Milind Mehta, 

Chang, Oh, Kwon, & Kim, 2022). The scale and complexity of modern construction projects, 

especially major projects, increase the likelihood of delays, making them a critical area of study in 

project management and construction engineering (Bahamid, Doh, Khoiry, Kassem, & Al-Sharafi, 

2022; Qazi, Quigley, Dickson, & Kirytopoulos, 2016). Understanding the causes of these delays and 

developing effective strategies for their mitigation has been a focal point of research in construction 

management over the past several decades. Research has consistently highlighted the multifaceted 

nature of delays in construction projects (Bahamid et al., 2022; Ingle & Mahesh, 2022; Williams, 

2016). These delays often arise from a combination of technical, operational, and external factors that 

disrupt the planned schedule. Previous studies such as those by Assaf and Al-Hejji (2006) and 

Gómez-Cabrera, Gutierrez-Bucheli, and Muñoz (2024) identified common causes of delays, 



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including design changes, inadequate project planning, and resource shortages. More recent studies 

have expanded on these causes, highlighting the growing complexity of modern construction projects 

and the increased coordination required between multiple stakeholders, including contractors, 

subcontractors, consultants, and government agencies (Alkilani & Loosemore, 2024). 

The consequences of these delays are substantial, leading to cost overruns, project disputes, 

and sometimes even the failure of projects (Alkilani & Loosemore, 2024). As per Osei-Asibey et al. 

(2024), delays in large-scale construction projects not only inflate budgets but also erode stakeholder 

confidence and damage the reputation of the firms involved. In major infrastructure projects, where 

timelines are often linked to political or public commitments, delays can have even more profound 

effects, leading to public dissatisfaction and legal disputes (Floricel, Abdallah, Hudon, Petit, & 

Brunet, 2023; Musenero, Baroudi, & Gunawan, 2023). 

Traditionally, delay analysis and mitigation strategies in construction projects have relied on 

several well-established methodologies, such as the Critical Path Method (CPM) and Earned Value 

Management (EVM) (Akram, Habib, & Deveci, 2023; Ekanayake et al., 2021; Khan, Ali, Garai-

Fodor, & Csiszárik-Kocsir, 2023). These methods provide project managers with tools to assess 

which tasks are critical to the project’s completion and how any delays in these tasks may impact the 

overall timeline. According to Hegazy, Saad, and Mostafa (2020), CPM remains one of the most 

commonly used techniques for understanding the sequential relationships between activities and 

identifying critical tasks that must be managed to avoid project delays. 

As projects become more complex, these traditional methods have proven to be insufficient 

on their own for managing the dynamic nature of modern construction environments. Projects now 

require more proactive delay mitigation techniques that not only react to delays but also predict them 

before they occur (Grzeszczyk, Sainati, & Unterhitzenberger, 2024; Gurgun, Koc, & Kunkcu, 2024). 

The concept of risk registers and contingency planning, as noted by Padil, Bakhary, Abdulkareem, 



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Li, and Hao (2020), has gained traction as part of a broader strategy to anticipate potential sources of 

delay and mitigate their impact. These methods rely on historical project data and expert judgment to 

estimate risk, but they still face limitations in handling real-time project data and rapidly changing 

site conditions. 

In recent years, technological advancements have introduced new tools for delay 

management, particularly in the areas of real-time data analytics and Building Information Modelling 

(BIM) (Lauria & Azzalin, 2024; Radman, Jelodar, Lovreglio, Ghazizadeh, et al., 2022). These 

technologies have revolutionized how project delays are detected, managed, and mitigated. BIM, for 

instance, offers a highly visual and collaborative approach to project planning and execution, allowing 

for greater foresight in identifying potential delays arising from design or coordination issues (Bryde, 

Broquetas, & Volm, 2013). By creating digital models of construction projects, BIM integrates 

information from all project stakeholders, enabling more precise planning and reducing the risk of 

delays due to miscommunication or design flaws (Cannavacciuolo, Ferraro, Ponsiglione, Primario, & 

Quinto, 2023; Darko, Chan, Yang, & Tetteh, 2020). 

