{"title":"基于长短期记忆神经网络和共享气象数据的桥梁温度预测方法","authors":"Linren Zhou, Taojun Wang, Yumeng Chen","doi":"10.1177/13694332241247918","DOIUrl":null,"url":null,"abstract":"Temperature is an important load factor affecting the structural performances of bridges. The rapid acquisition of bridge temperature data is significant for bridge temperature effect analysis and assessment. On the bases of ground meteorological shared big data, a bridge temperature prediction method based on long short-term memory (LSTM) neural network is proposed. The proposed method is used to investigate the key issues of data preprocessing, model input feature selection, time-length determination, and hyper-parameter preference. Moreover, the proposed method relies on the maximum information coefficient to quantify the strongly correlated features and uses a two-layer deep LSTM neural network to improve the model’s time series information utilization and prediction capability. The constructed neural grid model is experimentally studied and verified based on the long-term measured data of the scaled bridge model in an outdoor environment. Comparative assessment with other typical time series models, such as NARX, RNN, and GRU, demonstrate that the LSTM neural network model exhibits the best generalization ability and highest temperature prediction accuracy, with a maximum absolute error of approximately 2°C and relative error below 5%. The engineering applicability and effectiveness of LSTM for bridge temperature prediction are verified.","PeriodicalId":50849,"journal":{"name":"Advances in Structural Engineering","volume":"58 1","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bridge temperature prediction method based on long short-term memory neural networks and shared meteorological data\",\"authors\":\"Linren Zhou, Taojun Wang, Yumeng Chen\",\"doi\":\"10.1177/13694332241247918\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Temperature is an important load factor affecting the structural performances of bridges. The rapid acquisition of bridge temperature data is significant for bridge temperature effect analysis and assessment. On the bases of ground meteorological shared big data, a bridge temperature prediction method based on long short-term memory (LSTM) neural network is proposed. The proposed method is used to investigate the key issues of data preprocessing, model input feature selection, time-length determination, and hyper-parameter preference. Moreover, the proposed method relies on the maximum information coefficient to quantify the strongly correlated features and uses a two-layer deep LSTM neural network to improve the model’s time series information utilization and prediction capability. The constructed neural grid model is experimentally studied and verified based on the long-term measured data of the scaled bridge model in an outdoor environment. Comparative assessment with other typical time series models, such as NARX, RNN, and GRU, demonstrate that the LSTM neural network model exhibits the best generalization ability and highest temperature prediction accuracy, with a maximum absolute error of approximately 2°C and relative error below 5%. The engineering applicability and effectiveness of LSTM for bridge temperature prediction are verified.\",\"PeriodicalId\":50849,\"journal\":{\"name\":\"Advances in Structural Engineering\",\"volume\":\"58 1\",\"pages\":\"\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Structural Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1177/13694332241247918\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Structural Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/13694332241247918","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
Bridge temperature prediction method based on long short-term memory neural networks and shared meteorological data
Temperature is an important load factor affecting the structural performances of bridges. The rapid acquisition of bridge temperature data is significant for bridge temperature effect analysis and assessment. On the bases of ground meteorological shared big data, a bridge temperature prediction method based on long short-term memory (LSTM) neural network is proposed. The proposed method is used to investigate the key issues of data preprocessing, model input feature selection, time-length determination, and hyper-parameter preference. Moreover, the proposed method relies on the maximum information coefficient to quantify the strongly correlated features and uses a two-layer deep LSTM neural network to improve the model’s time series information utilization and prediction capability. The constructed neural grid model is experimentally studied and verified based on the long-term measured data of the scaled bridge model in an outdoor environment. Comparative assessment with other typical time series models, such as NARX, RNN, and GRU, demonstrate that the LSTM neural network model exhibits the best generalization ability and highest temperature prediction accuracy, with a maximum absolute error of approximately 2°C and relative error below 5%. The engineering applicability and effectiveness of LSTM for bridge temperature prediction are verified.
期刊介绍:
Advances in Structural Engineering was established in 1997 and has become one of the major peer-reviewed journals in the field of structural engineering. To better fulfil the mission of the journal, we have recently decided to launch two new features for the journal: (a) invited review papers providing an in-depth exposition of a topic of significant current interest; (b) short papers reporting truly new technologies in structural engineering.