{"title":"Reinforced concrete beam full response prediction with hybrid feature-orientation transformer-LSTM model","authors":"Zecheng Yu, Bing Li","doi":"10.1016/j.engstruct.2025.120040","DOIUrl":null,"url":null,"abstract":"<div><div>Accurate and efficient prediction of load-displacement behavior in reinforced concrete (RC) beams is essential for data-driven interventions and predictive maintenance in structural engineering. Such predictions are critical for ensuring the safety, reliability, and longevity of structures. Develop a comprehensive approach that incorporates advanced deep learning (DL) techniques, leveraging historical data and time series modeling to achieve high-precision predictions. This study introduces a hybrid feature-orientation Transformer-LSTM (HFT-LSTM) model for accurately predicting the load-displacement response of RC beams, from initial loading to failure. It leverages a Transformer architecture, embedded with a multi-headed attention mechanism, to achieve a deeper representation of constant features and dynamic fusion of variable features, enabling a more comprehensive understanding of the RC beam's behavior. It employs a bidirectional LSTM to capture dynamic and temporal relationships within the displacement series. To gain a deeper understanding of the history-dependent mechanical behavior of RC beams, we conducted a detailed analysis of the influence of input features on the load-displacement curve. The proposed model significantly outperforms existing DL models, achieving a 46 % reduction in Mean Absolute Error (MAE), a 58 % decrease in Root Mean Squared Error (RMSE), a 5 % increase in R-squared (R²) and Explained Variance (EV), and a 36 % reduction in Median Absolute Error (MedAE).</div></div>","PeriodicalId":11763,"journal":{"name":"Engineering Structures","volume":"332 ","pages":"Article 120040"},"PeriodicalIF":5.6000,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Structures","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0141029625004316","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0
Abstract
Accurate and efficient prediction of load-displacement behavior in reinforced concrete (RC) beams is essential for data-driven interventions and predictive maintenance in structural engineering. Such predictions are critical for ensuring the safety, reliability, and longevity of structures. Develop a comprehensive approach that incorporates advanced deep learning (DL) techniques, leveraging historical data and time series modeling to achieve high-precision predictions. This study introduces a hybrid feature-orientation Transformer-LSTM (HFT-LSTM) model for accurately predicting the load-displacement response of RC beams, from initial loading to failure. It leverages a Transformer architecture, embedded with a multi-headed attention mechanism, to achieve a deeper representation of constant features and dynamic fusion of variable features, enabling a more comprehensive understanding of the RC beam's behavior. It employs a bidirectional LSTM to capture dynamic and temporal relationships within the displacement series. To gain a deeper understanding of the history-dependent mechanical behavior of RC beams, we conducted a detailed analysis of the influence of input features on the load-displacement curve. The proposed model significantly outperforms existing DL models, achieving a 46 % reduction in Mean Absolute Error (MAE), a 58 % decrease in Root Mean Squared Error (RMSE), a 5 % increase in R-squared (R²) and Explained Variance (EV), and a 36 % reduction in Median Absolute Error (MedAE).
期刊介绍:
Engineering Structures provides a forum for a broad blend of scientific and technical papers to reflect the evolving needs of the structural engineering and structural mechanics communities. Particularly welcome are contributions dealing with applications of structural engineering and mechanics principles in all areas of technology. The journal aspires to a broad and integrated coverage of the effects of dynamic loadings and of the modelling techniques whereby the structural response to these loadings may be computed.
The scope of Engineering Structures encompasses, but is not restricted to, the following areas: infrastructure engineering; earthquake engineering; structure-fluid-soil interaction; wind engineering; fire engineering; blast engineering; structural reliability/stability; life assessment/integrity; structural health monitoring; multi-hazard engineering; structural dynamics; optimization; expert systems; experimental modelling; performance-based design; multiscale analysis; value engineering.
Topics of interest include: tall buildings; innovative structures; environmentally responsive structures; bridges; stadiums; commercial and public buildings; transmission towers; television and telecommunication masts; foldable structures; cooling towers; plates and shells; suspension structures; protective structures; smart structures; nuclear reactors; dams; pressure vessels; pipelines; tunnels.
Engineering Structures also publishes review articles, short communications and discussions, book reviews, and a diary on international events related to any aspect of structural engineering.