Moreover, IoT applications and real-time data analytics have opened up new avenues for 

tracking project progress. As noted by Li et al. (2018), IoT-enabled sensors installed on construction 

sites and equipment provide continuous streams of data on project performance metrics, such as 

worker productivity, equipment usage, and material availability (Radman, Babaeian Jelodar, 

Ghazizadeh, & Wilkinson, 2021a; Radman, Jelodar, Lovreglio, Ghazizadeh, et al., 2022). This real-

time data, when analysed through machine learning algorithms and advanced analytics platforms, 

offers project managers the ability to predict potential delays more accurately and take pre-emptive 

action before they escalate (Karamthulla, Muthusubramanian, Tadimarri, & Tillu, 2024). 

The adoption of real-time data in construction project management has marked a paradigm 

shift in how delays are managed. Real-time data allows for immediate updates on site conditions, 



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project progress, and material logistics, providing a dynamic view of project status (Dardouri et al., 

2023; Yang, Li, Yu, & Zhong, 2024). This immediacy in data availability has the potential to 

significantly reduce delays by enabling project managers to quickly identify and respond to any 

deviations from the project schedule. The integration of real-time data through platforms like Power 

BI, combined with IoT and API-driven data collection methods, allows for centralized data analysis 

and sharing among all stakeholders, facilitating quicker decision-making and accountability 

(Adeniran, Efunniyi, Osundare, & Abhulimen, 2024; Banerjee, 2022). 

The implementation of real-time data analytics has also enabled the application of predictive 

models, which leverage historical data to forecast potential delays. By utilising AI and machine 

learning algorithms, construction projects can now predict delays based on factors such as weather 

patterns, equipment usage, and workforce productivity (Datta, Islam, Sobuz, Ahmed, & Kar, 2024; 

Gondia, Siam, El-Dakhakhni, & Nassar, 2020). This predictive capability provides project managers 

with valuable insights into potential risks before they materialize, allowing for more proactive delay 

management strategies. While the application of technology in delay management offers significant 

advantages, such as enhanced efficiency, accuracy, and predictive capabilities, it also comes with its 

own set of challenges. The upfront cost of implementing technologies like BIM, IoT, and AI can be 

prohibitive, particularly for smaller projects or firms with limited resources (Hall, Durdyev, Koc, 

Ekmekcioglu, & Tupenaite, 2023). Additionally, the complexity of these technologies necessitates 

specialized training for personnel, which can introduce a learning curve and further increase costs 

(Abdelalim, Essawy, Alnaser, Shibeika, & Sherif, 2024). 

Another major challenge is data security (Ahmad, Rasool, Javed, Baker, & Jalil, 2021). As 

construction projects increasingly rely on cloud-based platforms for real-time data sharing and 

analysis, they become vulnerable to cyber-attacks or data breaches (Sharma & Barua, 2023). 

Protecting sensitive project data is paramount, and failure to do so can lead to significant financial 

and legal repercussions. Despite these challenges, the benefits of utilising advanced technologies in 



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managing construction delays cannot be understated. As construction projects grow in size and 

complexity, the ability to leverage real-time data, predictive analytics, and collaborative platforms 

will continue to be essential for ensuring projects are delivered on time and within budget (Rahaman, 

Rozony, Mazumder, & Haque, 2024; Zhu, Hwang, Ngo, & Tan, 2022). 

In major construction projects, early-stage delay notifications play a critical role in identifying 

and addressing potential disruptions to the project timeline (Gwynne, Purser, Boswell, & Sekizawa, 

2012; Tinaburri, 2022). These notifications are issued during the projects’ phases to alert stakeholders 

about occurred or even potential delays that could impact the overall schedule and cost (Nikander, 

2002; Ye et al., 2023). Timely recognition of these delays allows for more efficient mitigation 

strategies, thus minimizing the risk of prolonged disruptions. However, detecting and notifying delays 

at early stages is challenging due to the complexity of construction processes, the dynamic 

environment, and the involvement of multiple stakeholders (Alvand, Mirhosseini, Ehsanifar, 

Zeighami, & Mohammadi, 2023; Sharma & Barua, 2023). 

