Massey Research Online - Browsing by Author "Sutrisna M"

Browsing by Author "Sutrisna M"

Now showing 1 - 9 of 9

A review of climate change impact assessment and methodologies for urban sewer networks
(Elsevier B V, 2025-06) Karimi AM; Jelodar MB; Susnjak T; Sutrisna M
Understanding how climate change affects urban sewer networks is essential for the sustainable management of these infrastructures. This research uses a systematic literature review (PRISMA) to critically review methodologies to assess the effects of climate change on these systems. A scientometric analysis traced the evolution of research patterns, while content analysis identified three primary research clusters: Climate Modelling, Flow Modelling, and Risk and Vulnerability Assessment. These clusters, although rooted in distinct disciplines, form an interconnected framework, where outputs of climate models inform flow models, and overflow data from flow models contribute to risk assessments, which are gaining increasing attention in recent studies. To enhance risk assessments, methods like Gumbel Copula, Monte Carlo simulations, and fuzzy logic help quantify uncertainties. By integrating these uncertainties with a Bayesian Network, which can incorporate expert opinion, failure probabilities are modelled based on variable interactions, improving prediction. The study also emphasises the importance of factors, such as urbanisation, asset deterioration, and adaptation programs in order to improve predictive accuracy. Additionally, the findings reveal the need to consider cascading effects from landslides and climate hazards in future risk assessments. This research provides a reference for methodology selection, promoting innovative and sustainable urban sewer management.
Comparing two AI methods for predicting the future trend of New Zealand building projects: Decision Tree and Artificial Neural Network
(IOP Publishing Ltd, 2022-01-01) Zavvari A; Jelodar MB; Sutrisna M
The rise of Artificial Intelligence and Machine Learning in many aspects of construction management has helped this industry to further improve the management, design, and planning of construction projects. This trend happens in many construction sectors, including in New Zealand. Whilst relatively smaller compared to construction sectors in other OECD countries, the construction sector in New Zealand carries a similar degree of complexity and with its own unique characteristics. Various studies showed that AI and ML can be used for analysis of construction data to generate further insights and to predict future trends in construction sectors. However, the AI approaches have their own set of challenges such as complexity, high cost of training, failure, and change. Aiming to better understand the trends and requirements of New Zealand building projects, this study started with a review of the existing AI methods that are currently being applied. Accordingly, compare and evaluate the accuracy of two AI prediction methods. The two methods of Decision Tree and Artificial Neural Network are selected based on their predictive power and accuracy. These methods are conducted by using available historical building data which is available on StatsNZ website. A portion of the data is used for testing and evaluation purposes, and the rest of the data is used for training the AI methods. It was identified that the Decision Tree method did not show suitable accuracy for prediction building consents issued data. In comparison, Artificial Neural Network shows a reasonable range with 95% of confidence level. Therefore, this method is applied for building consents issued in New Zealand.
Construction industry classification systems: Defining the construction sector in New Zealand
(IOP Publishing Ltd, 2022-01-01) Hoai Le AT; Domingo N; Sutrisna M
CanConstrucNZ is a partnership programme between New Zealand universities, government agencies, and professional bodies to develop a smart system that enables mapping future pipeline projects with the industry capacity and capability to advise the stakeholders whether the sector will be capable of delivering the proposed construction projects. Defining the scope of the construction sector itself is the first step of the programme that helps measure the construction sector capacity. This paper compares different definitions and approaches of the construction sector boundaries and discusses similarities and differences in the selected classification systems, usually used to define, collect, and generate data for measuring the construction sector. The findings highlight the need for a more comprehensive classification to help generate the correct level of data for measuring the construction sector's true scope and size, resulting in better policy initiatives, and informing changes in the industry. The findings of this study recommend future research to develop a customised classification system to represent the value of the New Zealand construction sector in a holistic manner.
End-User Stakeholder Engagement in Refurbishment Design in Higher Education
(MDPI (Basel, Switzerland), 2022-10-01) Seki Y; Olanipekun AO; Sutrisna M
The refurbishment of building facilities needs to incorporate end-user engagement to ensure refurbished building facilities outcomes that include user-responsive learning spaces and satisfy users’ learning needs. However, existing refurbishment design process frameworks neglect to show the engagement process. A new framework for engaging end users in the refurbishment design of building facilities in higher education is presented. A qualitative research methodology was employed to obtain and analyse interview data from twenty-one design team stakeholders involved in two cases of refurbished building facilities in higher education institutions in Australia and New Zealand. The findings revealed four core themes which indicate the context and phases in the refurbishment design process where end-user engagement should be taken seriously. They are the higher education context, early design, user engagement in the design process and post-design phases. In addition, the findings revealed six specific strategies for end-user engagement in the refurbishment design of building facilities in higher education institutions. They are identifying stakeholder value systems, capturing end-user needs, communicating and integrating. Others are the setting of engagement boundaries and surveying of end users. This study modified the project heartbeat originally developed by Stanford University in 2010 for the refurbishment design process in a higher education context. The new framework bridges the gaps in the current literature between stakeholder theory and refurbishment design, and, by incorporating the refurbishment design processes, the framework can be employed in wider education and other project contexts to facilitate the balanced involvement of end users.
