Engineering Structures最新文献

{"title":"Shear response and economic efficiency of steel reinforced ECC hollow beam without web reinforcement: Towards lightweight structure development","authors":"Qiao Liao ,&nbsp;Zi-Ming Tang ,&nbsp;Bi-Xiong Li ,&nbsp;Feng Dai ,&nbsp;Ke-Quan Yu","doi":"10.1016/j.engstruct.2026.122172","DOIUrl":"10.1016/j.engstruct.2026.122172","url":null,"abstract":"<div><div>Engineered cementitious composites (ECC) possesses prominent mechanical properties and low density, which is expected to be applied in high-performance lightweight structures. Unfortunately, ECC owns high preparation cost owing to the utilization of a large amount of fibers and binder materials. To realize light weight and lower cost, steel reinforced ECC hollow beams without web reinforcement were proposed in the current research. The shear responses of ECC hollow beams were experimentally studied. A total of twelve beams with various hollow ratios (i.e., 0%, 3.14% and 12.56%) and matrix materials (i.e., 0.22, 0.27 and 0.32 in the water-to-binder ratio of ECC or mortar) were tested by three-point bending. Digital image correlation (DIC) system was applied for recording crack development during the shear test. The results demonstrated that some ECC hollow beams possessed larger shear capacity and better ductility in comparison with ECC solid beams. The prediction model for the shear capacity of ECC hollow beams was put forward through considering the role of fibers, and the predicted results were well matched to the experimental results. The failure mechanism and hollow shape effect (i.e., square, triangle and circle hollow) of ECC hollow beams were analyzed through finite element software. At the same hollow ratio, ECC beams with circle hollow possessed larger shear capacity when compared with ECC beams with square or triangle hollow. Furthermore, the economic efficiency and composite performance of these beams were evaluated. The proposed composite index considering bearing capacity, cost and weight indicated that compared with ECC solid beams and mortar hollow beams, ECC hollow beams held superior comprehensive performance. This study laid the cornerstone for the engineering utilization of steel reinforced ECC hollow beams without web reinforcement.</div></div>","PeriodicalId":11763,"journal":{"name":"Engineering Structures","volume":"353 ","pages":"Article 122172"},"PeriodicalIF":6.4,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of crack repair level by measuring the electrical resistance of the crack and repair area","authors":"Tae Uk Kim,&nbsp;Dong Joo Kim","doi":"10.1016/j.engstruct.2026.122269","DOIUrl":"10.1016/j.engstruct.2026.122269","url":null,"abstract":"<div><div>Evaluation of the crack repair level (CRL) is crucial for restoring the mechanical performance of damaged concrete. Current methods rely on surface inspection or complex procedures, making reliable real-time assessment difficult. This study proposes an innovative method for estimating CRL using the electrical resistance of the crack and repair area. When a repair material containing conductive fillers is injected, current flow is restored, and electrical resistance decreases. The CRL is then inferred from the change in electrical resistance relative to the injected volume fraction. Electrical field analysis produced piecewise linear equations linking electrical resistance to CRL. Experiments conducted under varying temperature and relative humidity (RH) conditions verified the method’s accuracy. For a 40 mm crack, electrical resistance decreased from 1.38 to 0.59 kΩ as the CRL increased from 0 % to 100 % at 25 ℃ and 60 % RH. Analytical and experimental results differed by only 1–4 %, with determination coefficients greater than 0.995. This method provides a rapid and accurate tool for real-time evaluation of crack repairs, aiding structural maintenance.</div></div>","PeriodicalId":11763,"journal":{"name":"Engineering Structures","volume":"353 ","pages":"Article 122269"},"PeriodicalIF":6.4,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146184788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural behaviour of reinforced rubberised concrete beam with waste tyre steel fibres","authors":"Muneeb Qureshi,&nbsp;Jun Li","doi":"10.1016/j.engstruct.2026.122230","DOIUrl":"10.1016/j.engstruct.2026.122230","url":null,"abstract":"<div><div>This study evaluates a novel high-performance rubberised concrete (RuC) system incorporating sustainable waste materials. In the concrete mix design, 63 % cement was replaced by supplementary cementitious materials (SCMs), specifically a blend of 36 % Ground-Granulated Blast Furnace Slag (GGBS) and 27 % Fly Ash (FA), and the fine natural sand was replaced by waste tyre rubber (WTR) particles at 10 % volume fraction. The RuC material matrix ultimately developed a 28-day compressive strength of 86.4 MPa. The mix was further reinforced by waste tyre steel fibres (WTSF) at 1 %-3 % volume fraction. WTSF addition significantly improved post-cracking tensile behaviour, with 3 % WTSF enhancing flexural strength by 32 %. Structural testing of reinforced RuC beams demonstrated the synergistic combination of conventional steel rebars plus WTSF improved the ultimate loading capacity. Reinforced RuC beams supplemented with 1 % WTSF exhibited 7 % higher load capacity, while 2 % WTSF addition further increased this capacity by 17 % compared to reinforced RuC specimens with no fibre addition. The optimised hybrid system (RuC mixture + WTSF + steel rebar) ultimately supported approximately 20 % greater loads than reinforced control concrete beam. The economic assessment validates the viability of this sustainable approach, demonstrating that incorporating WTSF reduces material costs while significantly lowering environmental impact. By replacing 63 % of Portland cement with SCMs, the resulting mixture achieves a notable reduction in cost per megapascal and carbon emissions. The research validates RuC as a technically viable and environmentally sustainable alternative for structural applications, balancing mechanical performance, ductility, and eco-efficiency through intelligent waste material utilisation.