Development of an integrated intelligent BIM-based model for multi-objective optimization in engineering assembly processes
Yanfen Zhang
TL;DR
This paper introduces a smart BIM-based model that optimizes construction assembly by balancing time, cost, and resource conflicts using advanced algorithms.
Contribution
The novel integration of BIM semantic modeling, MOPSO with dynamic weighting, and DQN-based reinforcement learning for multi-objective construction optimization.
Findings
The model achieves an average construction period of 85.2 days and budget cost of USD 1.486 million.
It outperforms existing methods in hypervolume (0.683), solution spread (0.227), and IGD (0.017).
The model shows robustness in handling complex trade-offs with fewer than 1.7 resource conflicts.
Abstract
To enhance construction efficiency and economic performance in prefabricated building projects under limited resource conditions, this study proposes an integrated intelligent optimization model based on Building Information Modeling (BIM) semantic representation. The model is designed to generate optimal assembly plans under multi-objective trade-offs, achieving a balanced compromise between shortened construction periods, reduced costs, and minimized resource conflicts. The study begins by constructing an assembly semantic model using the publicly available BuildingNet dataset, extracting key components’ geometric structures and spatial topology to establish a data foundation suitable for multi-objective scheduling modeling. A multi-objective particle swarm optimization (MOPSO) algorithm enhanced with a dynamic objective weighting mechanism is then introduced. By allowing flexible…
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Taxonomy
TopicsBIM and Construction Integration · Resource-Constrained Project Scheduling · Construction Project Management and Performance