Journal of Infrastructure Intelligence and Resilience最新文献

{"title":"Corrigendum to “Finite element modeling and updating of historic timber structural components using 3D photogrammetry and ultrasound-based computed tomography” [J. Infrastruct. Intell. Resil. 3, (2026), 100227]","authors":"Zeyi Liu, Dongdong Chen, Xuewei Ling, Masaru Kitahara, Yasutaka Narazaki, Hongbing Chen","doi":"10.1016/j.iintel.2026.100237","DOIUrl":"10.1016/j.iintel.2026.100237","url":null,"abstract":"","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"5 3","pages":"Article 100237"},"PeriodicalIF":6.5,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148951481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-assisted fibre optic sensing frameworks for automated monitoring of railway infrastructure","authors":"Mohammad Siahkouhi ,&nbsp;Maria Rashidi ,&nbsp;Won-Hee Kang ,&nbsp;Behruz Bozorg","doi":"10.1016/j.iintel.2026.100214","DOIUrl":"10.1016/j.iintel.2026.100214","url":null,"abstract":"<div><div>This review examines the integration of artificial intelligence (AI) with fibre optic sensing (FOS) for automated structural health monitoring (SHM) of railway infrastructure, encompassing embankments, bridges, tracks, tunnels, and turnouts. Railways are among the most sustainable and strategically critical modes of transportation; however, their distributed nature, increasing axle loads, and harsh environmental conditions present significant monitoring challenges. Distributed fibre optic sensing (DFOS) systems, including fibre Bragg grating (FBG), distributed acoustic sensing (DAS), and Brillouin- and Rayleigh-based scattering sensors, are identified as uniquely capable platforms for real-time, long-range, and high-resolution measurement. Sensor deployment configuration, including gauge length, spatial resolution, and signal-to-noise ratio, critically determines whether a system supports train localisation or enables detection of track component defects such as fastener loosening, rail cracks, and ballast degradation. The convergence of AI with FOS has redefined SHM as a data-driven and predictive discipline. Machine learning algorithms including Q-Learning and Deep Q-Networks (DQN), deep learning architecture such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU), and reinforcement learning (RL)-based frameworks enable automated defect detection, localisation, and maintenance optimisation. Emerging frameworks incorporating Large Language Models (LLMs) and Digital Twin (DT)-integrated RL offer new opportunities for adaptive, closed-loop decision-making in railway maintenance. This review systematically identifies principal deployment challenges, including sensor cross-sensitivity, signal attenuation, massive data volumes from DAS systems, environmental degradation of protective coatings, labour-intensive installation, high interrogator costs, and power constraints at remote trackside locations. Railway-specific resolutions and alternatives are presented for each challenge, grounded in current literature.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"5 2","pages":"Article 100214"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148241032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bending performance of recycled aggregate concrete beams with hybrid tire-derived steel fibers","authors":"Asad Zia ,&nbsp;Ivan Holly ,&nbsp;Ciaran McNally","doi":"10.1016/j.iintel.2026.100211","DOIUrl":"10.1016/j.iintel.2026.100211","url":null,"abstract":"<div><div>This study examines the structural and material performance of hybrid recycled tire steel fibers (HRTFs) in recycled aggregate concrete (RAC), focusing on both plain concrete and reinforced beam applications. A hybrid fiber mix comprising of tire-derived and hook-end industrial steel fibers, in proportions of 60 % and 40 %, was utilized; the dosage rate was 40 kg/m³. Material-level tests assessed strength, elasticity, crack behavior, and shrinkage up to 142 days. Results showed that while the compressive strength of recycled aggregate concrete dropped by 12 %, tensile strength, toughness, and shrinkage resistance improved. Notably, linear shrinkage was reduced by 20 % at 142 days, and crack energy absorption increased significantly. Reinforced concrete (RC) beams were tested in flexure; beams were also designed with 18 % and 36 % less steel reinforcement to evaluate the impact of the HRTF. Beams incorporating HRTF exhibited enhanced ductility, more stable post-cracking behavior, and improved crack control, achieving a 5 % increase in normalized load compared with the reference beams. These findings confirm the potential of HRTFs to enhance the performance of RAC in both structural and durability aspects, supporting their use as a sustainable reinforcement strategy. Further research on dosage optimization and field-scale validation is recommended.