{"title":"基于长短期记忆的无约束路面冻结深度和融化时间实时预测模型","authors":"Y. Ma, S. Park, A. Bae, K. Kwon, H. Choi","doi":"10.1111/mice.13516","DOIUrl":null,"url":null,"abstract":"The prediction of freezing depth and thawing time of unbound pavement layers in cold regions is a critical task in pavement design and management. This study developed long short‐term memory (LSTM)‐based encoder–decoder models to accurately predict freezing depth and thawing time, with air temperature as the sole input variable. The models, which aim to offer a 14‐day advance prediction of the thawing time for effective pavement management, utilized data from the Long‐Term Pavement Performance program's database, provided by the Federal Highway Administration in United States. This database contains extensive records on air temperature and freezing states. The LSTM models were trained using data collected from four regions in North America with severely cold winters (Quebec, Minnesota, Ontario, and Maine) and subsequently validated using data from both severely cold (South Dakota and Vermont) and mild (Idaho and Wyoming) winter regions. During the validation phase, the models demonstrated strong performance in the severely cold regions, with predicted freezing depths deviating from the measured values by only 0.05 to 0.20 m and thawing date predictions differing by just 1 to 3 days. However, in the mild winter regions, the models showed less accuracy, with freezing depth differences ranging from 0.10 to 0.40 m and thawing date delays of 3–6 days. Compared to existing analytical and empirical models, the LSTM prediction models developed in this study provide enhanced convenience while maintaining a satisfactory level of accuracy.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"162 1","pages":""},"PeriodicalIF":8.5000,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Long short‐term memory‐based real‐time prediction models for freezing depth and thawing time in unbound pavement layers\",\"authors\":\"Y. Ma, S. Park, A. Bae, K. Kwon, H. Choi\",\"doi\":\"10.1111/mice.13516\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The prediction of freezing depth and thawing time of unbound pavement layers in cold regions is a critical task in pavement design and management. This study developed long short‐term memory (LSTM)‐based encoder–decoder models to accurately predict freezing depth and thawing time, with air temperature as the sole input variable. The models, which aim to offer a 14‐day advance prediction of the thawing time for effective pavement management, utilized data from the Long‐Term Pavement Performance program's database, provided by the Federal Highway Administration in United States. This database contains extensive records on air temperature and freezing states. The LSTM models were trained using data collected from four regions in North America with severely cold winters (Quebec, Minnesota, Ontario, and Maine) and subsequently validated using data from both severely cold (South Dakota and Vermont) and mild (Idaho and Wyoming) winter regions. During the validation phase, the models demonstrated strong performance in the severely cold regions, with predicted freezing depths deviating from the measured values by only 0.05 to 0.20 m and thawing date predictions differing by just 1 to 3 days. However, in the mild winter regions, the models showed less accuracy, with freezing depth differences ranging from 0.10 to 0.40 m and thawing date delays of 3–6 days. Compared to existing analytical and empirical models, the LSTM prediction models developed in this study provide enhanced convenience while maintaining a satisfactory level of accuracy.\",\"PeriodicalId\":156,\"journal\":{\"name\":\"Computer-Aided Civil and Infrastructure Engineering\",\"volume\":\"162 1\",\"pages\":\"\"},\"PeriodicalIF\":8.5000,\"publicationDate\":\"2025-05-20\",\"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.13516\",\"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.13516","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}
Long short‐term memory‐based real‐time prediction models for freezing depth and thawing time in unbound pavement layers
The prediction of freezing depth and thawing time of unbound pavement layers in cold regions is a critical task in pavement design and management. This study developed long short‐term memory (LSTM)‐based encoder–decoder models to accurately predict freezing depth and thawing time, with air temperature as the sole input variable. The models, which aim to offer a 14‐day advance prediction of the thawing time for effective pavement management, utilized data from the Long‐Term Pavement Performance program's database, provided by the Federal Highway Administration in United States. This database contains extensive records on air temperature and freezing states. The LSTM models were trained using data collected from four regions in North America with severely cold winters (Quebec, Minnesota, Ontario, and Maine) and subsequently validated using data from both severely cold (South Dakota and Vermont) and mild (Idaho and Wyoming) winter regions. During the validation phase, the models demonstrated strong performance in the severely cold regions, with predicted freezing depths deviating from the measured values by only 0.05 to 0.20 m and thawing date predictions differing by just 1 to 3 days. However, in the mild winter regions, the models showed less accuracy, with freezing depth differences ranging from 0.10 to 0.40 m and thawing date delays of 3–6 days. Compared to existing analytical and empirical models, the LSTM prediction models developed in this study provide enhanced convenience while maintaining a satisfactory level of accuracy.
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.