Building a Genetic Algorithm-Based and BIM-Based 5D Time and Cost Optimization Model

Alzara, Majed; Attia, Yehia Abdelhamid; Mahfouz, Sameh Youssef; Yosri, Ahmed M.; Ehab, Ahmed

doi:10.1109/access.2023.3317137

Cited by 4 publications

(1 citation statement)

References 49 publications

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“…Five-Dimensional Building Information Modeling (5D BIM) represents a significant evolution in construction project management, adding time (4D) and cost (5D) dimensions to traditional 3D BIM [33,34]. It enables real-time cost estimation and effective budget management throughout a project's lifecycle by incorporating financial data into the digital project model [35,36]. This approach provides dynamic visualizations of financial impacts for each design and construction decision, transforming cost perception and management.…”

Section: D-bimmentioning

confidence: 99%

Navigating the Adoption of 5D Building Information Modeling: Insights from Norway

Hosamo,

Rolfsen,

Zeka

et al. 2024

Infrastructures

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Exploring the integration of 5D Building Information Modeling (BIM) within the Norwegian construction sector, this study examines its transformative impact on cost estimation and project management, highlighting technological and skill-based adoption challenges. Through methodical case studies and interviews with industry experts, it is revealed that 5D BIM significantly enhances the precision of cost estimations and effectively reduces financial overruns in complex construction projects, indicating an industry shift towards its broader acceptance. The research sets out to explore current challenges and opportunities in 5D BIM, assess the usability and integration of software tools, and understand systemic barriers and skill gaps hindering further progress. These objectives lead to a detailed understanding of 5D BIM’s role in improving economic and procedural efficiencies in construction. Suggesting its pivotal role in the evolving construction management realm, the study contributes important insights into 5D BIM’s transformative potential and underscores its importance in advancing the construction industry’s digital transformation.

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Section: D-bimmentioning

confidence: 99%

Navigating the Adoption of 5D Building Information Modeling: Insights from Norway

Hosamo,

Rolfsen,

Zeka

et al. 2024

Infrastructures

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A data-driven approach for predicting cash flow performance of public owners in building projects: insights from Turkish cases

Kazar

2024

ECAM

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PurposeThe cash flow from government agencies to contractors, called progress payment, is a critical step in public projects. The delays in progress payments significantly affect the project performance of contractors and lead to conflicts between two parties in the Turkish construction industry. Although some previous studies focused on the issues in internal cash flows (e.g. inflows and outflows) of construction companies, the context of cash flows from public agencies to contractors in public projects is still unclear. Therefore, the primary objective of this study is to develop and test diverse machine learning-based predictive models on the progress payment performance of Turkish public agencies and improve the predictive performance of these models with two different optimization algorithms (e.g. first-order and second-order). In addition, this study explored the attributes that make the most significant contribution to predicting the payment performance of Turkish public agencies.Design/methodology/approachIn total, project information of 2,319 building projects tendered by the Turkish public agencies was collected. Six different machine learning algorithms were developed and two different optimization methods were applied to achieve the best machine learning (ML) model for Turkish public agencies' cash flow performance in this study. The current research tested the effectiveness of each optimization algorithm for each ML model developed. In addition, the effect size achieved in the ML models was evaluated and ranked for each attribute, so that it is possible to observe which attributes make significant contributions to predicting the cash flow performance of Turkish public agencies.FindingsThe results show that the attributes “inflation rate” (F5; 11.2%), “consumer price index” (F6; 10.55%) and “total project duration” (T1; 10.9%) are the most significant factors affecting the progress payment performance of government agencies. While decision tree (DT) shows the best performance among ML models before optimization process, the prediction performance of models support vector machine (SVM) and genetic algorithm (GA) has been significantly improved by Broyden–Fletcher–Goldfarb–Shanno (BFGS)-based Quasi-Newton optimization algorithm by 14.3% and 18.65%, respectively, based on accuracy, AUROC (Area Under the Receiver Operating Characteristics) and F1 values.Practical implicationsThe most effective ML model can be used and integrated into proactive systems in real Turkish public construction projects, which provides management of cash flow issues from public agencies to contractors and reduces conflicts between two parties.Originality/valueThe development and comparison of various predictive ML models on the progress payment performance of Turkish public owners in construction projects will be the first empirical attempt in the body of knowledge. This study has been carried out by using a high number of project information with diverse 27 attributes, which distinguishes this study in the body of knowledge. For the optimization process, a new hyper parameter tuning strategy, the Bayesian technique, was adopted for two different optimization methods. Thus, it is available to find the best predictive model to be integrated into real proactive systems in forecasting the cash flow performance of Turkish public agencies in public works projects. This study will also make novel contributions to the body of knowledge in understanding the key parameters that have a negative impact on the payment progress of public agencies.

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Minimizing Cost Overrun in Rail Projects Through 5d-Bim: The Case Study of Victoria

Hussain,

Moehler,

Walsh

et al. 2024

Preprint

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Building a Genetic Algorithm-Based and BIM-Based 5D Time and Cost Optimization Model

Cited by 4 publications

References 49 publications

Navigating the Adoption of 5D Building Information Modeling: Insights from Norway

Navigating the Adoption of 5D Building Information Modeling: Insights from Norway

A data-driven approach for predicting cash flow performance of public owners in building projects: insights from Turkish cases

Minimizing Cost Overrun in Rail Projects Through 5d-Bim: The Case Study of Victoria

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