</div></div>","PeriodicalId":11763,"journal":{"name":"Engineering Structures","volume":"353 ","pages":"Article 122230"},"PeriodicalIF":6.4,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-objective active vibration control strategy combining imitation learning and deep reinforcement learning: Proof-of-concept on a multi-story frame","authors":"Yi-Ang Zhang ,&nbsp;Songye Zhu","doi":"10.1016/j.engstruct.2026.122150","DOIUrl":"10.1016/j.engstruct.2026.122150","url":null,"abstract":"<div><div>Classical model-based optimal active controllers often suffer from performance degradation due to inaccuracies in structural models. To address this issue, this study introduces a novel model-free active control strategy that combines imitation learning (IL) and deep reinforcement learning (DRL) and aims to achieve efficient, robust, and multi-objective active structural vibration control. IL pre-trains a neural network (NN) controller, which is subsequently fine-tuned by DRL under random excitations without using any structural model information. The IL pre-training enhances the subsequent DRL sampling efficiency and training stability. The DRL reward function can flexibly incorporate multiple performance indices, enabling multi-objective optimization. This strategy overcomes a key limitation of classical model-based control algorithms, in which the objective functions are often in a fixed form and lack the flexibility to fit different situations. The proposed IL–DRL framework is experimentally validated on the laboratory model of a three-story frame structure equipped with an active mass damper. Its control performance is proven to outperform classical model-based controllers (such as the linear quadratic regulator and linear quadratic Gaussian controllers) under various excitations with full and partial state observation feedback. The experimental results affirm the method’s efficacy as a model-free control strategy that is promising for real-world applications.</div></div>","PeriodicalId":11763,"journal":{"name":"Engineering Structures","volume":"353 ","pages":"Article 122150"},"PeriodicalIF":6.4,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliability assessment of steel bridge elements in terms of the condition ratings under varying environmental conditions: A case study of New Jersey bridges","authors":"Ahmed A. Saady ,&nbsp;S. Hooman Ghasemi ,&nbsp;John Braley ,&nbsp;Perumalsamy Balaguru ,&nbsp;Ali Maher","doi":"10.1016/j.engstruct.2026.122355","DOIUrl":"10.1016/j.engstruct.2026.122355","url":null,"abstract":"<div><div>Several studies have evaluated bridge performance over time using statistical and stochastic methodologies and determined the reliability index. However, there are few studies on predicting bridge element painting ratings from bridge age, painting age, and environmental variables. Furthermore, a definitive method for correlating condition ratings with the reliability index is lacking, even though load ratings alone may inadequately represent the structure's actual condition or safety. The primary contribution of this paper is the integration of statistical and stochastic analysis to evaluate the painting rating of steel bridge elements under various conditions and subsequently establish the relationship between the rating and the reliability index. This includes a Monte Carlo simulation of rating distributions and a linear regression model for the mean and standard deviation to predict bridge ratings based on building age and environmental conditions. The analysis is based on NJDOT data and focuses on more than 1300 bridges with varying ages, painting ages, and environmental classifications. Furthermore, the paper conducts a structural reliability analysis by formulating expressions for the load-carrying capacity of the moment, identifying the loads on the bridge components, and proposing a correlation between rating and the reliability index. The primary outcome of this study is a comprehensive reliability framework that simulates the rating progression and structural performance of bridge components. The results demonstrate the impact of the bridge's age and maintenance on its ratings and reliability index over time. This framework facilitates maintenance planning, risk assessment, and long-term infrastructure management.