To manage this complexity, several data analysis methods are commonly used to predict and 

analyse delays. The four popular data analysis techniques include windows analysis method, time 

impact analysis method, impacted as-planned analysis method and as planned vs. as-built analysis 

method. While these methods are widely adopted, they have limitations concerning real-time data 

integration. For instance, the windows analysis method involves assessing delays by periodically 

updating the project schedule at specific phases. It is often referred to as the "snapshot technique" or 

"contemporaneous period analysis", so the process is time-consuming and costly (Alkass, Mazerolle, 

& Harris, 1996; Gurgun et al., 2024). Time Impact Analysis (TIA) is a variation of the Windows 

Analysis method, focusing on the impact of individual delay events rather than broader project 

periods. However, TIA applies real-time Critical Path Method (CPM) analysis and can be used during 

or after project execution (Gutta, Bammidi, Batchu, & Kanchepu, 2024). Guida and Sacco (2019) 

stated the Impacted As-Planned method evaluates delays by adding them as activities to the 



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contractor's original CPM schedule. The delays are inserted in the order they occur to assess how they 

impact the overall timeline. They added the types of delays are not analysed in this method. Finally, 

the As Planned vs. As-Built method compares the original planned schedule to the actual as-built 

schedule, showing delays (including excusable, non-excusable, and concurrent delays) on the as-built 

schedule. The claimant seeks compensation for the difference between the planned and actual 

completion dates. Both schedules are used to identify the critical path and the overall delay impact 

(Guida & Sacco, 2019; Gutta et al., 2024). 

Given the limitations of traditional analysis techniques, the Time Impact Analysis (TIA) 

method has been proposed by the Society of Construction Law (SCL) Protocol and endorsed in the 

AACE International’s RP 29R-03 for addressing delay claims and forensic schedule analysis 

(Çevikbaş & Işık, 2021; Çevikbaş, Okudan, & Işık, 2022). TIA focuses on assessing the impact of 

changes or delays on the project schedule by inserting delays into the as-planned schedule and 

recalculating the completion date. This method is particularly effective in addressing real-time issues 

as it allows for periodic updates and the re-evaluation of potential delays as the project progresses, 

providing a dynamic and responsive approach to schedule management (Yousri, Sayed, Abdelalim, 

& Farag, 2024). 

In terms of measuring the impact of identified delays at the early stages of a project, earned 

value metrics such as Schedule Performance Index (SPI) and Cost Performance Index (CPI) are 

valuable tools (Institute, 2021; Lipke, Zwikael, Henderson, & Anbari, 2009). SPI evaluates the 

efficiency of time usage relative to the planned schedule, while CPI assesses the cost efficiency of 

the project (Institute, 2021; Zohoori, Verbraeck, Bagherpour, & Khakdaman, 2019). Both metrics 

offer insights into how early-stage delays affect project time and cost forecasting (Institute, 2021; 

Zohoori et al., 2019). For instance, an SPI value below 1 indicates that the project is behind schedule, 

and this can be used in conjunction with TIA to assess the cumulative effect of delays on the project’s 

expected completion date and budget. Similarly, a CPI value less than 1 signifies cost overruns, which 



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can be correlated with delays identified in the early stage to forecast their impact on the overall project 

cost. Furthermore, the five criteria of the RealCONs—Specific, Measurable, Achievable, Relevant, 

and Time-bound—can be aligned with the TIA method and early-stage delay notifications to enhance 

project management (Radman, Jelodar, Lovreglio, Ghazizadeh, et al., 2022).  

