Journal of Infrastructure Intelligence and Resilience最新文献

Cascading failures in urban infrastructure systems: A comprehensive review of disaster chain mechanisms 城市基础设施系统的级联故障：灾害链机制的综合综述

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-06-03 DOI: 10.1016/j.iintel.2025.100157

Zheng Lu , Deyu Yan , Huanjun Jiang , Hongjing Xue , Zhao-Dong Xu

{"title":"Cascading failures in urban infrastructure systems: A comprehensive review of disaster chain mechanisms","authors":"Zheng Lu , Deyu Yan , Huanjun Jiang , Hongjing Xue , Zhao-Dong Xu","doi":"10.1016/j.iintel.2025.100157","DOIUrl":"10.1016/j.iintel.2025.100157","url":null,"abstract":"<div><div>Urban engineering systems (UESs) are highly interconnected, forming complex dependencies that render them vulnerable to cascading failures during disasters. While existing studies have explored specific aspects of disaster chains in UESs, a synthesized framework for understanding their interdependencies, data acquisition challenges, and methodological limitations remains underdeveloped. This paper addresses this gap by conducting a systematic review of UES disaster chains, beginning with the definitions of disaster chains from different academic perspectives, common types of urban disaster chains, namely earthquake, flood, fire, freezing and ground subsidence disaster chains, as well as the interdependency of UES. Furthermore, three identification methods of disaster chains are summarized, namely based on historical disaster data, expert experience, and natural language processing (NLP). Moreover, five analysis methods of disaster chains are summarized, including those based on Bayesian networks, complex networks, numerical simulation, scenario simulation and remote sensing, with comparison of their applicability, advantages, limitations and complexity. The benefits and drawbacks of each approach are clearly illustrated. The paper concludes by discussing the limitations in the current literature and suggests that future research may utilize new technologies to facilitate data analyzing process, conduct cross-regional studies, and focus on integrating socio-economic factors for disaster-related decision-making support.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 3","pages":"Article 100157"},"PeriodicalIF":0.0,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330377","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}

引用次数: 0

Corrigendum to “Fractal-based numerical simulation of multivariate typhoon wind speeds utilizing weierstrass mandelbrot function” [J. Infrastruct. Intell. Resilience, 2 (2025) 100135] “利用weierstrass mandelbrot函数的多变量台风风速分形数值模拟”的勘误[J]。Infrastruct。智能。弹性，2 (2025)100135]

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-06-01 DOI: 10.1016/j.iintel.2025.100156

Kang Cai , Mingfeng Huang , Qiang Li , Qing Wang , Yi-Qing Ni

引用次数: 0

Strain signal denoising in bridge SHM: A comparative analysis of MODWT and other techniques 桥梁SHM应变信号去噪：MODWT与其它方法的比较分析

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-04-29 DOI: 10.1016/j.iintel.2025.100155

Yun-Xia Xia , Ru-Kai Xu , Yi-Qing Ni , Zu-Quan Jin

{"title":"Strain signal denoising in bridge SHM: A comparative analysis of MODWT and other techniques","authors":"Yun-Xia Xia , Ru-Kai Xu , Yi-Qing Ni , Zu-Quan Jin","doi":"10.1016/j.iintel.2025.100155","DOIUrl":"10.1016/j.iintel.2025.100155","url":null,"abstract":"<div><div>Accurate denoising of strain signals is critical for early damage detection in bridge structural health monitoring (SHM). However, signals denoising methods often struggle with the non-stationary and broadband noise encountered in real-world environments. This study provides the first comprehensive comparison of various denoising techniques specifically tailored for bridge strain signals, emphasizing the maximal overlapping discrete wavelet transform (MODWT) for its capacity to handle complex noise profiles. We rigorously compare MODWT with time-domain (moving average filter, finite impulse response filter, empirical mode decomposition), frequency-domain (bandpass filter, Fourier mode decomposition), and other wavelet-based (discrete wavelet transform) approaches. Uniquely, this study employs three datasets from two distinct bridge types (masonry arch and steel bowstring) and evaluates performance using both expert assessments and quantitative metrics (signal-to-noise ratio, peak signal-to-noise ratio, root mean square error, and correlation coefficient). Our findings demonstrate that MODWT exhibits a distinct advantage in high-intensity white noise environments, a common scenario in real-world bridge monitoring, offering valuable guidance for engineers in selecting appropriate denoising strategies. The results not only validate MODWT as a promising preprocessing technique but also offer critical insights into the limitations of existing methods, paving the way for the development of more adaptive and robust denoising solutions in bridge SHM.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 3","pages":"Article 100155"},"PeriodicalIF":0.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115297","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}

