Bayesian Optimized LightGBM model for predicting the fundamental vibrational period of masonry infilled RC frames

作     者：Taimur RAHMAN Pengfei ZHENG Shamima SULTANA Taimur RAHMAN;Pengfei ZHENG;Shamima SULTANA

作者机构：Department of Civil EngineeringWorld University of BangladeshDhaka 1230Bangladesh School of Civil EngineeringZhengzhou UniversityZhengzhou 450001China Department of Computer Science&EngineeringUniversity of Asia PacificDhaka 1205Bangladesh 

出 版 物：《Frontiers of Structural and Civil Engineering》 (结构与土木工程前沿（英文版）)

年 卷 期：2024年第18卷第7期

页      面：1084-1102页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 0814[工学-土木工程] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

主　　题：masonry-infilled RC frame fundamental period LightGBM FP4026 research dataset machine learning data-driven approach Bayesian Optimization 

摘      要：The precise prediction of the fundamental vibrational period for reinforced concrete(RC)buildings with infilled walls is essential for structural design,especially earthquake-resistant *** learning models from previous studies,while boasting commendable accuracy in predicting the fundamental period,exhibit vulnerabilities due to lengthy training times and inherent dependence on pre-trained models,especially when engaging with continually evolving data *** predicament emphasizes the necessity for a model that adeptly balances predictive accuracy with robust adaptability and swift data *** latter should include consistent re-training ability as demanded by realtime,continuously updated data *** research implements an optimized Light Gradient Boosting Machine(LightGBM)model,highlighting its augmented predictive capabilities,realized through the astute use of Bayesian Optimization for hyperparameter tuning on the FP4026 research data set,and illuminating its adaptability and efficiency in predictive *** results show that the R^(2) score of LightGBM model is 0.9995 and RMSE is 0.0178,while training speed is 23.2 times faster than that offered by XGBoost and 45.5 times faster than for Gradient ***,this study introduces a practical application through a streamlit-powered,web-based dashboard,enabling engineers to effortlessly utilize and augment the model,contributing data and ensuring precise fundamental period predictions,effectively bridging scholarly research and practical applications.