Specific and Measurable aspects ensure that delay notifications are precise and quantifiable, 

which is essential for accurate impact analysis (PMI, 2018). Achievability ensures that the proposed 

mitigation strategies are feasible within the project constraints. Relevance ensures that notifications 

focus on critical aspects of the project, and the Time-bound criterion emphasizes the importance of 

timely communication (Radman, Babaeian Jelodar, Ghazizadeh, & Wilkinson, 2021b; Radman, 

Jelodar, Lovreglio, Ghazizadeh, et al., 2022). Incorporating these SMART criteria into TIA can 

improve the accuracy and timeliness of early-stage delay notifications, thereby reducing the overall 

impact on the project. By integrating TIA with earned value metrics and aligning with SMART 

criteria, project managers can gain a comprehensive understanding of how early-stage delays 

influence the broader project scope, enabling proactive decision-making and more accurate 

forecasting of project outcomes (Institute, 2021; Perera, Wijewickrama, Goonawardana, & Jayalath, 

2021). 

1.3 PROBLEM STATEMENT AND RESEARCH QUESTIONS 

The increasing scale and complexity of construction projects pose significant challenges in 

identifying and mitigating delays, especially in their early stages (Kao, Chen, & Ho, 2023; Parsamehr 

et al., 2023). Delays often result from fragmented data, poor coordination among stakeholders, and 

reliance on traditional, manual tracking methods that lack real-time responsiveness (Ali, Aibinu, & 

Paton-Cole, 2024; Mukuka, Aigbavboa, & Thwala, 2015; Radman et al., 2021a; Radman, Jelodar, 

Lovreglio, Ghazizadeh, et al., 2022). While real-time data integration and IoT technologies offer 



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potential solutions, existing systems remain underdeveloped, with limited capability to centralise 

activity- and object-based data for proactive delay management (Abdelalim et al., 2024; Radman et 

al., 2021a). 

This research identifies a need for enhanced Integrated Management Systems (IMS) to act as 

centralised hubs for tracking and monitoring in dynamic construction sites. These systems must be 

capable of acquiring both activity, and object-based data. While progress has been made, their full 

potential remains unrealised. This doctoral thesis addresses this gap by proposing RealCONs, a real-

time, data-driven framework designed to improve delay management. Through integrated data 

acquisition, early notification systems, and predictive analytics, the study aims to enhance 

communication, minimise delays, and boost overall project performance by fostering real-time 

collaboration and decision-making. To effectively manage construction projects and mitigate delays 

especially notifying project key stakeholders at the early stage , six key research gaps must be 

addressed: 

1) Simple and inexpensive approach: Affordable for projects and easy to learn and quick to use for 

all. 

2) Real time data acquisition and centralisation: Ensuring all project data is captured timely and 

stored in a central, accessible location. 

3) Real-time Data Analytics: Analysing project real time data as it's generated to identify potential 

issues and their analytical impacts on Project’s KPIs. 

4) Early Notification Systems: Alerting stakeholders to potential delays as soon as they are detected. 

5) Stakeholder Coordination: Facilitating communication and collaboration among all project 

participants (head contractors, consultants, subcontractors). 

6) Predictive Delay Analytics: Using real time and historical data to forecast potential future delays. 



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Without addressing these criteria, delays will continue to disrupt project timelines, leading to 

cost overruns as measured by EV metrics, such as Cost Performance Index (CPI) and Schedule 

Performance Index (Ando, Baglio, Castorina, Crispino, & Marletta, 2020). For example, the absence 

of real-time delay notifications can cause SPI to drop significantly, reflecting time inefficiencies, 

while poor coordination across data sources can increase actual costs (AC), leading to a lower CPI 

and overall project inefficiency (Amini et al., 2023; Hasan, Chowdhury, & Akter, 2021). Therefore, 

the integration of real-time data analysis, early delay notification, and centralised project management 

platforms is essential to improve the timeliness and cost-efficiency of major construction projects 

(Radman et al., 2021a).  

Furthermore, to rely on a delay management system with reasonable reliability, scalability 

and timely through using combination of digital technologies, three research questions are therefore 

developed:  

1) What causes delays in major construction projects and how they affect stakeholder 

collaboration? 

2) How digital technologies being used to improve major construction projects key performance 

(e.g. delay, risk)? 

