Journal of Infrastructure Intelligence and Resilience最新文献

Partial transfer matrix-based group sparse regularisation for impact force localization and reconstruction 基于部分传递矩阵的群稀疏正则化求解冲击力局部化与重构

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-09-01 DOI: 10.1016/j.iintel.2025.100170

Bing Zhang, Xinqun Zhu, Zihao He, Jianchun Li

{"title":"Partial transfer matrix-based group sparse regularisation for impact force localization and reconstruction","authors":"Bing Zhang, Xinqun Zhu, Zihao He, Jianchun Li","doi":"10.1016/j.iintel.2025.100170","DOIUrl":"10.1016/j.iintel.2025.100170","url":null,"abstract":"<div><div>Existing methods for impact force identification are based on full transfer matrix. Constructing and using transfer matrices can be computationally intensive, especially for large-scale complex structures in practice. Partial transfer matrix refers to a subset of the full transfer matrix, potentially reducing computational cost and complexity. In this paper, a partial transfer matrix-based group sparse regularisation method is proposed for the impact force localization and reconstruction. Its robustness and adaptivity with respect to different subsets of full transfer matrix, noise level and number of impact forces are numerically studied using impact forces on a simply supported beam. The number of sensors for impact force identification can be significantly reduced by the proposed method and its localization and time history reconstruction can be determined even with one single sensor configuration. A 10 m long steel-concrete composite bridge model is built in the laboratory. The effectiveness of the proposed method for impact force identification is validated and compared with <em>L</em><sub>1</sub>-norm and <em>L</em><sub>2</sub>-norm regularisation methods numerically and experimentally. Results show that the proposed partial transfer matrix-based group sparse regularisation method has good robustness and identification accuracy and has better performance on the impact force localization and time history reconstruction comparing with <em>L</em><sub>1</sub>-norm and <em>L</em><sub>2</sub>-norm regularisation methods.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 3","pages":"Article 100170"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988669","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

Data normalization for the continuous monitoring of a steel truss bridge: A case study from the Italian railway line 钢桁架桥梁连续监测数据归一化：以意大利铁路线为例

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-09-01 DOI: 10.1016/j.iintel.2025.100171

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

{"title":"Data normalization for the continuous monitoring of a steel truss bridge: A case study from the Italian railway line","authors":"Antonio Argentino, Luca Radicioni, Francesco Morgan Bono, Lorenzo Bernardini, Lorenzo Benedetti, Gabriele Cazzulani, Claudio Somaschini, Marco Belloli","doi":"10.1016/j.iintel.2025.100171","DOIUrl":"10.1016/j.iintel.2025.100171","url":null,"abstract":"<div><div>Structural health monitoring is recognized as a powerful tool to assist bridge management. Continuous long-term monitoring of bridge structures presents several challenges, including the need for effective system design, robust sensors deployment, efficient data management, and comprehensive data analysis and interpretation. In the field of operational modal analysis, automatic tracking of bridge frequencies over time has been shown to be significantly influenced by temperature fluctuations. This effect is also observed in low-frequency sampled signals. To address these issues, the authors present a double-step procedure to effectively mitigate the influence of temperature on the estimated modal parameters and raw signals from displacement, strain and rotation transducers. The procedure is based on multiple linear regression, taking the measured temperatures as inputs, followed by low-pass filtering operations applied to the residuals through moving averages, leading to the creation of minimum detectable anomaly curves. The latter allow to establish quantitative relationships between filtering window lengths and detectable damage thresholds at specified confidence levels. The case study involves a railway steel truss bridge, where more than a year of data was collected through a permanent monitoring system. The monitoring layout includes a variety of sensors deployed to measure the structural response, as well as environmental and operational variables. A 15-month dataset demonstrates how temperature compensation effectively reduces signal variability, which is crucial for enhancing early-stage anomalies detection.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 3","pages":"Article 100171"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145104594","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

A combined approach for detection and localization of subsurface pipe leaks using ground microphone and GPR 一种利用地传声器和探地雷达联合探测和定位地下管道泄漏的方法

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-08-12 DOI: 10.1016/j.iintel.2025.100169

Zhihao Lin , Hai Liu , Xin Deng , Xu Meng , Jie Cui , Erol Tutumluer

{"title":"A combined approach for detection and localization of subsurface pipe leaks using ground microphone and GPR","authors":"Zhihao Lin , Hai Liu , Xin Deng , Xu Meng , Jie Cui , Erol Tutumluer","doi":"10.1016/j.iintel.2025.100169","DOIUrl":"10.1016/j.iintel.2025.100169","url":null,"abstract":"<div><div>Leaks in underground water pipelines not only lead to water wastage but can also cause serious public safety issues such as road collapse in urban areas. To address the low efficiency and accuracy of traditional pipeline leak monitoring and detection methods, this paper proposes a combined approach for detection and localization of subsurface pipeline leaks using ground microphones and ground penetrating radar (GPR). In the ground microphone method, loudness units referenced to digital full scale is used to identify leaky range along a pipeline. A GPR investigation is performed in the suspected area to detect the water-rich area and localize the leakage point. According to the depth and leakage volume of the pipeline, two strategies using the two methods are proposed for efficient and accurate detection and localization of buried pipeline leaks. Two case studies are conducted to validate the proposed approaches. The results indicate that the combined approach effectively leverages the advantages of ground microphones and GPR, enabling efficient and accurate detection and localization of buried pipeline leaks.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 4","pages":"Article 100169"},"PeriodicalIF":0.0,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144924962","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

Laser rangefinder and vision-based 3D structural displacement monitoring 基于激光测距和视觉的三维结构位移监测

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-07-21 DOI: 10.1016/j.iintel.2025.100158

Yihong Ou , Liang Su , Lujia Zhu , Qianli Ma , Haijian He , Fengwei Wu

引用次数: 0

AI-powered automatic design of fire sprinkler layout for random building floorplans 人工智能自动设计随机建筑平面图的消防喷头布局

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-07-21 DOI: 10.1016/j.iintel.2025.100167

Yanfu Zeng , Xinyi Liu , Yifei Ding , Zhe Zheng , Tianhang Zhang , Xinyan Huang , Xinzheng Lu

引用次数: 0

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