Exploring Off-site Construction and Building Information Modelling Integration Challenges; Enhancing Capabilities within New Zealand Construction Sector
(IOP Publishing Ltd, 2022-01-01) Ghalenoei NK; Jelodar MB; Paes D; Sutrisna M
Over the last few years off-site construction (OSC); which is essentially manufacturing different components in a controlled environment, has become popular in the construction industry. This method has the advantages of simplicity, speed, reducing project duration, and minimising construction waste. Therefore, a growing body of literature recognises the importance of OSC to gain better project performance. While OSC has received considerable critical attention, to enhance OSC applications, integrating advanced technologies such as building information modelling (BIM) is essential. There is a lack of research addressing the integration of BIM and OSC, particularly in New Zealand, and few studies investigated the current subject. Therefore, this study focuses on finding the existing OSC and BIM integration challenges within the New Zealand construction sector. The objective of this study has been investigated through literature review and interviews with experts. The common challenges of OSC and BIM integration were identified and classified. Human resources, documentation, managerial, and organisational are the main challenges. This paper is dedicated to exploring OSC and BIM integration in New Zealand, an essential step for the OSC application strategies within the construction sector. This study findings will lend to the construction sector expanding capabilities to improve the status quo and optimise OSC applications through advanced technologies.
Facilitating Digital Transformation in Construction—A Systematic Review of the Current State of the Art
(Frontiers Media S.A., 2021-07-09) Olanipekun AO; Sutrisna M
There is increasing implementation of digital technologies in construction. However, the transformation effects encompassing digital technology implementation are yet to be fully comprehended within the context of construction. Therefore, this study was aimed to provide a holistic understanding of digital transformation in construction. The study drew on extant literature by studying 36 journal publications published between 2016 when digital transformation emerged in construction from the information systems field and 2020. This led to the development of an inductive framework using a grounded theory methodology (GTM) to highlight digital transformation in construction as a process where the implementation of digital technologies creates transformation effects that trigger strategic considerations for putting in place the enablers that facilitate transformation effects and for suppressing the barriers to it. Building on the framework, this study described and presented the strategic considerations for facilitating specific enablers and those for suppressing specific barriers as digital transformation guideline in construction. This study demonstrated how the implementation of digital technologies has increased the understanding of and provided the basis for digital transformation in construction.
In support of sustainable densification in urban planning: a proposed framework for utilising CCTV for propagation of human energy from movement within urban spaces
(Taylor and Francis Group, 2019-12-18) Jonescu E; Mercea T; Do K; Sutrisna M
Co-generation of energy derived from human movement is not new. Intentionally accumulating energy, from mass urban-mobility, provides opportunities to re-purpose power. However, when mass-mobility is predictable, yet not harnessed, this highlights critical gaps in application of interdisciplinary knowledge. This research highlights a novel application of geostatistical modelling for the built environment with the purpose of understanding where energy harvesting infrastructure should be located. The work presented argues that advanced Geostatistical methods can be implemented as an appropriate method to predict probability distribution, density, clustering of populations and mass-population mobility patterns from large-scale online distributed and heterogeneous data sets published by the Australian Urban Research Infrastructure Network. Where clear urban spatio-behavioural relationships of density and movement can be predicted–understanding such patterns supports cross-disciplinary city planning and decision-making. A data-informed–predictive spatial decision-making framework is proposed–facilitating the endeavour of cogenerating kinetic human energy within a prescribed space. This novel proposition could further sustainability strategies for compact living for cities such as in Perth, Western Australia which is increasingly economically and geographically pressured to densify. This research argues that surveillance data elucidate a capacity to interpret and understand impacts of densification strategies, efficacy of CCTV networks in existing and emerging cities.
Statistical cost modelling for preliminary stage cost estimation of infrastructure projects
(IOP Publishing, 2022-12-15) Atapattu C; Domingo N; Sutrisna M
Statistical cost modelling for preliminary stage cost estimation of infrastructure projects
(IOP Publishing Ltd, 2022-01-01) Atapattu CN; Domingo ND; Sutrisna M
Reliable and accurate cost estimates are essential to construction projects. They are even more critical in infrastructure projects as they require more time, cost, and public constraints. Therefore, a better cost model is required for infrastructure projects. An extensive literature review was carried out to identify various statistical modelling techniques and models, as well as models developed using these techniques. The literature identified seven statistical modelling techniques. They are; regression analysis, Monte-Carlo simulation, support vector machine, case-based reasoning, reference class forecasting, artificial neural networks, and fuzzy logic. These techniques were all used in various cost models developed for construction projects. According to the analysis of results, neural networks and support vector machine-based models displayed better performance in their cost estimation models. However, it was found that combining several techniques into a hybrid model, for example, the neuro-fuzzy hybrid, can significantly increase these results. Thus, the reliability and accuracy of the current estimation process can be improved with these techniques. Finally, the techniques identified as having better performance can be used to develop a cost estimation model for the preliminary stage. This is because these techniques perform well even though the availability of information is lower. The results of this research are limited to the seven identified techniques and the literature used in the review.

Browsing by Author "Sutrisna M"

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