Framework For the Optimization of Construction Project

Construction project optimisation is crucial for effective resource use, timely completion, and cost-effectiveness. This study critically reviews to analyses different optimisation methodologies. The study shed light on various contemporary methodologies, such as artificial intelligence (AI), Lean construction, Six Sigma, and Building Information Modelling (BIM), with more established approaches like the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT). Conventional techniques have functioned as fundamental instruments for organising and overseeing. Modern methods, on the other hand, increase project efficiency: AI provides predictive analytics for resource optimisation, Lean Construction maximises value while minimising waste, Six Sigma lowers process variability, and BIM enhances collaboration and decision-making. These sophisticated techniques, however, frequently need both a large financial and skill commitment. The results show that depending on the size, complexity, and resources available for a project, different optimisation strategies perform differently. While larger projects frequently require advanced tools like BIM and AI, smaller ones could benefit from simpler approaches. For thorough optimisation, an integrated strategy that incorporates several approaches is advised, highlighting the significance of ongoing learning and adaptation as new technologies arise. Achieving the best results in construction requires customising optimisation solutions to the requirements of each project. Projects are finished to the appropriate quality standards, on schedule, and within budget due to this integrated strategy.

Authors

Publication Details

Journal
African Journal of Biomedical Research
Published
2024-12-06
DOI
https://doi.org/10.53555/ajbr.v27i4s.4457
Citations
2
Primary Topic
BIM and Construction Integration
Type
article
Field-Weighted Citation Impact
0.53
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article

Framework For the Optimization of Construction Project

Najid SVP
2 citations
African Journal of Biomedical Research
BIM and Construction Integration
0.53
article

Framework For the Optimization of Construction Project

Najid SVP
article en
2 citations

Abstract

Construction project optimisation is crucial for effective resource use, timely completion, and cost-effectiveness. This study critically reviews to analyses different optimisation methodologies. The study shed light on various contemporary methodologies, such as artificial intelligence (AI), Lean construction, Six Sigma, and Building Information Modelling (BIM), with more established approaches like the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT). Conventional techniques have functioned as fundamental instruments for organising and overseeing. Modern methods, on the other hand, increase project efficiency: AI provides predictive analytics for resource optimisation, Lean Construction maximises value while minimising waste, Six Sigma lowers process variability, and BIM enhances collaboration and decision-making. These sophisticated techniques, however, frequently need both a large financial and skill commitment. The results show that depending on the size, complexity, and resources available for a project, different optimisation strategies perform differently. While larger projects frequently require advanced tools like BIM and AI, smaller ones could benefit from simpler approaches. For thorough optimisation, an integrated strategy that incorporates several approaches is advised, highlighting the significance of ongoing learning and adaptation as new technologies arise. Achieving the best results in construction requires customising optimisation solutions to the requirements of each project. Projects are finished to the appropriate quality standards, on schedule, and within budget due to this integrated strategy.

African Journal of Biomedical Research
Openalex Percentile: Top 33%
BIM and Construction Integration
0.53
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Framework For the Optimization of Construction Project — Najid SVP · African Journal of Biomedical Research (2024) | TGRS Research Map | TGRS