3) How proposed framework to be designed and evaluated (focusing on performance and in-

depth early delay detection and their impacts)? 



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1.4 RESEARCH OBJECTIVES  
Based on the above problem statement this research the following objectives have been 

established. Five objectives (OBJ) belong to the research questions as follows: 

Question 1:   

OBJ_1: Identify and Analysis Key Stockholders  

OBJ_2: Identify & Analysis Key Causes Delay 

Question 2:  

OBJ_3: Identify & Analysis Digital Technologies in construction projects 

Question 3:  

OBJ_4: Develop a Proposed Framework 

OBJ_5: Evaluate the Proposed Framework 

 

 

1.5 RESEARCH SCOPE 

This research tackles the inefficiencies inherent in the delay notification and management 

processes of major construction projects. Current systems are often fragmented and reactive, 

presenting a significant challenge. To overcome these limitations, this study aims to enhance early-



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stage delay notifications by developing a centralised framework for real-time detection and 

management. This framework will leverage technologies including IoT sensors, Building Information 

Modelling (BIM), and API integration. By centralising data acquisition and analysis, the research 

seeks to streamline communication and facilitate proactive decision-making, ultimately improving 

project outcomes.  

The research seeks to replace traditional tools with a unified platform for tracking and 

analysing project data in real time. It will also evaluate the framework's impact on performance 

metrics such as the Schedule Performance Index (SPI) and Cost Performance Index (CPI). The 

ultimate aim is to enable more efficient and predictable project outcomes. By integrating automated 

tracking systems, real-time data acquisition, and centralised data analysis, this doctoral research aims 

to provide an innovative solution to the challenges of delay management in major construction 

projects through four core phases as follows: 

• Phase 1 introduces the research and identifies the core problem by examining the limitations 

inherent in current construction project delay management systems (Ye et al., 2023). The focus is 

on understanding why traditional tools fail to provide real-time insights and early warnings (Fadhel 

et al., 2024; Parsamehr et al., 2023; Radman, Jelodar, Lovreglio, Ghazizadeh, et al., 2022). 

• Phase 2 introduces examining technologies using in construction management (Data Acquisition 

and Analysis Modelling). This phase will investigate methods for collecting and centralising real-

time data. It will explore technologies such as IoT devices, cloud platforms, and APIs to understand 

their potential for improving project tracking and communication (Khan et al., 2023). 

• In phase 3 a framework called RealCONs designed and developed. Phase 3 focus here is on creating 

a centralised, real-time data-driven framework. This framework will integrate various technologies 

to deliver a unified platform (e.g. UML) for delay detection and management (Cing & Mansor, 

2023; Gutta et al., 2024; Saravanan et al., 2022). 



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• Phase 4 focusing on implementing and verifying the framework. It will implement the framework 

in major construction projects as a case study to evaluate its effectiveness (Pieterse et al., 2024). It 

will assess how well it addresses communication challenges and its impact on project performance 

metrics such as SPI and CPI (Radman et al., 2021a). 

Finally, based on the findings, this phase will propose recommendations for enhancing real-

time delay management systems. It will also identify areas for future technological advancements, 

including AI-based predictive delay analysis. 

1.6 RESEARCH METHODOLOGY 

The research methodology is a critical foundation for any academic study, as it shapes how 

data is collected, analysed, and interpreted, ensuring that findings are valid, reliable, and relevant to 

the research questions (Fellows & Liu, 2021; Saunders, Lewis, & Thornhill, 2016). A well-chosen 

methodology allows researchers to systematically investigate complex issues and provides a 

structured pathway for achieving research objectives. In the context of this thesis, the research 

methodology guides the multi-phase investigation, supporting the development of a comprehensive 

understanding of project delay impacts within construction. 