</div></div>","PeriodicalId":11763,"journal":{"name":"Engineering Structures","volume":"353 ","pages":"Article 122355"},"PeriodicalIF":6.4,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deterioration of shear performance of reinforced concrete beams with corroded stirrup and longitudinal reinforcements","authors":"Guohua Xing,&nbsp;Xiangyu Li,&nbsp;Junjie Tao,&nbsp;Zhaoqun Chang,&nbsp;Jiao Huang","doi":"10.1016/j.engstruct.2026.122340","DOIUrl":"10.1016/j.engstruct.2026.122340","url":null,"abstract":"<div><div>Corrosion of steel reinforcement is widely recognized as one of the most critical causes of structural deterioration in reinforced concrete (RC) beams, which reduces the load capacity of RC beams and altering their potential failure modes. In this study, an experimental investigation was conducted to examine the shear performance of corroded RC beams. The corrosion-induced crack patterns, failure modes, load-deflection relationship, and strain response of RC beams with stirrups and longitudinal reinforcements subjected to varying degrees of corrosion were analyzed. The test results showed that stirrup corrosion significantly accelerated the formation of diagonal cracks in the shear span region of RC beams, lowering 40.5 % of the cracking loads. As the corrosion in stirrups progressed, the shear strength and ductility of RC beams severely deteriorated, which were reduced by up to 17.7 % and 70.6 %, respectively, thus potentially causing the beams to fail in shear rather than flexure. However, as the degree of corrosion in the longitudinal reinforcement increased from 4 % to 8 %, the tendency toward shear failure shifted back toward flexural failure. In addition, a modified variable angle truss model incorporating the effects of corrosion-induced bond deterioration was developed to predict the load capacity and failure modes of the corroded RC beams. Four types of failure modes including bond failure, flexural failure, diagonal compression failure, and shear compression failure were identified using the proposed model. The predicted results correlated well with the experimental results with a normalized root mean square error (NRMSE) of 2.9 % for ultimate load capacity and an accuracy of 87.4 % for failure mode prediction.</div></div>","PeriodicalId":11763,"journal":{"name":"Engineering Structures","volume":"353 ","pages":"Article 122340"},"PeriodicalIF":6.4,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconstruction of host matrix strain field from distributed fiber optic sensing: Deep learning based approach addressing strain transfer effects","authors":"Xuanyi Lu,&nbsp;Sudao He,&nbsp;Shenghan Zhang","doi":"10.1016/j.engstruct.2026.122181","DOIUrl":"10.1016/j.engstruct.2026.122181","url":null,"abstract":"<div><div>Distributed fiber optic sensing (DFOS) shows great potential for structural health monitoring, as it can provide distributed strains along the sensor. It is well known that the strain measured by optical fibers differs from the host matrix strain due to the strain transfer effect. However, reconstructing the host matrix strain field from DFOS measurements remains a significant challenge. This paper investigates key factors (strain discontinuity, varying gradient, bond length) governing the discrepancy between true host matrix strain and DFOS measured strain. To accurately reconstruct host matrix strain fields from DFOS measured strain, a deep learning-based StrainNet model is proposed. The StrainNet model can achieve strain inversion for various types of optical fiber cables, and is applicable for both linear and nonlinear scenarios. The generalization ability of the StrainNet model is validated through two types of experiments: a uniaxial tensile test of carbon fiber reinforced polymer (CFRP) specimen, and a set of bending tests on aluminum cantilever beams with varying cross-sections. This study provides both insights into key factors influencing DFOS measured strain sensing and a technical tool for using DFOS to accurately measure strain fields in engineering structures.</div></div>","PeriodicalId":11763,"journal":{"name":"Engineering Structures","volume":"353 ","pages":"Article 122181"},"PeriodicalIF":6.4,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A two-stage deep learning model for fracture location and fatigue life prediction of corroded cable steel wires","authors":"Zhenwen Liu ,&nbsp;Xuan Kong ,&nbsp;Ignasi Fernandez ,&nbsp;Lu Deng","doi":"10.1016/j.engstruct.2026.122238","DOIUrl":"10.1016/j.engstruct.2026.122238","url":null,"abstract":"<div><div>Corrosion will accelerate the degradation of materials in cables and thereby compromise the long-term serviceability of bridges. Most existing studies consider the deepest corrosion pit as both the crack initiation point and the fracture location, and employ a single corrosion parameter, such as maximum/average corrosion depth or total corrosion area, for fatigue life prediction. However, these methods exhibit inherent limitations in capturing the complex interactions between the corrosion distribution, fatigue load, and material properties. Therefore, this study proposes a two-stage deep learning model for comprehensive fatigue performance evaluation of corroded steel wires. In the first stage, the 3D scanning technique is used to obtain surface morphological images, and the training dataset is constructed by combining the finite element simulation data. The Fracture Prediction Pix2Pix (FP-Pix2Pix) model is then developed to predict fracture locations. In the second stage, the physics-informed neural network (PINN) is adopted by integrating prior information, including corrosion, fatigue load, material properties, and fracture locations predicted in the first stage, to predict the residual fatigue life (RFL). Experimental results show that the proposed two-stage model outperforms existing models in both fracture location prediction and RFL prediction with errors less than 5 %. The proposed method provides a robust and efficient approach for evaluating the fatigue performance of corroded steel wires in bridge cables.