引用次数: 0

Bridge post-disaster rapid inspection using 3D point cloud: a case study on vehicle-bridge collision 基于三维点云的桥梁灾后快速检测——以车桥碰撞为例

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-04-01 DOI: 10.1016/j.iintel.2025.100153

Tianyu Ma , Yanjie Zhu , Wen Xiong , Beiyang Zhang , Kaiwen Hu

{"title":"Bridge post-disaster rapid inspection using 3D point cloud: a case study on vehicle-bridge collision","authors":"Tianyu Ma , Yanjie Zhu , Wen Xiong , Beiyang Zhang , Kaiwen Hu","doi":"10.1016/j.iintel.2025.100153","DOIUrl":"10.1016/j.iintel.2025.100153","url":null,"abstract":"<div><div>With the increase in traffic volume, vehicle-bridge collision accidents have been more frequent, creating significant threats to the safe operation of bridges. In the face of sudden vehicle collision accidents, bridge management agencies urgently require fast and accurate damage inspection methods to assess the service performance of the damaged bridge and provide support for post-disaster recovery. However, the service performance of a bridge is related to its overall structure and localized damage morphology. It is challenging for traditional measurement methods to obtain the three-dimensional (3D) morphology of the bridge and damaged areas. They can only obtain limited data points, which cannot provide adequate data for bridge damage assessment. Recently developed 3D laser scanning technology has guaranteed an accurate and timely 3D morphology inspection for the damaged bridge. Based on 3D laser scanning technology, this research proposed a post-disaster emergency inspection solution using a vehicle-bridge collision accident as a practical case, which provides a basis for emergency response decisions. This study focused on the rapid acquisition of the bridge digital model, spatial morphology identification of bridge components, and refined assessment of collision damage. The inspecting results revealed anomalies in the elevation of the damaged main girder and main cable, which necessitated urgent reinforcement measures. Additionally, the damaged hanger was found to have exhibited a lateral deflection angle of 17.12°, with a maximum cable clamp damage depth of 33.06 mm, requiring immediate replacement.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 3","pages":"Article 100153"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879314","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}

引用次数: 0

Large multimodal model assisted underground tunnel damage inspection and human-machine interaction 大型多模态模型辅助地下隧道损伤检测和人机交互

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-04-01 DOI: 10.1016/j.iintel.2025.100154

Yanzhi Qi , Zhi Ding , Yaozhi Luo

{"title":"Large multimodal model assisted underground tunnel damage inspection and human-machine interaction","authors":"Yanzhi Qi , Zhi Ding , Yaozhi Luo","doi":"10.1016/j.iintel.2025.100154","DOIUrl":"10.1016/j.iintel.2025.100154","url":null,"abstract":"<div><div>Artificial Intelligence is playing an increasingly important role in tunnel inspection as a core driver of the new generation of engineering. Traditional methods are difficult to directly generate human linguistic information and lack valid messages extracted from different modalities. This paper proposes Damage LMM, a multimodal damage detection model that can handle images or videos as well as text inputs, to realize fast damage identification and human-computer interaction. The visual instruction database is first created from real damage data collected using different visual sensors and captions extracted by a regional convolutional neural network. The basic language model is then fine-tuned into a specialised Damage LMM, which enhances user instructions by integrating virtual prompt injection and system messages. Finally, the enhanced prompts are processed through the tuned multimodal model to generate a detailed visual description of the damage. The performance of the method is evaluated using a real tunnel dataset, and the results show that it has better robustness and accuracy than other models in multimodal data, with an accuracy of 0.93 for the in-domain image data and a contextual correlation of 0.94. The proposed method can effectively identify tunnel defects and realize multimodal user interaction functions with a moderate number of markers and a short delay time, which will greatly help engineers to quickly obtain effective information and assess the degree of damage at the tunnel inspection site.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 3","pages":"Article 100154"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903728","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}

引用次数: 0

Automatic settlement assessment of urban road from 3D terrestrial laser scan data 利用三维地面激光扫描数据自动评估城市道路沉降状况

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-03-01 DOI: 10.1016/j.iintel.2025.100142

