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

IF 5.7 2区 工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Mohammad Yasir Mohammad Hasan Shaikh, Sourav Gur
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引用次数: 0

摘要

研究了基于机器学习(ML)的超弹性形状记忆合金(SMA)集成摩擦摆(SMA-FP)隔振器在爆炸致地震动(BIGM)作用下的优化设计和响应可控性,并与FP隔振器进行了比较。采用非线性时程分析(NLTHA)对采用FP或SMA-FP体系隔震的弹性钢剪力建筑进行了分析。通过对顶楼峰值加速度(TFPA)和隔离器峰值位移(PID)两个相互冲突的目标进行优化,得到了基础隔离器(BIs)的设计。采用多目标优化算法估计摩擦系数和SMA钢丝强度的最优组合。对pareto最优前沿的比较清楚地揭示了SMA-FP BI比FP BI在两个目标函数之间更好的权衡。通过对隔离器、建筑物和BIGM的各种参数进行广泛的参数研究,研究了优化设计的鲁棒性和控制有效性。研究结果表明,SMA-FP结合PID和剩余隔离器位移(RID)可以显著降低TFPA(高达30%),比FP BI分别降低42%和60%。最后,采用不同的基于机器学习的回归方法(多元线性回归、脊回归、套索回归、弹性网回归),提出了结构和隔振器优化设计和优化响应的预测模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Advances in Engineering Software
Advances in Engineering Software 工程技术-计算机:跨学科应用
CiteScore
7.70
自引率
4.20%
发文量
169
审稿时长
37 days
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.
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