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Start date: 2020Subject: search.filters.subject.construction

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    A Comparative Study of Standardised Inputs and Inconsistent Outputs in LCA Software
    (MDPI (Basel, Switzerland), 2025-09-04) Gong J; Vishnupriya V; Wilkinson S; Shrestha PP; Shrestha K
    Motivation: Life Cycle Assessment (LCA) is a valuable tool for quantifying environmental impacts in construction. However, inconsistencies between software outputs may compromise effective decision-making. Knowledge Gap: In New Zealand’s construction sector, practitioners have limited guidance in selecting suitable LCA tools due to gaps in software scope, data transparency, and the quality of result interpretation. Aim and Objectives: This study investigates inconsistencies in results produced by eight widely used LCA software tools and identifies the key factors contributing to these variations. Research Method: This study uses a comparative analysis with data from a timber-framed warehouse project in Auckland, New Zealand. Eight software tools (SimaPro V9.0, openLCA V2.0, LCAQuick V3.5, Building Carbon Calculator V1.0, CCaLC2 V3.1, eTool V5.0, One Click LCA, and Athena Impact Estimator for Buildings V5.4) were evaluated across 14 environmental impact categories using standardised inputs. Preliminary Findings: Substantial inconsistencies were observed even with standardised inputs, although SimaPro V9.0 and openLCA V2.0 provided the most consistent results. These findings highlight the importance of software selection for reliable environmental assessments. Research Significance: This study aids industry practitioners in selecting effective LCA tools for sustainable construction practices.
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    Identifying and Prioritizing Critical Risk Factors in the Context of a High-Voltage Power Transmission Line Construction Project: A Case Study from Sri Lanka
    (MDPI, Basel, Switzerland, 2024-12) Weerakkody W; Rathnayaka B; Siriwardana C; Luongo A; Giunta MS
    This study addresses critical risk factors in high-voltage power transmission line (HVPTL) construction projects, which are vital components of national energy infrastructure. HVPTL projects are essential for meeting energy needs but are often plagued by risks due to their linear construction nature, leading to project underperformance. However, the lack of attention to risk management often leads to project underperformance. This research aims to identify and rank these risks to facilitate effective risk management. Through literature review and preliminary surveys, 63 risk elements were identified under 14 main categories. These risks were ranked using two rounds of Delphi surveys and the analytical hierarchy process (AHP). The study focuses on a Sri Lankan HVPTL project. The most critical risk factors identified include “improper planning by the main contractor”, “delays in decision-making by the client/consultant”, “errors in initial costing”, and “inaccuracies in survey data”, with AHP analysis assigning significant weights of 43.9%, 18%, 16%, and 14.9% to these factors, respectively. Comparative analysis with similar studies reveals consistent findings, underscoring the importance of addressing delays in approvals, material unavailability, and construction-quality challenges. These results emphasize the necessity of adopting systematic risk-management techniques in HVPTL projects to mitigate uncertainties and enhance project outcomes.
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    Decision-Making Framework for Construction Clients in Selecting Appropriate Procurement Route
    (MDPI (Basel, Switzerland), 2022-12-12) Bolomope M; Amidu A-R; Ajayi S; Javed A; Settembre-Blundo D; Lopes JP
    Procurement decision-making is a crucial determinant of project success. Although several objective, stage-based models have been proposed to guide clients’ procurement choices, little emphasis has been made on the subjective nature of construction clients. Recognizing the role of clients’ experiences in justifying procurement routes, this study develops a decision-making framework that is capable of guiding construction clients in making informed procurement choices. Adopting a mixed-method approach, comprising semi-structured interviews and multi-objective optimization, relevant procurement options were appraised based on clients’ specifications and project deliverables. The lived experiences of construction clients and the importance they attach to pre-defined selection rating criteria were subsequently evaluated, using a template that enables clients to prioritize procurement methods for different project types. The resultant framework offers a holistic, practical, and collaborative procurement selection process that promotes the efficient delivery of construction projects by reducing the cost overrun and delays associated with uninformed client decisions in construction procurement.
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    Development of a decision support tool for automation adoption and optimisation in precast concrete plants : a New Zealand case study : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Construction Project Management at Massey University, Albany, New Zealand
    (Massey University, 2022) Ansari, Reza
    In response to the growing demand in the New Zealand construction market, this study aims to develop a decision-support framework for adopting and optimising automation in precast concrete plants, which are increasingly recognised for their numerous benefits. The primary resources required by these plants include labour, equipment, and materials, and their efficient use is essential for maintaining competitiveness. Automation has been identified as a potential solution for improving productivity and profitability in precast concrete manufacturing; however, an appropriate decision-support tool is currently lacking. The current study commences with a comprehensive literature review, followed by historical data collection, face-to-face interviews, and site observations of precast concrete plants to address this research gap. These methods help identify attributes that affect profitability, leading to developing and validating of a theoretical framework named the Precast Plant Automation System Tool (PPAST) through a case study. The PPAST framework comprises two sequential phases: the strategic phase, which uses the direct rating method for preliminary feasibility evaluation of automation adoption, and the tactical phase, where the AHP method assesses the appropriate automation sequence for the plant. The study’s main findings indicate that the developed decision support system enables decision-makers to articulate their objectives and attitudes towards risk as they explore the feasibility of automation and formulate an optimal automation strategy. Specifically, the system aids in evaluating the impact of automation on cost and quality and identifying necessary process changes before implementing new technologies. The primary contribution of this research is its novel approach to systematically evaluating alternative automation scenarios in precast concrete production plants. The results demonstrate that the proposed model is a valuable and effective decision-making tool for adopting and optimising automation in precast concrete plants. This research fills a critical knowledge gap concerning the crucial measurements of precast concrete plant profitability and the absence of an automation adoption tool. The developed framework can be extended to investigate automation adoption and optimisation in other precast concrete plants across New Zealand. This study's practical implications include empowering precast plants to meet their organisation's profitability measures, thus satisfying stakeholder value propositions. A thriving precast concrete industry will lead to more satisfied clients, attract additional investment, and improve the overall construction industry's quality, productivity, and profitability at the national level. Theoretically, this research contributes a reliable benchmark for future studies by developing decision support tools that facilitate selecting optimised automation methods for precast concrete plants and contributing to theoretical knowledge by establishing an optimised automation decision support method that guides researchers in exploring other avenues for maximising profitability.