{"title":"基于深度迁移学习的高土岩大坝变形监测时变模型","authors":"","doi":"10.1016/j.engappai.2024.109310","DOIUrl":null,"url":null,"abstract":"<div><p>The analysis of deformation, a critical aspect of high earth-rock dams, holds immense importance for ensuring the safety and stability of dam operations. The structural behavior of high earth-rock dams exhibits time-varying nonlinear characteristics influenced by materials and loads. Over time, the fitting and prediction abilities of static dam Structural Health Monitoring (SHM) models tend to diminish. To address this, a novel SHM model is proposed in this study. It leverages deep transfer learning to enhance prediction accuracy and generalization by incorporating a starting point timestamp and employing a transfer learning approach. The methodology begins with the construction of an encoder structure based on the graph convolutional network and long and short-term memory model. Additionally, the attention mechanism-based encoder structure is designed to include starting point time markers. Knowledge migration is then executed through transfer learning, thereby improving the model's generalization to the time-varying deformation challenge. The proposed model is applied to a horizontal displacement monitoring project for a 185.5 m-high panel rockfill dam. Ablation experiments demonstrate that the transfer learning method effectively enhances the model's handling of time-varying deformation by improving prediction accuracy, with a more pronounced effect observed for measurement points close to the top of the dam and the upstream dam face. Comparison with eight baseline models validates that the proposed model achieves optimal prediction, fitting performance, and generalization. Consequently, the model emerges as a more suitable choice for the deformation health monitoring of high earth-rock dam projects.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep transfer learning-based time-varying model for deformation monitoring of high earth-rock dams\",\"authors\":\"\",\"doi\":\"10.1016/j.engappai.2024.109310\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The analysis of deformation, a critical aspect of high earth-rock dams, holds immense importance for ensuring the safety and stability of dam operations. The structural behavior of high earth-rock dams exhibits time-varying nonlinear characteristics influenced by materials and loads. Over time, the fitting and prediction abilities of static dam Structural Health Monitoring (SHM) models tend to diminish. To address this, a novel SHM model is proposed in this study. It leverages deep transfer learning to enhance prediction accuracy and generalization by incorporating a starting point timestamp and employing a transfer learning approach. The methodology begins with the construction of an encoder structure based on the graph convolutional network and long and short-term memory model. Additionally, the attention mechanism-based encoder structure is designed to include starting point time markers. Knowledge migration is then executed through transfer learning, thereby improving the model's generalization to the time-varying deformation challenge. The proposed model is applied to a horizontal displacement monitoring project for a 185.5 m-high panel rockfill dam. Ablation experiments demonstrate that the transfer learning method effectively enhances the model's handling of time-varying deformation by improving prediction accuracy, with a more pronounced effect observed for measurement points close to the top of the dam and the upstream dam face. Comparison with eight baseline models validates that the proposed model achieves optimal prediction, fitting performance, and generalization. Consequently, the model emerges as a more suitable choice for the deformation health monitoring of high earth-rock dam projects.</p></div>\",\"PeriodicalId\":50523,\"journal\":{\"name\":\"Engineering Applications of Artificial Intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2024-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Applications of Artificial Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0952197624014684\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Applications of Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0952197624014684","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Deep transfer learning-based time-varying model for deformation monitoring of high earth-rock dams
The analysis of deformation, a critical aspect of high earth-rock dams, holds immense importance for ensuring the safety and stability of dam operations. The structural behavior of high earth-rock dams exhibits time-varying nonlinear characteristics influenced by materials and loads. Over time, the fitting and prediction abilities of static dam Structural Health Monitoring (SHM) models tend to diminish. To address this, a novel SHM model is proposed in this study. It leverages deep transfer learning to enhance prediction accuracy and generalization by incorporating a starting point timestamp and employing a transfer learning approach. The methodology begins with the construction of an encoder structure based on the graph convolutional network and long and short-term memory model. Additionally, the attention mechanism-based encoder structure is designed to include starting point time markers. Knowledge migration is then executed through transfer learning, thereby improving the model's generalization to the time-varying deformation challenge. The proposed model is applied to a horizontal displacement monitoring project for a 185.5 m-high panel rockfill dam. Ablation experiments demonstrate that the transfer learning method effectively enhances the model's handling of time-varying deformation by improving prediction accuracy, with a more pronounced effect observed for measurement points close to the top of the dam and the upstream dam face. Comparison with eight baseline models validates that the proposed model achieves optimal prediction, fitting performance, and generalization. Consequently, the model emerges as a more suitable choice for the deformation health monitoring of high earth-rock dam projects.
期刊介绍:
Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.