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"5 2","pages":"Article 100211"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148183905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CrackMambaNet: A road crack segmentation network based on dual-branch encoder ensemble with adaptive feature fusion","authors":"Shiqiang Qin,&nbsp;Yutao Yang,&nbsp;Mengfan Chen","doi":"10.1016/j.iintel.2025.100194","DOIUrl":"10.1016/j.iintel.2025.100194","url":null,"abstract":"<div><div>Automated crack detection is essential for extending the service life of roads and ensuring the operational safety of road structures. Numerous computer vision-based networks have been proposed for crack segmentation in recent years. However, existing networks frequently struggle to balance global context modeling with local detail preservation and lack sufficient robustness in complex scenarios. To address these issues, this study proposes CrackMambaNet for automated pavement crack detection. It is a U-shaped segmentation network with a dual-branch encoder incorporating an adaptive feature fusion module (FFM). The dual-branch encoder consists of global and local branches. The former stacks visual state space (VSS) blocks to capture multi-scale long-range dependencies with linear complexity. The latter is built upon an enhanced ResNet34 backbone integrated with visual attention convolution modules to reinforce edge-aware and fine-grained feature extraction. An adaptive FFM is thus introduced to suppress redundant features through dual SE-attention. Extensive experiments were conducted on various public crack datasets to validate the performance of CrackMambaNet. The results indicate that CrackMambaNet achieved the best performance across different datasets compared with seven existing networks. The mean intersection over union on the DeepCrack, EdmCrack600, and CFRL datasets reached 87.94 %, 76.33 %, and 75.58 %, respectively. These results demonstrate that CrackMambaNet can balance global context modeling and local detail preservation, exhibiting excellent robustness and accuracy in complex scenarios and fine crack segmentation tasks.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"5 2","pages":"Article 100194"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145852438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Augmented reality-based smart structural health monitoring system with accurate 3D model alignment","authors":"Omar Awadallah ,&nbsp;Katarina Grolinger ,&nbsp;Ayan Sadhu","doi":"10.1016/j.iintel.2025.100186","DOIUrl":"10.1016/j.iintel.2025.100186","url":null,"abstract":"<div><div>Structural Health Monitoring (SHM) has become increasingly critical due to the rapid deterioration of civil infrastructure. Traditional methods involving heavy equipment are costly and time-consuming. Recent SHM approaches use advanced non-contact sensors, internet-of-things (IoT), and Augmented Reality (AR) glasses for faster inspections and immersive experiences during inspections. However, current methods lack quantitative damage data, remote collaboration support, and accurate 3D model alignment with the real structure. Recognizing these current challenges, this paper proposes an AR-based system that integrates Building Information Modelling (BIM) visualization and follows a flexible manipulation approach of 3D holograms to improve structural condition assessments. The proposed framework utilizes the Vuforia software development toolkit to enable the automatic alignment of 3D models to the real structure, ensuring successful model alignment to assist users in accurately visualizing damage locations. The framework also enables flexible manipulation of damage locations, making it easier for users to identify multiple damage points in the 3D models. The proposed system is validated through lab-scale and full-scale bridge use cases, with data transfer performance analyzed under 4G and 5G networks for remote collaboration. This study demonstrates that the proposed AR-based SHM framework successfully aligns 3D models with real structures, allowing users to manually adjust models and damage locations. The experimental results confirm its feasibility for remote collaborative inspections, highlighting significant improvements with 5G networks. Nevertheless, performance under 4G remains acceptable, ensuring reliability even without 5G coverage.