Equally important is the research philosophy, which underpins the methodological approach 

by establishing the assumptions and principles that guide the study’s interpretation of knowledge 

(Mbanaso, Abrahams, & Okafor, 2023; Saunders et al., 2016). Considering research philosophy is 

essential, as it defines the perspective through which data is perceived and shapes the entire research 

process—from data collection to analysis and conclusions (Al-Ababneh, 2020). For this doctoral 

thesis, a thorough understanding of research philosophy ensures alignment between theoretical 

concepts and practical methods, enabling a well-grounded approach to delay analysis. 



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For this purpose, this section provides a detailed outline of the research methodology and 

philosophy, divided into the following parts: 1) Research Philosophy - Overview: An exploration 

of various research philosophies, including positivism, interpretivism, and pragmatism, to establish a 

theoretical foundation for methodological choices; 2) Research Philosophy - This Doctoral Thesis: 

A discussion of the specific research philosophy guiding this thesis, explaining how it aligns with the 

study’s goals and why it is best suited for analysing delay impacts in construction projects; 3) 

Research Conceptual Framework: A comprehensive description of the thesis phases, detailing how 

each objective is addressed through multiple stages of research. This section explains the 

interconnectivity of the phases and how each phase builds on previous findings to achieve the overall 

research objectives; 4) Adopted Method for Research: A breakdown of each phase, outlining the 

methodologies and techniques employed in each stage. This includes qualitative and quantitative 

methods, mixed methods approach, and data triangulation techniques; 5) Ethics and 6) Data 

Collection: A detailed explanation of the data collection process in each phase, addressing ethical 

considerations and describing the outcomes. This section covers the sources of data, participant 

selection, data validation, and any limitations encountered. As shown below, the research approach 

outlines how the research objectives have been targeted and addressed: 

 



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1.6.1 Research philosophy - Overview 

Research philosophy addresses fundamental principles that guide a study’s approach to 

understanding knowledge (epistemology) and the nature of reality (ontology). Ontology considers 

what constitutes reality and whether it is objective and independent or socially constructed and 

subjective. Epistemology concerns what we accept as valid knowledge, determining if knowledge 

should be gathered objectively (as in natural sciences) or through subjective interpretation (as in 

social sciences). These distinctions shape research approaches, methods, and data interpretation (Al-

Ababneh, 2020; Mbanaso et al., 2023). 

Key philosophies include positivism, which assumes an objective reality that can be 

scientifically measured; interpretivism, which sees reality as socially constructed and thus understood 

through qualitative, interpretive means; realism, which acknowledges an independent reality but 

considers that it is imperfectly known; and pragmatism, which uses methods best suited to address 

the research question, regardless of epistemological stance (Fellows & Liu, 2021). The research onion 

model is a widely used framework that guides researchers in creating a structured and comprehensive 

research design. This model proposed by Saunders et al. (2016) to visualises research methodology 

as layered stages, resembling the layers of an onion. Each layer represents a key component in the 

research process, helping researchers make sequential decisions on aspects such as philosophical 

stances, research approaches, research strategies, methodological choices, time horizons, and data 

collection techniques (Figure 1.1). By moving through each layer systematically, researchers can 

build a coherent and well-aligned research design (Fellows & Liu, 2021): 



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Figure 1.1: Research onion model (Fellows & Liu, 2021) 
 

• Layer 1 - Philosophies: The outermost layer represents the research philosophy (positivism, 

interpretivism, realism, pragmatism), which underpins the researcher’s worldview and informs the 

entire approach to data and methods. 

• Layer 2 - Approaches: The second layer denotes research approaches (deductive and inductive). 

A deductive approach tests existing theories, while an inductive approach develops new theories 

based on data. 

• Layer 3 - Strategies: This layer involves choosing a research strategy, such as experiments, 

surveys, case studies, or ethnography, depending on the nature of the inquiry. 

• Layer 4 - Choices: It addresses methodological choices, like mono-method, mixed-method, or 

multi-method approaches, indicating whether the study will utilise one method or combine multiple. 

• Layer 5 - Time Horizons: Researchers select between cross-sectional (single point in time) or 

longitudinal (multiple time points) studies based on the research aim. 

• Layer 6 - Techniques and Procedures: The innermost layer involves the specific techniques for 

data collection and analysis, like interviews, observations, or statistical analysis. 