</div></div>","PeriodicalId":11763,"journal":{"name":"Engineering Structures","volume":"353 ","pages":"Article 122238"},"PeriodicalIF":6.4,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stack-AttenLSTM: A surrogate deep learning model for sequential earthquake-flood structural response assessment of steel buildings","authors":"Delbaz Samadian,&nbsp;Annalisa Occhipinti,&nbsp;Imrose B. Muhit,&nbsp;Nashwan Dawood","doi":"10.1016/j.engstruct.2026.122345","DOIUrl":"10.1016/j.engstruct.2026.122345","url":null,"abstract":"<div><div>Accurately assessing the vulnerability of critical building portfolios is fundamental for regional risk management and decision support, especially in regions facing sequential earthquake–flood events exacerbated by climate change. Such compound disasters pose severe environmental challenges, yet current practice lacks reliable surrogate models to rapidly predict structural response and damage-relevant demand parameters under combined seismic and flood loading. This study addresses that gap by introducing a soft computing approach, the Stacked Attention-based Long Short-Term Memory network (Stack-AttenLSTM), to efficiently predict key structural response quantities under sequential earthquake–flood hazards. In this framework, structural vulnerability is interpreted in a performance-based sense, whereby hazard-induced response metrics serve as proxies for damage susceptibility rather than direct loss estimation. The surrogate model predicts key engineering demand parameters (EDPs), including the maximum inter-storey drift ratio (MIDR), maximum floor acceleration (MFA), and maximum base shear (MBS), which are widely used indicators of structural damage and vulnerability. To develop this model, a large-scale meta-database comprising 30,000 steel special moment-resisting frame (SMRF) buildings is first generated to capture structural variability across low-, mid-, and high-rise typologies, from which a representative subset is selected for detailed high-fidelity three-dimensional (3D) nonlinear time-history analyses (NLTHA) under sequential earthquake–flood loading and surrogate model training. Flood loading is represented using computational fluid dynamics (CFD) simulations with a dam-break–type inflow condition, employed as a conservative hydrodynamic proxy to study flow-induced forces under extreme inundation scenarios. Multiple Stack-AttenLSTM architectures are trained and evaluated, and the final model is selected for its optimal balance of predictive accuracy and computational efficiency, enabling rapid yet reliable response prediction. The proposed model achieves high predictive accuracy, with coefficients of determination (R²) approaching 0.88 and low error metrics across all hazard scenarios, demonstrating its effectiveness for rapid multi-hazard vulnerability assessment. Although explicit fragility or loss models are not derived, the Stack-AttenLSTM framework is suitable for integration with early warning systems and digital twin platforms, enabling real-time monitoring, improved uncertainty management, and proactive disaster response.</div></div>","PeriodicalId":11763,"journal":{"name":"Engineering Structures","volume":"353 ","pages":"Article 122345"},"PeriodicalIF":6.4,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146154150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seismic performance of trussed concrete-filled steel tubular (CFST) hybrid structures: Experiments","authors":"Zhan-Shuo Liang ,&nbsp;Lin-Hai Han ,&nbsp;Ming Xiao","doi":"10.1016/j.engstruct.2026.122234","DOIUrl":"10.1016/j.engstruct.2026.122234","url":null,"abstract":"<div><div>To investigate the seismic performance of trussed concrete-filled steel tubular (CFST) hybrid structures, a cyclic loading test program was conducted on 18 specimens to examine the influences of key parameters on hysteretic behavior. The effects of cross-sectional shape (circular and square), web types (KT-, T-, K-, and Y-joint), web-to-chord diameter ratio (<em>d</em>/<em>D</em> = 0.39 and 0.47), shear span ratio (<em>m</em> = 2.17 and 3.31), chord steel yield strength (<em>f</em>_y = 431 and 562 N/mm² for circular specimens; <em>f</em>_y = 388 and 513 N/mm² for square specimens), and axial compression ratio (<em>n</em> = 0–0.6) were studied. Based on thorough recording and analysis of the failure processes, hysteretic curves, and strain distributions, typical failure modes and hysteretic characteristics of these structures were clarified, the effects of key parameters on the skeleton curve, ductility, energy dissipation capacity, strength degradation, and stiffness degradation were elucidated, further the hybrid effects of the structure were revealed. Moreover, comparative tests with the trussed hollow steel tubular specimen C2M-a2a3-s indicate that the peak load (<em>P</em>_m), elastic stiffness (<em>K</em>), ductility factor (<em>μ</em>), and total cumulative energy dissipation (<em>E</em>_ta) of the trussed CFST hybrid specimen C2M-a2a3 are increased by 54.5 %, 47.4 %, 27.9 %, and 88.5 %, respectively.</div></div>","PeriodicalId":11763,"journal":{"name":"Engineering Structures","volume":"353 ","pages":"Article 122234"},"PeriodicalIF":6.4,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146185396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}