Integrating BIM and machine learning to predict carbon emissions under foundation materialization stage:Case study of China’s 35 public buildings  

作　　者：Haining Wang Yue Wang Liang Zhao Wei Wang Zhixing Luo Zixiao Wang Jinghui Luo Yihan Lv 

机构地区：[1]School of Architecture,Southeast University,Nanjing 210096,China [2]Key Laboratory of Urban and Architectural Heritage Conservation(Southeast University),Ministry of Education,Nanjing 210096,China [3]School of Architecture,Xi’an University of Architecture and Technology,Xi’an 710055,China [4]Hubei Haizheng Technology Co.,Ltd,Wuhan 430000,China [5]School of Energy and Environment Engineering,Hebei University of Engineering,Handan 056038,China [6]State Key Laboratory of Green Building,Xi’an University of Architecture and Technology,Xi’an 710055,China

出　　处：《Frontiers of Architectural Research》2024年第4期876-894,共19页建筑学研究前沿（英文版）

基　　金：supported by the National Key Research and Development Program of China(Grant No.2022YFE0208600);the Key Research and Development Plan of Shaanxi Province of China(Grant No.2023-ZDLSF-66);the National Natural Science Foundation of China(Grant No.51908111);the SRTP project of Southeast University(Grant No.202310286006Z).

摘　　要：For the significant energy consumption and environmental impact,it is crucial to identify the carbon emission characteristics of building foundations construction during the design phase.This study would like to establish a process-based carbon evaluating model,by adopting Building Information Modeling(BIM),and calculated the materialization-stage carbon emissions of building foundations without basement space in China,and identifying factors influencing the emissions through correlation analysis.These five factors include the building function type,building structure type,foundation area,foundation treatment method,and foundation depth.Additionally,this study develops several machine learning-based predictive models,including Decision Tree,Random Forest,XGBoost,and Neural Network.Among these models,XGBoost demonstrates a relatively higher degree of accuracy and minimal errors,can achieve the RMSE of 206.62 and R2 of 0.88 based on testing group feedback.The study reveals a substantial variability carbon emissions per building’s floor area of foundations,ranging from 100 to 2000 kgCO_(2)e/m^(2),demonstrating the potential for optimizing carbon emissions during the design phase of buildings.Besides,materials contribute significantly to total carbon emissions,accounting for 78%e97%,suggesting a significant opportunity for using BIM technology in the design phase to optimize carbon reduction efforts.

关 键 词：Building foundations Carbon emissions Building information modeling Machine learning Sustainable architectural design 

分 类 号：X51[环境科学与工程—环境工程] TU-02[建筑科学—建筑理论]