Xinchen Zhang , Qian Wang , Hai Fang , Guogang Ying

{"title":"Automatic settlement assessment of urban road from 3D terrestrial laser scan data","authors":"Xinchen Zhang , Qian Wang , Hai Fang , Guogang Ying","doi":"10.1016/j.iintel.2025.100142","DOIUrl":"10.1016/j.iintel.2025.100142","url":null,"abstract":"<div><div>Tunnel construction in urban environments often requires passing beneath existing roads, where excessive soil excavation can lead to road cracking, settlement, or heaving, posing risks to road safety. Traditional road settlement monitoring methods rely on manual measurements, which are time-consuming, labor-intensive, and costly. Some existing approaches also require extensive sensor deployment, complicating installation and maintenance. To address these challenges, this study introduces a LiDAR-based method for efficient and accurate road settlement assessment. The impact of various LiDAR measurement parameters on assessment accuracy and efficiency was analyzed under typical urban road conditions. A comprehensive workflow was developed, incorporating both rough and fine alignment processes. Key steps in the workflow, such as automated identification of matching planes between point clouds, directional alignment, and angle fine-tuning, were automated using advanced algorithms. The proposed method was applied and validated in a region undergoing tunneling works in Singapore. Results demonstrated that the partially automated LiDAR-based approach achieved comparable accuracy to manual point cloud alignment methods while significantly improving efficiency and reducing labor costs. Furthermore, when compared to traditional total station methods, the LiDAR-based technique maintained errors within acceptable limits and enabled broader spatial coverage. Overall, this study highlights the feasibility and potential of LiDAR technology to enhance road settlement monitoring in engineering practice, offering a cost-effective and scalable alternative to traditional methods.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 1","pages":"Article 100142"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687560","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}

引用次数: 0

Automated Operational Modal Analysis of a steel truss railway bridge employing free decay response 采用自由衰减响应对钢桁梁铁路桥进行自动化运行模态分析

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-03-01 DOI: 10.1016/j.iintel.2025.100145

Francesco Morgan Bono, Antonio Argentino, Lorenzo Bernardini, Lorenzo Benedetti, Gabriele Cazzulani, Claudio Somaschini, Marco Belloli

{"title":"Automated Operational Modal Analysis of a steel truss railway bridge employing free decay response","authors":"Francesco Morgan Bono, Antonio Argentino, Lorenzo Bernardini, Lorenzo Benedetti, Gabriele Cazzulani, Claudio Somaschini, Marco Belloli","doi":"10.1016/j.iintel.2025.100145","DOIUrl":"10.1016/j.iintel.2025.100145","url":null,"abstract":"<div><div>The efficiency and resilience of transportation networks depend significantly on the integrity of bridges, which are increasingly threatened by ageing, traffic, and extreme climate events. Traditional visual inspections have notable limitations, necessitating the adoption of more objective methods like Structural Health Monitoring (SHM). This study explores the application of Operational Modal Analysis (OMA) to estimate the modal parameters of railway bridges, specifically using the Covariance-based Stochastic Subspace Identification (SSI-COV) algorithm. The case study involves a steel Warren truss bridge monitored over 20 months. The research demonstrates that SSI-COV, typically requiring stationary random input, can effectively utilise the bridge’s free decay responses following train passages. This approach strongly improves signal-to-noise ratio, which is vice-versa critical for railway bridges ambient vibrations due to the very low input energy, enabling precise modal parameter estimation with shorter time windows and lower-performance sensors. Results were validated against the Peak-Picking (PP) and the Enhanced Frequency Domain Decomposition (EFDD) methods, with SSI-COV identifying three additional natural frequencies and exhibiting lower dispersion in frequency estimates throughout the monitored period. Statistical analysis further indicated that using multiple free decays enhances the accuracy and reduces variability for challenging modes, while dominant modes are reliably estimated with minimal decay data. These findings endorse the combination of SSI-COV and free decays as a robust tool for detailed and long-term bridge monitoring, offering a valuable and potentially low-cost alternative to ambient vibration-based OMA techniques.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 1","pages":"Article 100145"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687561","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}

引用次数: 0

Deep learning in crack detection: A comprehensive scientometric review 裂纹检测中的深度学习：科学计量学综述

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-02-23 DOI: 10.1016/j.iintel.2025.100144