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The Research Onion model thus offers a clear, layered framework that can be customized 

according to a study’s specific goals, ensuring methodological coherence (Fellows & Liu, 2021; 

Saunders et al., 2016). 

1.6.2 Research philosophy - This doctoral thesis 

The RealCONs framework, developed for real-time, data-driven project analysis with a focus 

on early-stage delay notifications, operates within distinct ontological and epistemological 

foundations that inform its structure and methods. This framework can be aligned effectively with 

the structured methodology of Saunders’ Research Onion, which outlines research design in layers 

from philosophy to techniques. Analysing RealCONs within this framework not only provides clarity 

but also emphasizes the systematic application of ontological and epistemological principles in 

achieving reliable, actionable insights for project management. This approach supports RealCONs’ 

goal of minimising delays through timely, data-informed decision-making. In this section, we discuss 

the ontological and epistemological underpinnings of this doctoral research alongside Saunders’ 

onion research model as follow:  

1.6.2.1 Ontology: The Nature of Reality in RealCONs’ Framework 

Ontology in research explores the nature of reality and seeks to define what constitutes "truth" 

or "existence" in a given field. RealCONs’ framework assumes a realist ontology, operating on the 

premise that delays, risks, and performance metrics within a project have an objective reality that can 

be observed, quantified, and influenced. RealCONs’ structure, with its focus on tracking and 

analysing data in real time, reflects an underlying belief that project conditions, risks, and outcomes 

are measurable and exist independently of the observer. 



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However, while adopting this realist stance, RealCONs’ also acknowledges that the 

interpretation of project data may involve subjective perspectives, particularly in assessing the 

potential impacts of delays. Therefore, the framework incorporates a pragmatic approach, recognising 

the need for both objective data and the subjective judgments of project managers. This pragmatism 

aligns well with Saunders’ Research Onion by situating RealCONs’ within a structured process that 

moves from objective analysis (positivism) to interpretative understanding (interpretivism) as needed 

for comprehensive insights. 

1.6.2.2 Epistemology: What counts as knowledge in RealCONs’ framework 

Epistemology, or the study of knowledge, shapes the RealCONs’ framework by defining what 

is considered valid knowledge and how it can be acquired. In RealCONs’, knowledge about project 

delays is seen as valid when it is based on empirical, real-time data from multiple sources (e.g., 

workforce logs, material shipments, and machinery availability). Moreover, RealCONs employs a 

data-driven, empirical approach that aligns with the positivist philosophy within Saunders’ Research 

Onion, which emphasises observable, measurable, and repeatable evidence. 

By capturing and analysing data continuously, RealCONs’ aims to provide an objective 

foundation for predicting and mitigating project delays. The framework’s reliance on quantifiable 

metrics, statistical analysis, and real-time monitoring reflects a commitment to knowledge that is 

testable and verifiable. In practice, this epistemological approach empowers decision-makers with 

timely information and allows for an evidence-based understanding of project dynamics. However, 

RealCONs’ does not disregard interpretive knowledge. While objective data is foundational, the 

interpretation of delays and risks requires subjective expertise, particularly when projecting future 

scenarios or estimating impact severity. This interpretative element in RealCONs’ epistemology 



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acknowledges that, in complex, fast-paced projects, human insight complements quantitative data for 

holistic analysis. 

1.6.2.3 Aligning RealCONs’ Framework with Saunders’ Research Onion 

Saunders’ Research Onion provides a structured approach to research design that parallels the 

systematic structure of RealCONs’ data-driven framework. Each layer of the Onion can be mapped 

onto RealCONs’ methodological components, guiding the framework from its philosophical 

foundations to its data collection and analysis techniques: 

 Layer 1- Philosophical Stance (Outer Layer): 

Given RealCONs' emphasis on real-time data and predictive analysis for early-stage delay 

notification, a pragmatist philosophy offers an appropriate guiding framework. Pragmatism supports 

the integration of quantitative and qualitative methods by prioritising solutions that are both practical 

and effective in addressing real-world challenges. From a quantitative perspective, pragmatism 

allows for the use of positivist approaches to analyse objective data, such as project schedules, 

performance metrics, and predictive models. These methods are essential for providing measurable 

and reliable insights into delays and their potential impacts. Simultaneously, pragmatism 

accommodates interpretivist elements by recognising the importance of understanding project 

dynamics, stakeholder interactions, and the contextual nuances that influence decision-making. 