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"5 1","pages":"Article 100186"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A resilience analysis framework for airport runways considering offensive-defensive strategies","authors":"Tie Yan ,&nbsp;Yingjun Wang","doi":"10.1016/j.iintel.2025.100183","DOIUrl":"10.1016/j.iintel.2025.100183","url":null,"abstract":"<div><div>The functionality evaluation of military infrastructure under attack determines the validity of operational command decisions in a critical way. In battlefield environments, conventional methodologies have been proven to be insufficient for accurate functionality evaluation due to challenges in considering multi-source uncertainties from adversarial strategies, weapon system variability, and environmental dynamics. To address this gap, an analysis framework for resilience of military infrastructure (AFRMI) was proposed, systematically integrating uncertainties in five phases, i.e. (1) offensive-defensive strategy, (2) weapon engagement, (3) physical damage, (4) functionality loss, and (5) functionality recovery. Focusing on airport runways in Air Defense and Missile Countermeasures scenarios, this study established a probabilistic methodology for quantifying multi-dimensional uncertainties as well as their cross-phase propagation mechanisms. The adversarial interactions between combatants were formulated as <em>a Two-player Zero-sum Markov Game</em>, where Nash Equilibrium strategies under resource constraints were derived through <em>Q-learning</em> reinforcement algorithms. A case study was conducted to demonstrate the application of AFRMI and revealed that it could quantifies the effect of equilibrium strategies to infrastructure functionality, as well as that of phase-coupled uncertainty propagation, overcoming the shortcoming of traditional static damage assessment by incorporating dynamic recovery pathways. The framework will provide a theoretical basis for real-time functionality state prediction under evolving adversarial conditions, and a decision-making paradigm for resilience management of military infrastructures.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"5 1","pages":"Article 100183"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-term monitoring of externally prestressed concrete bridges using semi-closed elasto-magnetic sensors","authors":"Sai Wu","doi":"10.1016/j.iintel.2025.100182","DOIUrl":"10.1016/j.iintel.2025.100182","url":null,"abstract":"<div><div>The effectiveness of external reinforcement significantly influences the durability and safety of bridges. During construction, prestress is typically applied and controlled using hydraulic jacks. However, long-term service conditions may lead to sectional deterioration or wire fractures in external tendons, which often remain undetected due to the absence of effective monitoring techniques. Since non-destructive methods should be applied to obtain the condition of external tendons for long-term monitoring, and considering the limitations of anchor tendon gauges and vibration-based methods, this research implemented a novel study for long-term monitoring and analysis of tendons in externally prestressed concrete bridges during operation. First, semi-closed elasto-magnetic sensors were deployed on two existing highway viaducts without removing the tendons; Second, prestress loss in bridges with different external prestressing configurations were analyzed using finite element method. Third, data collected over a 12-month period following bridge opening were analyzed under actual traffic loads and with consideration of temperature effects. The results indicate that the prestress values measured by the elasto-magnetic sensors correspond closely with theoretical predictions, demonstrating the suitability of this monitoring approach for practical bridge applications. Furthermore, no significant prestress loss was observed during the monitoring period.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"5 1","pages":"Article 100182"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145478853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synergies between coal-gangue activation and LC3 systems: From raw-material characteristics to durability","authors":"Tiantian Xu ,&nbsp;Jin Jiang","doi":"10.1016/j.iintel.2026.100197","DOIUrl":"10.1016/j.iintel.2026.100197","url":null,"abstract":"<div><div>The resource utilization of coal gangue provides substantial environmental benefits by reducing pollution, conserving natural resources, and supporting “dual carbon” goals and sustainable development. Limestone Calcined Clay Cement (LC3) has emerged as a promising low-carbon alternative to ordinary Portland cement, enabling up to 50 % clinker reduction while achieving comparable performance. Activated coal gangue, an abundant industrial solid waste with latent pozzolanic activity, serves as a cost-effective supplementary cementitious material (SCM) that can effectively substitute calcined clay in LC3 systems, simultaneously addressing waste management challenges and sustainable construction demands. However, systematic studies on its activation mechanisms, compatibility with LC3, and long-term durability remain limited. This review synthesizes recent advances in the synergistic incorporation of activated coal gangue into LC3 binders, emphasizing applicability, hydration mechanisms, and performance characteristics. Particular focus is placed on the cooperative formation of C-A-S-H gels and calcium carboaluminate phases, which refine pore structure and improve durability. The influence of various activation methods (thermal, mechanical, chemical, and combined) on reactivity, mechanical properties, and long-term performance is critically evaluated. This work offers theoretical foundations and practical guidance for designing coal gangue-based low-carbon cements, thereby facilitating solid waste valorization and advancing sustainable construction practices.