[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126090-en":3,"doc-seo-126090-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126090,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Machine Learning-Aided Nonlinear Dynamic Analysis of Engineering Structures - Paper","Machine learning assists the dynamic analysis of real-life engineering structures with mixed geometric and material nonlinearities. A 3D geometric elastoplastic analysis generates realistic large-deformation response descriptions, while uncertainties in system properties are incorporated through multiple input scenarios. A cluster-based extended support vector regression (X-SVR) surrogate model is trained and rebuilt at each Newmark time step to rapidly predict deflection, force, and stress. Accuracy and efficiency are validated via numerical engineering applications under linear and nonlinear properties.","Machine Learning-Aided Nonlinear Dynamic Analysis of Engineering Structures  \nY. Feng, Q. Wang, D. Wu, and W. Gao  \nAbstract A machine learning (ML) technique was used to assist in the dynamic analysis ofmixed geometric and material nonlinearities ofreal-life engineering structures. Various types ofinputs of system properties were considered in the3D dynamic geometric elastoplastic analysis, giving a series of realistic nonlinear descriptions of complex, large deformation structural behaviors. To resolve the numerical challenges of solving the mixed nonlinear problems, a newly established ML technique using a new cluster-based extended support vector regression (X-SVR) algorithm was applied. With this technique, a surrogate model can be built at each time step in the Newmark time integration process, which can then be used to predict the deflection, force and stress ofthe relevant structural performance at different loading time stages. To demonstrate the accuracy and efficiency of the proposed framework, practical engineering applications with linear and nonlinear properties are fully demonstrated, and the nonlinear behavior of the structure under predicted working conditions in the future was predicted and verified in numerical studies.  \nKeywords Engineering structures · Machine learning · Nonlinear dynamic analysis  \nY. Feng (B) · Q. Wang · W. Gao  \nCentre for Infrastructure Engineering and Safety, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW, Australia  \ne-mail: [yuan.feng1@unsw.edu.au](yuan.feng1@unsw.edu.au)  \nD. Wu  \nSchool of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW, Australia  \n© The Author(s) 2023  \nW. Duan et al. (eds.), Nanotechnology in Construction for Circular Economy, Lecture Notes in Civil Engineering 356, [https://doi.org/10.1007/978-981-99-3330-3_36](https://doi.org/10.1007/978-981-99-3330-3_36)  \n347  \n1 Introduction  \nThe nonlinear response of a practical structure is affected by various factors such as system properties, operational coefficients, loads and environment, as well as uncertainties in collected information and estimation models. Liu et al. investigated the random mean values of elastoplastic responses of structure using a probabilistic partial differentiation approach [1]. Feng et al. introduced a stochastic elastoplastic analysis of two-dimensional engineering structures with the aid of samplingbased machine learning algorithm [2] . In our study, the uncertain parameters were studied simultaneously within the nonlinear dynamical framework with the help of an advanced machine learning (ML) technique [3] . By using the ML algorithm, an explicit regression function can be obtained to represent the relationship between the uncertain inputs and the nonlinear responses. Subsequently, frequent, and fast nonlinear prognosis can be conducted to assess the nonlinear behavior of engineering structures during the dynamic loading process.  \nHere, a brief introduction to the proposed ML-aided framework for nonlinear dynamics of engineering structure is given. The two main components of the approach are briefly introduced. First, the deterministic solution to geometric–elastoplastic dynamics is presented. Then, the novel ML technique named the “extended support regression” is introduced. To demonstrate the accuracy and applicability of the proposed framework, an illustrative numerical case is incorporated to build the proposed framework and demonstrate the nonlinear response for the concerned structure.  \n2 Methods  \n2.1 Solution to Geometric–Elastoplastic Dynamics  \nFor structural systems with both material and geometric nonlinearities, the plastic strain and second-order Green–Lagrange terms must be considered in the incremental strain–displacement relations as:  \nΔε = Δεe + Δεp + Δεg = (B + Bg )Δu (1)  \nwhere Δεe , Δεp and Δεg denote the elastic, plastic and high-order strain increments; B and Bg denote the material and","cbCaihrHeDE3HNfE","https://ap.wps.com/l/cbCaihrHeDE3HNfE","pdf",266132,5,1,6,"English","en",105,"# Introduction\n## Uncertain nonlinear dynamics and prior work\n# Methods\n## Solution to geometric–elastoplastic dynamics\n## Extended support vector regression","[{\"question\":\"How does the proposed method use machine learning in nonlinear dynamic analysis?\",\"answer\":\"It trains a cluster-based extended SVR surrogate on simulation data so that, during Newmark time integration, the model can predict structural deflection, force, and stress at each time step.\"},{\"question\":\"What types of nonlinearities are considered in the engineering structure modeling?\",\"answer\":\"The framework accounts for mixed geometric and material nonlinearities within a 3D dynamic geometric elastoplastic analysis, including large deformation effects.\"},{\"question\":\"How are numerical challenges of mixed nonlinear problems addressed?\",\"answer\":\"The approach replaces repeated costly solvers with an ML-based surrogate model, built at each time step, enabling fast prediction across loading time stages.\"}]","Machine Learning-Aided Nonlinear Dynamic Analysis of Engineering Structures - 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