Optimization of deep excavation construction using an improved multi-objective particle swarm algorithm

Managing construction uncertainties, especially those affecting safety and the environment, is critical in optimizing deep excavation projects. This paper introduces an integrated framework that leverages Building Information Modeling (BIM) to aggregate essential data on construction cost and durati...

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Veröffentlicht in:Automation in construction Jg. 166; S. 105613
Hauptverfasser: Meng, Fanli, Xu, Jiayi, Xia, Changqing, Chen, Wei, Zhu, Min, Fu, Chuanqing, Chen, Xiangsheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.10.2024
Schlagworte:
BIM
Deep excavation
Improved MOPSO algorithm
Multi-objective optimization
Improved MOPSO algorithm
Multi-objective optimization
Deep excavation
BIM
ISSN:0926-5805
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Zusammenfassung:Managing construction uncertainties, especially those affecting safety and the environment, is critical in optimizing deep excavation projects. This paper introduces an integrated framework that leverages Building Information Modeling (BIM) to aggregate essential data on construction cost and duration. A multi-objective optimization model is proposed, incorporating the critical path method, system reliability metrics, reward-penalty mechanisms, and environmental impact considerations to balance the objectives of duration, cost, safety, and environment. An improved Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is applied to solve this complex problem. The effectiveness of this algorithm is demonstrated through statistical tests, showing a significant improvement in the solution quality and a reduction in the mean square error of particle density distance by over 85%. A case study from a project in Hangzhou, China, illustrates the practical application of this method, achieving compliance with safety and environmental regulations while reducing the duration by 22 days and saving over €28,350. •A decision-making optimization system is established for deep excavations.•The system is capable of effectively optimizing multiple objectives simultaneously.•The high-precision algorithm source data is obtained by using BIM technology.•The uniformity and diversity of the Pareto solution set have been greatly enhanced.
ISSN:0926-5805
DOI:10.1016/j.autcon.2024.105613