Xuebing Zhang, Jia Wang, Jun Cao, Yang Quan, Luoqing Liu, Kenxuan Wen, Ping Xiang
{"title":"冻融循环作用下沥青混凝土损伤演变的分布式光纤传感和Informer-LSTM预测","authors":"Xuebing Zhang, Jia Wang, Jun Cao, Yang Quan, Luoqing Liu, Kenxuan Wen, Ping Xiang","doi":"10.1617/s11527-025-02758-y","DOIUrl":null,"url":null,"abstract":"<div><p>In this study, distributed fiber optic sensing together with deep learning frameworks was developed to accurately capture the strain distribution characteristics of asphalt concrete under different conditions. As the performance and damage evolution of asphalt concrete in low-temperature environments have garnered increasing attention, the effects of freeze–thaw cycles on crack evolution in asphalt concrete were investigated under various test conditions, such as saturated versus unsaturated states and elevated versus reduced temperatures. The informer and long short-term memory (Informer-LSTM) networks time series prediction model was proposed to predict the strain state of the specimen across both temporal and spatial dimensions. The results indicate that the distributed fiber optic sensor effectively monitors local damage during the freeze–thaw cycles of the trabeculae and identifies the location of cracks. Furthermore, the Informer-LSTM model accurately captures and predicts strain distribution at the cracks, indicating the superior robustness and adaptability of the parallel time-series prediction model. This research significantly contributes to improving the durability and safety of pavement structures, providing a scientific foundation for assessing the durability and safety of road infrastructure.</p></div>","PeriodicalId":691,"journal":{"name":"Materials and Structures","volume":"58 7","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Distributed fiber optic sensing and Informer-LSTM prediction for damage evolution of asphalt concrete under freeze–thaw cycling\",\"authors\":\"Xuebing Zhang, Jia Wang, Jun Cao, Yang Quan, Luoqing Liu, Kenxuan Wen, Ping Xiang\",\"doi\":\"10.1617/s11527-025-02758-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this study, distributed fiber optic sensing together with deep learning frameworks was developed to accurately capture the strain distribution characteristics of asphalt concrete under different conditions. As the performance and damage evolution of asphalt concrete in low-temperature environments have garnered increasing attention, the effects of freeze–thaw cycles on crack evolution in asphalt concrete were investigated under various test conditions, such as saturated versus unsaturated states and elevated versus reduced temperatures. The informer and long short-term memory (Informer-LSTM) networks time series prediction model was proposed to predict the strain state of the specimen across both temporal and spatial dimensions. The results indicate that the distributed fiber optic sensor effectively monitors local damage during the freeze–thaw cycles of the trabeculae and identifies the location of cracks. Furthermore, the Informer-LSTM model accurately captures and predicts strain distribution at the cracks, indicating the superior robustness and adaptability of the parallel time-series prediction model. This research significantly contributes to improving the durability and safety of pavement structures, providing a scientific foundation for assessing the durability and safety of road infrastructure.</p></div>\",\"PeriodicalId\":691,\"journal\":{\"name\":\"Materials and Structures\",\"volume\":\"58 7\",\"pages\":\"\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Materials and Structures\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://link.springer.com/article/10.1617/s11527-025-02758-y\",\"RegionNum\":3,\"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":"Materials and Structures","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1617/s11527-025-02758-y","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
Distributed fiber optic sensing and Informer-LSTM prediction for damage evolution of asphalt concrete under freeze–thaw cycling
In this study, distributed fiber optic sensing together with deep learning frameworks was developed to accurately capture the strain distribution characteristics of asphalt concrete under different conditions. As the performance and damage evolution of asphalt concrete in low-temperature environments have garnered increasing attention, the effects of freeze–thaw cycles on crack evolution in asphalt concrete were investigated under various test conditions, such as saturated versus unsaturated states and elevated versus reduced temperatures. The informer and long short-term memory (Informer-LSTM) networks time series prediction model was proposed to predict the strain state of the specimen across both temporal and spatial dimensions. The results indicate that the distributed fiber optic sensor effectively monitors local damage during the freeze–thaw cycles of the trabeculae and identifies the location of cracks. Furthermore, the Informer-LSTM model accurately captures and predicts strain distribution at the cracks, indicating the superior robustness and adaptability of the parallel time-series prediction model. This research significantly contributes to improving the durability and safety of pavement structures, providing a scientific foundation for assessing the durability and safety of road infrastructure.
期刊介绍:
Materials and Structures, the flagship publication of the International Union of Laboratories and Experts in Construction Materials, Systems and Structures (RILEM), provides a unique international and interdisciplinary forum for new research findings on the performance of construction materials. A leader in cutting-edge research, the journal is dedicated to the publication of high quality papers examining the fundamental properties of building materials, their characterization and processing techniques, modeling, standardization of test methods, and the application of research results in building and civil engineering. Materials and Structures also publishes comprehensive reports prepared by the RILEM’s technical committees.