{"title":"基于机器学习的超弹性摩擦摆控制地下建筑爆震优化设计","authors":"Mohammad Yasir Mohammad Hasan Shaikh, Sourav Gur","doi":"10.1016/j.advengsoft.2025.103978","DOIUrl":null,"url":null,"abstract":"<div><div>This study shows the machine learning (ML) based optimal design and response controllability of superelastic shape memory alloy (SMA) integrated friction pendulum (SMA-FP) isolator under the blast induced ground motion (BIGM), and compared with the FP isolator. An elastic steel shear building isolated with the FP or SMA-FP system is analysed through nonlinear time-history analysis (NLTHA). Design of base isolators (BIs) are obtained through optimizing two conflicting objectives, i.e. top floor peak acceleration (TFPA) and peak isolator displacement (PID). A multi-objective optimization (MOO) algorithm is used to estimate the optimal combination of friction coefficient and SMA wire strength. Comparison of the pareto optimal front clearly reveals a better trade-off between two objective functions for SMA-FP BI than FP BI. Robustness of optimal design and control effectiveness is studied through extensive parametric studies, for various parameters of isolator, building, and BIGM. Study results reveal that, SMA-FP can substantially reduce the TFPA (up to 30 %) in conjunction with the PID and residual isolator displacement (RID), reduction up to 42 % and 60 %, respectively, than FP BI. Finally, employing different ML based regression methods (multilinear, ridge, lasso, elastic-net regression), predictive models have been proposed for optimal design and optimum responses of structure and isolator.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"208 ","pages":"Article 103978"},"PeriodicalIF":5.7000,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning based optimal design of superelastic friction pendulum for controlling underground blast-induced vibration of building\",\"authors\":\"Mohammad Yasir Mohammad Hasan Shaikh, Sourav Gur\",\"doi\":\"10.1016/j.advengsoft.2025.103978\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study shows the machine learning (ML) based optimal design and response controllability of superelastic shape memory alloy (SMA) integrated friction pendulum (SMA-FP) isolator under the blast induced ground motion (BIGM), and compared with the FP isolator. An elastic steel shear building isolated with the FP or SMA-FP system is analysed through nonlinear time-history analysis (NLTHA). Design of base isolators (BIs) are obtained through optimizing two conflicting objectives, i.e. top floor peak acceleration (TFPA) and peak isolator displacement (PID). A multi-objective optimization (MOO) algorithm is used to estimate the optimal combination of friction coefficient and SMA wire strength. Comparison of the pareto optimal front clearly reveals a better trade-off between two objective functions for SMA-FP BI than FP BI. Robustness of optimal design and control effectiveness is studied through extensive parametric studies, for various parameters of isolator, building, and BIGM. Study results reveal that, SMA-FP can substantially reduce the TFPA (up to 30 %) in conjunction with the PID and residual isolator displacement (RID), reduction up to 42 % and 60 %, respectively, than FP BI. Finally, employing different ML based regression methods (multilinear, ridge, lasso, elastic-net regression), predictive models have been proposed for optimal design and optimum responses of structure and isolator.</div></div>\",\"PeriodicalId\":50866,\"journal\":{\"name\":\"Advances in Engineering Software\",\"volume\":\"208 \",\"pages\":\"Article 103978\"},\"PeriodicalIF\":5.7000,\"publicationDate\":\"2025-06-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Engineering Software\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0965997825001164\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Engineering Software","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0965997825001164","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Machine learning based optimal design of superelastic friction pendulum for controlling underground blast-induced vibration of building
This study shows the machine learning (ML) based optimal design and response controllability of superelastic shape memory alloy (SMA) integrated friction pendulum (SMA-FP) isolator under the blast induced ground motion (BIGM), and compared with the FP isolator. An elastic steel shear building isolated with the FP or SMA-FP system is analysed through nonlinear time-history analysis (NLTHA). Design of base isolators (BIs) are obtained through optimizing two conflicting objectives, i.e. top floor peak acceleration (TFPA) and peak isolator displacement (PID). A multi-objective optimization (MOO) algorithm is used to estimate the optimal combination of friction coefficient and SMA wire strength. Comparison of the pareto optimal front clearly reveals a better trade-off between two objective functions for SMA-FP BI than FP BI. Robustness of optimal design and control effectiveness is studied through extensive parametric studies, for various parameters of isolator, building, and BIGM. Study results reveal that, SMA-FP can substantially reduce the TFPA (up to 30 %) in conjunction with the PID and residual isolator displacement (RID), reduction up to 42 % and 60 %, respectively, than FP BI. Finally, employing different ML based regression methods (multilinear, ridge, lasso, elastic-net regression), predictive models have been proposed for optimal design and optimum responses of structure and isolator.
期刊介绍:
The objective of this journal is to communicate recent and projected advances in computer-based engineering techniques. The fields covered include mechanical, aerospace, civil and environmental engineering, with an emphasis on research and development leading to practical problem-solving.
The scope of the journal includes:
• Innovative computational strategies and numerical algorithms for large-scale engineering problems
• Analysis and simulation techniques and systems
• Model and mesh generation
• Control of the accuracy, stability and efficiency of computational process
• Exploitation of new computing environments (eg distributed hetergeneous and collaborative computing)
• Advanced visualization techniques, virtual environments and prototyping
• Applications of AI, knowledge-based systems, computational intelligence, including fuzzy logic, neural networks and evolutionary computations
• Application of object-oriented technology to engineering problems
• Intelligent human computer interfaces
• Design automation, multidisciplinary design and optimization
• CAD, CAE and integrated process and product development systems
• Quality and reliability.