Yingjie Wu , Shaoqi Li , Jingqiu Li , Yanping Yu , Jianchun Li , Yancheng Li

{"title":"Deep learning in crack detection: A comprehensive scientometric review","authors":"Yingjie Wu , Shaoqi Li , Jingqiu Li , Yanping Yu , Jianchun Li , Yancheng Li","doi":"10.1016/j.iintel.2025.100144","DOIUrl":"10.1016/j.iintel.2025.100144","url":null,"abstract":"<div><div>Cracks represent one of the common forms of damage in concrete structures and pavements, leading to safety issues and increased maintenance costs. Therefore, timely crack detection is crucial for preventing further damage and ensuring the safety of these structures. Traditional manual inspection methods are limited by factors such as time consumption, subjectivity, and labor intensity. To address these challenges, deep learning-based crack detection technologies have emerged as promising solutions, demonstrating satisfactory performance and accuracy. However, the field still lacks comprehensive scientometric analyses and critical surveys of existing works, which are vital for identifying research gaps and guiding future studies. This paper conducts a bibliometric and critical analysis of the collected literature, providing novel insights into current research trends and identifying potential areas for future investigation. Analytical tools, including VOSviewer and CiteSpace, were employed for in-depth analysis and visualization. This study identifies key research gaps and proposes future directions, focusing on advancements in model generalization, computational efficiency, dataset standardization, and the practical application of crack detection methods.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 3","pages":"Article 100144"},"PeriodicalIF":0.0,"publicationDate":"2025-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739718","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}

引用次数: 0

Modular approach to model order reduction for offshore wind turbines supported by multi-bucket jacket foundation 多筒导管基础支撑海上风力机模型降阶的模块化方法

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-02-20 DOI: 10.1016/j.iintel.2025.100143

Zhaofeng Shen , Yue Chen , Pengfei Li , Jun Liang , Ying Wang , Jinping Ou

{"title":"Modular approach to model order reduction for offshore wind turbines supported by multi-bucket jacket foundation","authors":"Zhaofeng Shen , Yue Chen , Pengfei Li , Jun Liang , Ying Wang , Jinping Ou","doi":"10.1016/j.iintel.2025.100143","DOIUrl":"10.1016/j.iintel.2025.100143","url":null,"abstract":"<div><div>Offshore wind turbines (OWTs) supported by multi-bucket jacket foundations (MBJF) provide a cost-effective solution for offshore wind energy production when water depth exceeds 50 m. However, numerical simulation of their dynamic behaviors towards high accuracy and efficiency becomes challenging due to the intricate structural configuration. To tackle it, this paper introduces a model order reduction framework for OWTs with MBJF. The framework strategically decomposes the structure into five substructures, whose reduced-order models (ROMs) are individually constructed and then assembled into a ROM for the entire OWT structure with fixed boundary conditions. The parameters of the assembled ROM on soil are subsequently calibrated through a model updating process, to ensure the alignment of modal parameters and structural displacements between ROM and full-order model (FOM). The results show that Young's moduli of both tower and jacket dominate the frequencies of global bending modes while Young's modulus of the blade dominates the frequencies of blade bending modes. Among the support parameters, the combined T-Z soil spring stiffness plays a critical role, affecting the frequencies of global motion and bending modes. The proposed model order reduction framework provides a robust methodology towards accurate and efficient simulation of structural dynamics for OWTs supported by MBJF.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 2","pages":"Article 100143"},"PeriodicalIF":0.0,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681861","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}

引用次数: 0

Positional inaccuracy investigation and innovative connection solution for robotic construction of load carrying structures 承载结构机器人施工位置误差研究及创新连接解决方案

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-01-16 DOI: 10.1016/j.iintel.2025.100141

Cheav Por Chea, Yu Bai, Yihai Fang

{"title":"Positional inaccuracy investigation and innovative connection solution for robotic construction of load carrying structures","authors":"Cheav Por Chea, Yu Bai, Yihai Fang","doi":"10.1016/j.iintel.2025.100141","DOIUrl":"10.1016/j.iintel.2025.100141","url":null,"abstract":"<div><div>Robotic construction of load carrying structures in civil engineering becomes promising with the supports from robotics, computer-vision, and design for manufacturing and assembly. A multi-robot system was developed to demonstrate an automated construction of reciprocal frame structures where mobile robots were used to facilitate the access of robotic arms and a series of programming packages were developed to automate the construction. Furthermore, the AprilTag fiducial marker system was applied as a positioning system to align the mobile robots during construction tasks and to target the structural components. In this context, the key challenges are centred on the understanding of the accuracy and tolerance of the robotic system in positioning and navigation. To this end, experimental methods were developed in this study to understand the observed distances and the accuracy of the positioning system. The optimal observation distance for the positioning system in the robotic system was then determined considering the positional and orientational accuracies of the AprilTag fiducial marker system using a red, green, blue-depth (RGB-D) camera. Moreover, experiments were conducted to study the impact of the barycentre of robotic arms on the precision of the mobile robots and to determine the offset of the mobile robot during the manoeuvre. In consideration of the positional inaccuracies, the magnetic connection approach was creatively implemented using their inherent self-aligning property. The corresponding effective range was also firstly determined, within which the structural components could be installed successfully.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 2","pages":"Article 100141"},"PeriodicalIF":0.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146657","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}

引用次数: 0