Qualitative methods, such as interviews or case studies, can provide valuable insights into how 

stakeholders perceive delays and respond to management strategies. 

By balancing these theoretical perspectives, pragmatism bridges the gap between theory and 

practice. It ensures that the research is not only grounded in robust analytical frameworks but also 

remains applicable and responsive to the practical demands of construction projects. This dual 



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approach aligns with RealCONs' goal of enhancing delay management by combining data-driven 

precision with a nuanced understanding of the complex, dynamic nature of project environments. 

 Layer 2- Approaches (Deductive vs. Inductive): 

RealCONs’ employs a primarily deductive approach, aligning with Saunders’ framework by 

testing existing project real time tracking approaches about project delays based on historical data 

patterns. However, as the research progressed, the approach shifted towards inductive reasoning in order to 

RealCONs’ continually updates its model with new data, adapting to emerging trends and potential 

project-specific risks. 

 Layer 3- Strategies (Real-Time Monitoring): 

In Saunders' Research Onion model, selecting a research strategy involves determining the 

appropriate use of qualitative and quantitative methods. RealCONs’ emphasis on real-time, data-

driven analysis leans primarily towards quantitative methods, utilizing statistical models and 

predictive analytics, with qualitative methods introduced as needed for subjective assessment, such 

as expert judgment on potential impacts. Consequently, the research approach in Layer 2 shifted from 

a deductive to an inductive reasoning framework. 

To generate new insights and develop the RealCONs framework as a proposed solution, the 

research employed “case study” and focus group strategies, using “surveys” in facilitated workshops. 

These strategies were chosen because RealCONs has been tested on specific projects or under defined 

project conditions. This approach allowed for detailed, context-rich analysis, enabling an assessment 

of RealCONs' performance, particularly its accuracy and flexibility. Additionally, the “Action 

Research” strategy was well-suited to the RealCONs framework’s implementation. As the framework 

was applied in live projects, Action Research allowed for continual improvement and adaptation 

while enabling researchers to actively engage in and observe real-time decision-making processes.  

 Layer 4- Choices (Mixed-Method): 



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RealCONs employs a mixed-methods approach, integrating quantitative data analytics with 

qualitative insights from multiple project data sources. The framework gathers data from the 

following resources: 

1. Site_Work: Real-time and actual site data collected via smartphone and QR codes. 

2. Consultant Team: Design and engineering input using BIM tools like Revit and Navisworks. 

3. Oracle_P6: Planning data from the project team using Primavera P6. 

4. Oracle_Aconex: Document management organized by the head contractor. 

5. Power BI: Analytical platform consolidating data from all sources. 

This mixed approach enables RealCONs to capture objective metrics, such as Schedule 

Performance Index (SPI) and Cost Performance Index (CPI), while incorporating stakeholder 

feedback, crucial for understanding and predicting delay impacts from various perspectives. The use 

of both quantitative data and qualitative insights (e.g., stakeholder perceptions) offers a 

comprehensive and context-sensitive view of project delays. This aligns with Saunders’ Research 

Onion model, which supports a blended methodology to achieve a more nuanced analysis of complex 

project delays. 

 Layer 5- Time Horizons (Longitudinal): 

Based on Saunders' research onion model, while a cross-sectional approach looks suitable for this 

doctoral proposed framework, but it would not provide the depth required to assess trends, iterative 

improvements, or long-term impacts, which are central to the research objectives. Therefore, the 

longitudinal time horizon is more suitable for the RealCONs framework and the thesis objectives. As 

a result, the research aims to analyse changes a