Mauricio Pereira, Antonio Maria D'Altri, Stefano de Miranda, Branko Glisic
{"title":"基于裂缝模式的机器学习预测受损砌体墙体的残余漂移能力","authors":"Mauricio Pereira, Antonio Maria D'Altri, Stefano de Miranda, Branko Glisic","doi":"10.1111/mice.13212","DOIUrl":null,"url":null,"abstract":"In this paper, we present a method based on an ensemble of convolutional neural networks (CNNs) for the prediction of residual drift capacity in unreinforced damaged masonry walls using as only input the crack pattern. We use an accurate block-based numerical model to generate mechanically consistent crack patterns induced by external actions (earthquake-like loads and differential settlements). For a damaged masonry wall, we extract the crack width cumulative distribution, we derive a crack width exceedance curve (CWEC), and we evaluate the drift loss (DL) with respect to the undamaged wall. Numerous pairs of CWEC and DL are thus generated and used for training (and validating) an ensemble of CNNs generated via repeated <span data-altimg=\"/cms/asset/73c1e1c4-9d95-4765-b87a-eb7141100bb6/mice13212-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"159\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/mice13212-math-0001.png\"><mjx-semantics><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-role=\"latinletter\" data-semantic-speech=\"k\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:10939687:media:mice13212:mice13212-math-0001\" display=\"inline\" location=\"graphic/mice13212-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic-role=\"latinletter\" data-semantic-speech=\"k\" data-semantic-type=\"identifier\">k</mi>$k$</annotation></semantics></math></mjx-assistive-mml></mjx-container>-folding cross validation with shuffling. As a result, a method for damage prognosis (Level IV of SHM) is provided. Such method appears general, inexpensive, and able to adequately predict the DL using as only input the CWEC, providing real-time support for decision making in damaged masonry structures.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":8.5000,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Crack pattern–based machine learning prediction of residual drift capacity in damaged masonry walls\",\"authors\":\"Mauricio Pereira, Antonio Maria D'Altri, Stefano de Miranda, Branko Glisic\",\"doi\":\"10.1111/mice.13212\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present a method based on an ensemble of convolutional neural networks (CNNs) for the prediction of residual drift capacity in unreinforced damaged masonry walls using as only input the crack pattern. We use an accurate block-based numerical model to generate mechanically consistent crack patterns induced by external actions (earthquake-like loads and differential settlements). For a damaged masonry wall, we extract the crack width cumulative distribution, we derive a crack width exceedance curve (CWEC), and we evaluate the drift loss (DL) with respect to the undamaged wall. Numerous pairs of CWEC and DL are thus generated and used for training (and validating) an ensemble of CNNs generated via repeated <span data-altimg=\\\"/cms/asset/73c1e1c4-9d95-4765-b87a-eb7141100bb6/mice13212-math-0001.png\\\"></span><mjx-container ctxtmenu_counter=\\\"159\\\" ctxtmenu_oldtabindex=\\\"1\\\" jax=\\\"CHTML\\\" role=\\\"application\\\" sre-explorer- style=\\\"font-size: 103%; position: relative;\\\" tabindex=\\\"0\\\"><mjx-math aria-hidden=\\\"true\\\" location=\\\"graphic/mice13212-math-0001.png\\\"><mjx-semantics><mjx-mi data-semantic-annotation=\\\"clearspeak:simple\\\" data-semantic-font=\\\"italic\\\" data-semantic- data-semantic-role=\\\"latinletter\\\" data-semantic-speech=\\\"k\\\" data-semantic-type=\\\"identifier\\\"><mjx-c></mjx-c></mjx-mi></mjx-semantics></mjx-math><mjx-assistive-mml display=\\\"inline\\\" unselectable=\\\"on\\\"><math altimg=\\\"urn:x-wiley:10939687:media:mice13212:mice13212-math-0001\\\" display=\\\"inline\\\" location=\\\"graphic/mice13212-math-0001.png\\\" xmlns=\\\"http://www.w3.org/1998/Math/MathML\\\"><semantics><mi data-semantic-=\\\"\\\" data-semantic-annotation=\\\"clearspeak:simple\\\" data-semantic-font=\\\"italic\\\" data-semantic-role=\\\"latinletter\\\" data-semantic-speech=\\\"k\\\" data-semantic-type=\\\"identifier\\\">k</mi>$k$</annotation></semantics></math></mjx-assistive-mml></mjx-container>-folding cross validation with shuffling. As a result, a method for damage prognosis (Level IV of SHM) is provided. Such method appears general, inexpensive, and able to adequately predict the DL using as only input the CWEC, providing real-time support for decision making in damaged masonry structures.\",\"PeriodicalId\":156,\"journal\":{\"name\":\"Computer-Aided Civil and Infrastructure Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.5000,\"publicationDate\":\"2024-05-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer-Aided Civil and Infrastructure Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1111/mice.13212\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer-Aided Civil and Infrastructure Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1111/mice.13212","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Crack pattern–based machine learning prediction of residual drift capacity in damaged masonry walls
In this paper, we present a method based on an ensemble of convolutional neural networks (CNNs) for the prediction of residual drift capacity in unreinforced damaged masonry walls using as only input the crack pattern. We use an accurate block-based numerical model to generate mechanically consistent crack patterns induced by external actions (earthquake-like loads and differential settlements). For a damaged masonry wall, we extract the crack width cumulative distribution, we derive a crack width exceedance curve (CWEC), and we evaluate the drift loss (DL) with respect to the undamaged wall. Numerous pairs of CWEC and DL are thus generated and used for training (and validating) an ensemble of CNNs generated via repeated -folding cross validation with shuffling. As a result, a method for damage prognosis (Level IV of SHM) is provided. Such method appears general, inexpensive, and able to adequately predict the DL using as only input the CWEC, providing real-time support for decision making in damaged masonry structures.
期刊介绍:
Computer-Aided Civil and Infrastructure Engineering stands as a scholarly, peer-reviewed archival journal, serving as a vital link between advancements in computer technology and civil and infrastructure engineering. The journal serves as a distinctive platform for the publication of original articles, spotlighting novel computational techniques and inventive applications of computers. Specifically, it concentrates on recent progress in computer and information technologies, fostering the development and application of emerging computing paradigms.
Encompassing a broad scope, the journal addresses bridge, construction, environmental, highway, geotechnical, structural, transportation, and water resources engineering. It extends its reach to the management of infrastructure systems, covering domains such as highways, bridges, pavements, airports, and utilities. The journal delves into areas like artificial intelligence, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, internet-based technologies, knowledge discovery and engineering, machine learning, mobile computing, multimedia technologies, networking, neural network computing, optimization and search, parallel processing, robotics, smart structures, software engineering, virtual reality, and visualization techniques.