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BIM-Integrated Metaheuristic Approach for Multi-Objective Optimization of Large-Scale Construction Projects | ||
| نشریه مهندسی عمران و محیط زیست | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 14 شهریور 1405 | ||
| نوع مقاله: مقاله کامل پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22034/ceej.2026.73283.2505 | ||
| نویسندگان | ||
| فرهاد سعیدی1؛ سینا فرد مرادی نیا* 2؛ ابراهیم سلامی3 | ||
| 1گروه مهندسی عمران- واحد تبریز- دانشگاه آزاد اسلامی- تبریز-ایران | ||
| 2گروه عمران، واحد تبریز، دانشگاه آزاد اسلامی، تبریز، ایران | ||
| 3گروه مهندسی عمران- دانشگاه پیام نور- تهران- ایران | ||
| چکیده | ||
| Building Information Modeling (BIM) has become an essential platform for managing construction information; however, its integration with intelligent optimization techniques for simultaneous project planning and structural design remains limited. This study proposes a comprehensive data-driven framework integrating BIM with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to optimize construction planning, resource allocation, scheduling, and structural design in large-scale building projects. A six-story case-study building located in Sahand New Town, Tabriz, was modeled in Autodesk Revit, and project information including geometry, material quantities, costs, schedules, and resources was extracted directly from the BIM model. The extracted data were linked to multi-objective optimization models through Revit API, Dynamo, and Python-based computational tools. The optimization simultaneously minimized construction cost, project duration, structural weight, material consumption, and embodied carbon emissions while satisfying structural and constructability constraints. Results demonstrated that the proposed framework successfully generated feasible Paretooptimal solutions and selected a balanced structural configuration with a total material cost of IRR 95142270000, structural steel weight of 56.34 tons, embodied carbon emissions of 135,504 kgCO₂e, and a construction duration of 16.5 days. Compared with the baseline design, the optimized solution reduced structural cost by 3.04%, steel consumption by 3.52%, embodied carbon emissions by 2.74%, project duration by 10.81%, and execution risk by 10%, while increasing resource productivity by 12.82% without violating engineering constraints. The findings demonstrate that integrating BIM with metaheuristic optimization algorithms provides an effective intelligent decision-support framework capable of improving economic performance, construction efficiency, structural sustainability, and resource management in complex construction projects | ||
| کلیدواژهها | ||
| Building Information Modeling؛ Genetic Algorithm؛ Particle Swarm Optimization؛ Revit؛ Multi-Objective Optimization | ||
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