Multi-objective optimization design approach for prefabricated buildings to minimize cost, duration and carbon emissions using ant colony algorithm

基于蚁群算法的预制建筑多目标优化设计方法,旨在最大限度地降低成本、缩短工期并减少碳排放

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Abstract

A multi-objective optimization design approach for prefabricated components such as columns, beams, slabs, walls and stairs in prefabricated buildings using ant colony algorithm is proposed to minimize cost, duration and carbon emissions in this paper. The proposed approach takes cost, duration, and carbon emissions as objective functions, the construction technologies of cast-in-place and prefabricated components as variables, prefabrication rate as constraints, and the ant colony algorithm as a solution tool, to minimize the cost, duration, and carbon emissions of prefabricated buildings. The validity of the proposed approach was verified by applying it to the multi-objective optimization design of a three-story frame structure. The results showed that:(1) Compared to all cast-in-place buildings, the prefabricated building under the baseline scenario achieves a reduction of 0.42% in cost, 19.05% in duration, and 13.49% in carbon emissions. (2) The main factor influencing the optimal combination of prefabricated building components is the incremental benefit of cost, duration, and carbon emissions resulting from changes in sub-target weight and prefabrication rate. The weight coefficients of each sub-objective determine "how" the construction technologies of the components is selected, while the prefabrication rate determines "how much" of the construction technologies is chosen. (3) Under four scenarios with different weighting coefficients, the optimized solution for prefabricated buildings compared to cast-in-place construction shows maximum reductions of 1.26% in cost, 27.89% in duration, and 18.4% in carbon emissions. The proposed approach provides an effective pathway for the transformation from cast-in-place construction to prefabricated construction and promotes sustainable development in the building industry.

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