Journal of Infrastructure Intelligence and Resilience最新文献

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

A survey of generative models for image-based structural health monitoring in civil infrastructure 基于图像的民用基础设施结构健康监测生成模型综述

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-01-10 DOI: 10.1016/j.iintel.2025.100138

Gi-Hun Gwon, Hyung-Jo Jung

{"title":"A survey of generative models for image-based structural health monitoring in civil infrastructure","authors":"Gi-Hun Gwon, Hyung-Jo Jung","doi":"10.1016/j.iintel.2025.100138","DOIUrl":"10.1016/j.iintel.2025.100138","url":null,"abstract":"<div><div>Accurately assessing and monitoring the condition of structures is essential for ensuring the safety and integrity of civil infrastructure. Over the past decade, image-based structural health monitoring technologies have emerged as powerful tools to enhance efficiency and improve the objectivity of structural evaluations. The integration of deep learning technologies with these monitoring systems has significantly improved the efficiency and reliability of structural condition diagnostics. Of particular interest are specifically Variational Autoencoders, Generative Adversarial Networks, and Diffusion Models, which have gained increasing attention due to their versatility in data generation and ability to address fundamental challenges in structural monitoring. While image-based structural health monitoring encompasses both damage detection and structural response measurements, this review primarily focuses on local-level monitoring applications such as damage detection, where generative models have demonstrated particular effectiveness in addressing challenges like limited data availability and environmental variations. This paper provides a comprehensive analysis of these generative models, examining their underlying concepts, mechanisms, and applications in image-based structural health monitoring. Key applications are reviewed, including structural damage detection, data augmentation for training, and emerging areas such as image quality enhancement and domain generalization. Our analysis presents the current state of generative models in structural monitoring, identifying critical challenges and promising future research directions. This systematic review serves as a foundational resource for researchers and practitioners in the field, offering insights into current achievements and potential advancements.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 2","pages":"Article 100138"},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776268","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

Physics-trained artificial intelligence framework to detect chloride induced degradation in concrete 物理训练的人工智能框架，以检测氯化物引起的混凝土降解

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-01-10 DOI: 10.1016/j.iintel.2025.100139

Parth Patel , Abhinav Gupta , Saran Srikanth Bodda , Harleen Kaur Sandhu

{"title":"Physics-trained artificial intelligence framework to detect chloride induced degradation in concrete","authors":"Parth Patel , Abhinav Gupta , Saran Srikanth Bodda , Harleen Kaur Sandhu","doi":"10.1016/j.iintel.2025.100139","DOIUrl":"10.1016/j.iintel.2025.100139","url":null,"abstract":"<div><div>Numerous critical infrastructures in the United States, including bridges, dams, and nuclear plants, are aging and prone to concrete degradation, compromising their performance and structural integrity. One of the leading causes of degradation is chloride-induced corrosion, where chloride ions diffuse into the concrete, leading to reinforcement corrosion, spalling, and cracking. Detecting chloride degradation at an early stage is crucial for ensuring the safety of these vital structures. However, the visible signs of degradation, such as spalling and cracking, often appear only after significant damage has occurred. Degradation occurs gradually over many years, making it impractical to collect real-time non-destructive testing (NDT) data over extended periods while allowing the structure to continue deteriorating. To overcome this challenge, an integrated structural health monitoring framework is proposed that combines advanced finite element modeling, sensor data, and deep learning techniques. This framework follows a multi-step approach to simulate chloride degradation over the service life of the structure. Subsequently, finite element analyses are performed to numerically simulate non-destructive testing at various stages of degradation to generate corresponding sensor data. By leveraging these simulated data and insights, a physics-driven artificial intelligence framework is developed. The proposed framework offers a state-of-the-art solution to mitigate the challenges associated with long-term degradation monitoring by utilizing high-fidelity simulations and data-driven techniques to achieve detection of chloride-induced concrete damage.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 2","pages":"Article 100139"},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563491","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

Bayesian mixture of factor analyzers for structural damage detection under varying environmental conditions 基于贝叶斯混合因子的结构损伤检测

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-01-06 DOI: 10.1016/j.iintel.2025.100140

Binbin Li , Yulong Zhang , Zihan Liao , Zhilin Xue

{"title":"Bayesian mixture of factor analyzers for structural damage detection under varying environmental conditions","authors":"Binbin Li , Yulong Zhang , Zihan Liao , Zhilin Xue","doi":"10.1016/j.iintel.2025.100140","DOIUrl":"10.1016/j.iintel.2025.100140","url":null,"abstract":"<div><div>Variations of structural dynamic parameters (e.g., frequencies and damping ratios) can be caused by potential structural damages and environmental effects (e.g., temperature, humidity). It is of critical importance to distinguish them for a reliable vibration-based damage detection. A variational Bayesian mixture of factor analyzers (VB-MFA) is proposed in this paper for the probabilistic modeling of measured natural frequencies. It contains multiple factor analyzers to accommodate the nonlinear effect of environmental factors on the natural frequencies. The variational Bayes with automatic relevance determination prior empowers it to automatically determine the number of analyzers and the dimension of latent factors in each analyzer. In addition, the predictive marginal likelihood of natural frequencies is proposed as a damage index, which naturally considers the uncertainties in latent factors and estimated parameters. The method is verified in two case studies: a laboratory eight-story shear-type building model and the Z24-Bridge, both subjected to temperature variations. It shows that better performance has been achieved comparing to the conventional factor analysis and mixture of factor analyzers. The VB-MFA is capable to model the nonlinear effect of environmental effect on natural frequencies, and improves the accuracy of vibration-based structural damage detection.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 2","pages":"Article 100140"},"PeriodicalIF":0.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552763","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