Prediction of Damping Capacity Demand in Seismic Base Isolators via Machine Learning

作     者：Ayla Ocak Umit Isıkdag Gebrail Bekdas Sinan Melih Nigdeli Sanghun Kim ZongWoo Geem 

作者机构：Department of Civil EngineeringIstanbul University-CerrahpasaIstanbul34320Turkey Department of InformaticsMimar Sinan Fine Arts UniversityIstanbul34427Turkey Department of Civil and Environmental EngineeringTemple UniversityPhiladelphiaPA19122USA Department of Smart CityGachon UniversitySeongnam13120Korea 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2024年第138卷第3期

页      面：2899-2924页

核心收录：

学科分类：070801[理学-固体地球物理学] 07[理学] 0708[理学-地球物理学] 

基　　金：the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(2020R1A2C1A01011131) the Energy Cloud R&D Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT(2019M3F2A1073164) 

主　　题：Vibration control base isolation machine learning damping capacity 

摘      要：Base isolators used in buildings provide both a good acceleration reduction and structural vibration control *** base isolators may lose their damping capacity over time due to environmental or dynamic *** deterioration of them requires the determination of the maintenance and repair needs and is important for the long-termisolator *** this study,an artificial intelligence prediction model has been developed to determine the damage and maintenance-repair requirements of isolators as a result of environmental effects and dynamic factors over *** the developed model,the required damping capacity of the isolator structure was estimated and compared with the previously placed isolator capacity,and the decrease in the damping property was tried to be *** this purpose,a data set was created by collecting the behavior of structures with single degrees of freedom(SDOF),different stiffness,damping ratio and natural period isolated from the foundation under far fault *** data is divided into 5 different damping classes varying between 10%and 50%.Machine learning model was trained in damping classes with the data on the structure’s response to random seismic *** a result of the isolator behavior under randomly selected earthquakes,the recorded motion and structural acceleration of the structure against any seismic vibration were examined,and the decrease in the damping capacity was estimated on a class *** performance loss of the isolators,which are separated according to their damping properties,has been tried to be determined,and the reductions in the amounts to be taken into account have been determined by *** the developed prediction model,using various supervised machine learning classification algorithms,the classification algorithm providing the highest precision for the model has been *** the results are examined,it has been determined that the damping of the isolator structure with the