摘要
针对寒冷地区模块化钢框架结构节能性与经济性之间的矛盾问题,对模块化钢框架结构能耗和成本两个设计目标进行同步优化研究。根据模块化钢框架结构的特点进行参数化设计研究,提出在不同建筑尺寸下模块化钢框架结构的BIM模型自动建模方法;在Energyplus建筑能耗分析软件计算数据的基础上,采用多种机器学习算法进行建筑能耗预测,建立一种高效精确的建筑能耗预测模型;联立建筑能耗预测模型和建筑成本计算公式,在满足结构承载力的约束条件下,基于NSGA-Ⅱ算法进行模块化钢框架结构能耗和成本的多目标优化设计,生成帕累托最优解集。多目标优化设计方法解决了模块化钢框架结构“能耗+成本”的多目标一体化设计难题,推动了模块化钢框架结构的智能化升级,实现了模块化钢框架结构设计的快速高效化。
This paper aims at solving the contradictive design problem of the modular steel frame structure in cold regions considering both energy-and cost-saving.A synchronous optimization study with energy consumption and cost objectives is hence carried out for target modular steel frame structures.Parametric modeling of modular steel frame structures is studied according to their characteristics.An automatic BIM modeling method is developed for modular steel frame structures.The building energy consumption is modeled using various machine learning algorithms based on the database constructed from the Energyplus software.The proposed XGBoost model provides efficient and accurate predictions for the building energy consumption.The energy consumption model as well as the cost formula serve as the objective functions in NSGA-Ⅱalgorithm to build the design optimization program.During optimization,structural bearing capacity must be satisfied.Pareto solution set is then achieved by the developed program and analyzed.By solving the multi-objective design problem of modular steel frame structures with advanced computing techniques,this study contributes to the intelligent upgrade of the modular steel frame structure industry,and realizes its rapid and efficient design.
作者
苗茹云
黄轶淼
董威
张玉芬
马国伟
MIAO Ruyun;HUANG Yimiao;DONG Wei;ZHANG Yufen;MA Guowei(School of Civil and Transportation Engineering,Hebei University of Technology,Tianjin 300401,P.R.China)
出处
《土木与环境工程学报（中英文）》
CSCD
北大核心
2024年第1期152-162,共11页
Journal of Civil and Environmental Engineering
基金
国家自然科学基金(52078179)。
关键词
钢框架结构
参数化建模
建筑能耗预测
机器学习
多目标优化设计
steel frame structure
parametric modeling
building energy consumption prediction
machine learning
multi-objective optimization design
