基于深度学习的水相关混凝土结构环境感知变形预测

IF 8.5 1区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Hao Gu, Yangtao Li, Yixiang Fang, Yiming Wang, Yang Yu, Yang Wei, Liqun Xu, Yijun Chen
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引用次数: 0

摘要

准确的长期变形预测对于保证超高拱坝等大型涉水混凝土结构的结构安全和持续稳定至关重要。传统的统计回归和浅层机器学习方法由于算法的限制,往往无法全面捕捉高维原型监测数据中固有的复杂时空依赖关系,从而限制了其预测的准确性和鲁棒性。为了解决这些挑战,本研究提出了一个多点变形预测模型,该模型结合了环境因素与变形之间的时空相关性,利用先进的深度学习(DL)技术。具体来说,我们采用基于变压器的卷积长短期记忆(ConvLSTM)模型来捕获多个温度和变形监测序列之间的时空依赖性。利用多目标贝叶斯优化算法确定最优模型结构和超参数,实现回归系数最大化和均方根误差(RMSE)最小化。利用超高拱坝多监测点数据,验证了基于dl的高拱坝变形预测模型的有效性。实验结果表明,TransformerConvLSTM方法在5个监测点上的性能明显优于其他模型。在定量上,它始终实现较低的RMSE和较高的相关系数值,表明它具有以最小误差提供准确预测的优越能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
CiteScore
17.60
自引率
19.80%
发文量
146
审稿时长
1 months
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.
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