Interpretable gradient boosting based ensemble learning and African vultures optimization algorithm optimization for estimating deflection induced by excavation  

作　　者：Zenglong LIANG Shan LIN Miao DONG Xitailang CAO Hongwei GUO Hong ZHENG 

机构地区：[1]Key Laboratory of Urban Security and Disaster Engineering(Ministry of Education),Beijing University of Technology,Beijing 100124,China [2]Department of Civil and Environmental Engineering,The Hong Kong Polytechnic University(PolyU),Hong Kong 999077,China

出　　处：《Frontiers of Structural and Civil Engineering》2024年第11期1698-1712,共15页结构与土木工程前沿（英文版）

基　　金：National Natural Science Foundation of China(Grant Nos.42107214 and 52130905).

摘　　要：Intelligent construction has become an inevitable trend in the development of the construction industry.In the excavation project,using machine learning methods for early warning can improve construction efficiency and quality and reduce the chances of damage in the excavation process.An interpretable gradient boosting based ensemble learning framework enhanced by the African Vultures Optimization Algorithm(AVOA)was proposed and evaluated in estimating the diaphragm wall deflections induced by excavation.We investigated and compared the performance of machine learning models in predicting deflections induced by excavation based on a database generated by finite element simulations.First,we exploratively analyzed these data to discover the relationship between features.We used several state-of-the-art intelligent models based on gradient boosting and several simple models for model selection.The hyperparameters for all models in evaluation are optimized using AVOA,and then the optimized models are assembled into a unified framework for fairness assessment.The comprehensive evaluation results show that the AVOA-CatBoost built in this paper performs well(RMSE=1.84,MAE=1.18,R2=0.9993)and cross-validation(RMSE=2.65±1.54,MAE=1.17±0.23,R2=0.998±0.002).In the end,in order to improve the transparency and usefulness of the model,we constructed an interpretable model from both global and local perspectives.

关 键 词：African vultures optimization algorithm gradient boosting ensemble learning interpretable model wall deflection prediction 

分 类 号：TU528[建筑科学—建筑技术科学]