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

Frontiers of Structural and Civil Engineering 2024年第7期18卷 1084-1102页

作者： Taimur RAHMAN Pengfei ZHENG Shamima SULTANA Department of Civil Engineering World University of BangladeshDhaka 1230Bangladesh School of Civil Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computer Science&Engineering University of Asia PacificDhaka 1205Bangladesh

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.

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

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Study on the Effects of Mass and Stiffness Variations on fundamental periods of Structures

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Journal of Civil Engineering and Architecture 2022年第10期16卷 488-496页

作者： Krist Grigorakis Tadeh Zirakian David Boyajian Department of Civil Engineering and Construction Management California State UniversityNorthridgeUSA

fundamental period is an important parameter in seismic design and performance assessment of ***,comprehensive and detailed investigations of effectiveness as well as affectability of this parameter can result in the design of high-performing earthquake-resistant *** this basis,this research intends to evaluate the effects of variations of mass and stiffness on the fundamental periods of two three-and nine-story structures representing low-and high-rise buildings,*** this end,a MATLAB code was developed and validated to determine the fundamental periods of structures with various mass and stiffness *** case studies were performed to investigate the effects of mass and stiffness variations along the stories of the considered structural *** objective of this research endeavor is to provide a better understanding of affectability of fundamental period under different design considerations.

关键词： Earthquake engineering fundamental period structures mass stiffness

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