Hao Gu, Yangtao Li, Yixiang Fang, Yiming Wang, Yang Yu, Yang Wei, Liqun Xu, Yijun Chen
{"title":"基于深度学习的水相关混凝土结构环境感知变形预测","authors":"Hao Gu, Yangtao Li, Yixiang Fang, Yiming Wang, Yang Yu, Yang Wei, Liqun Xu, Yijun Chen","doi":"10.1111/mice.13513","DOIUrl":null,"url":null,"abstract":"Accurate long-term deformation prediction is essential to ensure the structural security and ongoing stability of large water-related concrete structures like ultra-high arch dams. Traditional statistical regression and shallow machine learning approaches, due to their algorithmic constraints, often fail to comprehensively capture the complex temporal and spatial dependencies inherent in high-dimensional prototypical monitoring data, thereby limiting their predictive accuracy and robustness. To address these challenges, this study proposes a multi-point deformation forecasting model that incorporates both spatial and temporal correlations between environmental factors and deformation, utilizing advanced deep learning (DL) techniques. Specifically, we employ a Transformer-based convolutional long short-term memory (ConvLSTM) model to capture the spatiotemporal dependencies across numerous temperature and deformation monitoring sequences. Furthermore, the multi-objective bayesian optimization algorithm is utilized to ascertain the optimal model architecture and hyperparameters, concurrently maximizing the regression coefficient and minimizing the root mean square error (RMSE). The effectiveness of the proposed DL-based model for high-arch dam deformation prediction is validated using data from multiple monitoring points of ultra-high arch dams. Experimental results demonstrate that the TransformerConvLSTM method significantly outperforms other models at five monitoring points. Quantitatively, it consistently achieves lower RMSE and high correlation coefficient values, indicating its superior ability to provide accurate predictions with minimal error.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"1 1","pages":""},"PeriodicalIF":8.5000,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Environmental-aware deformation prediction of water-related concrete structures using deep learning\",\"authors\":\"Hao Gu, Yangtao Li, Yixiang Fang, Yiming Wang, Yang Yu, Yang Wei, Liqun Xu, Yijun Chen\",\"doi\":\"10.1111/mice.13513\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accurate long-term deformation prediction is essential to ensure the structural security and ongoing stability of large water-related concrete structures like ultra-high arch dams. Traditional statistical regression and shallow machine learning approaches, due to their algorithmic constraints, often fail to comprehensively capture the complex temporal and spatial dependencies inherent in high-dimensional prototypical monitoring data, thereby limiting their predictive accuracy and robustness. To address these challenges, this study proposes a multi-point deformation forecasting model that incorporates both spatial and temporal correlations between environmental factors and deformation, utilizing advanced deep learning (DL) techniques. Specifically, we employ a Transformer-based convolutional long short-term memory (ConvLSTM) model to capture the spatiotemporal dependencies across numerous temperature and deformation monitoring sequences. Furthermore, the multi-objective bayesian optimization algorithm is utilized to ascertain the optimal model architecture and hyperparameters, concurrently maximizing the regression coefficient and minimizing the root mean square error (RMSE). The effectiveness of the proposed DL-based model for high-arch dam deformation prediction is validated using data from multiple monitoring points of ultra-high arch dams. Experimental results demonstrate that the TransformerConvLSTM method significantly outperforms other models at five monitoring points. 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Environmental-aware deformation prediction of water-related concrete structures using deep learning
Accurate long-term deformation prediction is essential to ensure the structural security and ongoing stability of large water-related concrete structures like ultra-high arch dams. Traditional statistical regression and shallow machine learning approaches, due to their algorithmic constraints, often fail to comprehensively capture the complex temporal and spatial dependencies inherent in high-dimensional prototypical monitoring data, thereby limiting their predictive accuracy and robustness. To address these challenges, this study proposes a multi-point deformation forecasting model that incorporates both spatial and temporal correlations between environmental factors and deformation, utilizing advanced deep learning (DL) techniques. Specifically, we employ a Transformer-based convolutional long short-term memory (ConvLSTM) model to capture the spatiotemporal dependencies across numerous temperature and deformation monitoring sequences. Furthermore, the multi-objective bayesian optimization algorithm is utilized to ascertain the optimal model architecture and hyperparameters, concurrently maximizing the regression coefficient and minimizing the root mean square error (RMSE). The effectiveness of the proposed DL-based model for high-arch dam deformation prediction is validated using data from multiple monitoring points of ultra-high arch dams. Experimental results demonstrate that the TransformerConvLSTM method significantly outperforms other models at five monitoring points. Quantitatively, it consistently achieves lower RMSE and high correlation coefficient values, indicating its superior ability to provide accurate predictions with minimal error.
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.