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"5 1","pages":"Article 100197"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experiment-based numerical simulation of chloride-induced corrosion for compression concrete columns in coastal areas","authors":"Ren-jie Wu ,&nbsp;Bo-yang Liu ,&nbsp;Jian Shen ,&nbsp;Jin Xia","doi":"10.1016/j.iintel.2025.100195","DOIUrl":"10.1016/j.iintel.2025.100195","url":null,"abstract":"<div><div>Marine reinforced concrete (RC) structures, such as pier columns, are exposed to the combined effects of chloride ion-induced corrosion and load action, significantly impacting their long-term performance and reliability. However, existing corrosion models typically overlook the role of load action in the electrochemical corrosion process of steel reinforcement. An improved numerical model of steel reinforcement corrosion in marine concrete structures has been established to explore the influence of load action on the corrosion process control and corrosion rate of steel reinforcement in concrete. Based on the existing experimental results, the load influence coefficient is employed to modify the time-dependent surface chloride ion concentration model, and the influence of load action on surface chloride ion concentration is considered. Additionally, the impact of the time-dependent and spatially non-uniform chloride environment on the corrosion behavior of steel reinforcement in concrete compression members is considered in our research, thoroughly examining how loading influences corrosion control and rates in these members. The results indicate variations in the lateral corrosion morphology of longitudinal rebars depending on their azimuthal orientation. The corrosion morphology of stirrups correlates with the circumferential distribution of chloride ion concentrations on the concrete surface. Moreover, axial compressive loads can intensify the corrosion of longitudinal rebars while reducing the corrosion level of stirrups as the axial compression load increases. This model demonstrates the reliability of risk assessment and is expected to enhance the understanding and prediction of the corrosion risk-resistance capability of ocean load-bearing structures in real-world applications.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"5 1","pages":"Article 100195"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pareto-optimal seismic co-design of a novel integrated VibroHarvest system for long-period structures","authors":"Wei Liu ,&nbsp;Koichi Kusunoki ,&nbsp;Jiang Liu","doi":"10.1016/j.iintel.2025.100196","DOIUrl":"10.1016/j.iintel.2025.100196","url":null,"abstract":"<div><div>Long-period structures are susceptible to resonance when subjected to ground motions with dominant frequencies below 1 Hz. This resonance can amplify seismic responses, compromising structural integrity. To enhance seismic resilience and promote energy sustainability, this study introduces an integrated VibroHarvest system (IVHS) that combines structural vibration control with energy harvesting, utilizing a piezoelectric nanogenerator. A multi-objective co-design optimization framework was developed to simultaneously minimize structural displacements and roof-floor accelerations, while maximizing the amount of harvested energy. A matrix-partitioning strategy was integrated into the solution scheme to ensure numerical robustness under strong electromechanical coupling. The dynamic performance of the IVHS was evaluated via time-domain seismic simulations. Additionally, global sensitivity analysis (GSA) was employed to identify the parameters that most significantly influence seismic response and energy-harvesting efficiency. Compared with conventional tuned viscous mass dampers (TVMDs), the IVHS reduces peak isolator displacement and roof-floor acceleration by up to 25.59 % and 15.50 %, respectively. Simultaneously, the system harvests an average power of 0.60 W (0.026–2.66 W under the tested conditions), which is sufficient to enable autonomous post-seismic monitoring and real-time damage evaluation without external power sources. Even the minimum output (∼26 mW) significantly exceeds that of typical piezoelectric harvesters. GSA based on Sobol’s indices reveals that load resistance and electromechanical coupling coefficients influence energy output. Conversely, TVMD damping coefficients and stiffness parameters predominantly influence vibration mitigation. These findings establish a dual-functional design framework for the development of resilient, energy-autonomous civil infrastructure.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"5 1","pages":"Article 100196"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}