[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126387-en":3,"doc-seo-126387-105":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},126387,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","A MACHINE LEARNING BASED MATERIAL HOMOGENIZATION TECHNIQUE FOR IN-PLANE LOADED MASONRY WALLS","The work develops a machine learning–driven homogenization strategy for in-plane analysis of masonry structures within finite element modeling. It targets calibration difficulties in macro-scale approaches while avoiding the high computational cost of micro-scale simulations. The method automatically calibrates a nonlinear damage constitutive law and yield criteria from micro-scale data. A virtual micro-model laboratory, data isotropization, and an optimization minimizing internal dissipated work mismatch enable accurate parameter identification for Flemish bond walls.","A MACHINE LEARNING BASED MATERIAL HOMOGENIZATION TECHNIQUE FOR IN-PLANE LOADED MASONRY WALLS  \narXiv :2408 . 17018v1 [ cs .CE] 30 Aug 2024  \nAlejandro Cornejo  \nCivil and Environmental Engineering Universitat Politècnica de Catalunya International Centre for Numerical Methods in Engineering Campus Norte UPC, 08034 Barcelona, Spain [alejandro.cornejo.velazquez@upc.edu](alejandro.cornejo.velazquez@upc.edu)  \nPhilip Kalkbrenner  \nCivil and Environmental Engineering Universitat Politècnica de Catalunya Campus Norte UPC, 08034 Barcelona, Spain [philip.kalkbrenner@upc.edu](philip.kalkbrenner@upc.edu)  \nRiccardo Rossi  \nCivil and Environmental Engineering  \nUniversitat Politècnica de Catalunya  \nInternational Centre for Numerical Methods in Engineering  \nCampus Norte UPC, 08034 Barcelona, Spain  \n[riccardo.rossi@upc.edu](riccardo.rossi@upc.edu)  \nLuca Pelà  \nCivil and Environmental Engineering  \nUniversitat Politècnica de Catalunya  \nCampus Norte UPC, 08034 Barcelona, Spain  \n[luca.pela@upc.edu](luca.pela@upc.edu)  \nSeptember 2, 2024  \nABSTRACT  \nIn recent years, significant advancements have been made in computational methods for analyzing masonry structures. Within the Finite Element Method, two primary approaches have gained traction: Micro and Macro Scale modeling, and their subsequent integration via Multi-scale methods based on homogenization theory and the representative volume element concept. While Micro and Multi-scale approaches offer high fidelity, they often come with a substantial computational burden. On the other hand, calibrating homogenized material parameters in Macro-scale approaches presents challenges for practical engineering problems.  \nMachine learning techniques have emerged as powerful tools for training models using vast datasets from various domains. In this context, we propose leveraging Machine Learning methods to develop a novel homogenization strategy for the in-plane analysis of masonry structures. This strategy involves automatically calibrating a continuum nonlinear damage constitutive law and an appropriate yield criteria using relevant data derived from Micro-scale analysis. The optimization process not only enhances material parameters but also refines yield criteria and damage evolution laws to better align with existing data.  \nTo achieve this, a virtual laboratory is created to conduct micro-model simulations that account for the individual behaviors of constituent materials. Subsequently, a data isotropization process is employed to reconcile the results with typical isotropic constitutive models. Next, an optimization algorithm that minimizes the difference of internal dissipated work between the micro and macro scales is executed.  \nWe apply this technique to the in-plane homogenization of a Flemish bond masonry wall. Evaluation examples, including simulations of shear and compression tests, demonstrate the method’s accuracy compared to micro modeling of the entire structure.  \nA PREPRINT-SEPTEMBER 2, 2024  \nKeywords Masonry · Machine Learning · Finite Element Method · Material Homogenization · Constitutive Modelling · Anisotropic Materials · Damage Mechanics · Micro and Macro Scales · Composite Materials  \n1 Introduction  \nMasonry is an assembly of many different materials-a conjunction of rocks/stones or bricks usually bound together with mortar. There is no unique definition of how to allocate the units in order to obtain masonry, as it depends on the explicit knowledge developed by each cultural group in order to construct. Nowadays there is a large collection of different masonry construction techniques. In any region, masonry features the local and traditional way of building. Many of the still existing structures around the globe belong to instances that represent social values of humanity, e.g. churches, temples and mosques while palaces represent the administrative institutions that have allowed the development of our society. The great importance of the aforementioned constructio","cbCaitQSzUEyFgpJ","https://ap.wps.com/l/cbCaitQSzUEyFgpJ","pdf",7888968,5,1,33,"English","en",105,"# Introduction\n## Material modelling for masonry and finite element analysis\n## Motivation: micro vs. macro homogenization challenges\n## Proposed machine learning homogenization strategy","[{\"question\":\"What is the main goal of the proposed technique?\",\"answer\":\"To create a machine learning–based material homogenization strategy for in-plane loaded masonry walls by calibrating constitutive and yield parameters from micro-scale data.\"},{\"question\":\"How does the method connect micro-scale simulations to macro-scale models?\",\"answer\":\"It builds a virtual laboratory for micro-model simulations, applies a data isotropization process, and then runs an optimization that minimizes the mismatch of internal dissipated work between scales.\"},{\"question\":\"What material behaviors and model components are calibrated automatically?\",\"answer\":\"A continuum nonlinear damage constitutive law and an appropriate yield criteria are automatically calibrated, along with refinement of damage evolution laws to better fit micro-derived data.\"}]","A MACHINE LEARNING BASED MATERIAL HOMOGENIZATION TECHNIQUE FOR IN-PLANE LOADED MASONRY WALLS | 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is the main goal of the proposed technique?","Question",{"text":77,"@type":78},"To create a machine learning–based material homogenization strategy for in-plane loaded masonry walls by calibrating constitutive and yield parameters from micro-scale data.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does the method connect micro-scale simulations to macro-scale models?",{"text":82,"@type":78},"It builds a virtual laboratory for micro-model simulations, applies a data isotropization process, and then runs an optimization that minimizes the mismatch of internal dissipated work between scales.",{"name":84,"@type":75,"acceptedAnswer":85},"What material behaviors and model components are calibrated automatically?",{"text":86,"@type":78},"A continuum nonlinear damage constitutive law and an appropriate yield criteria are automatically calibrated, along with refinement of damage evolution laws to better fit micro-derived 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