Journal of Infrastructure Intelligence and Resilience最新文献_第4页

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 Epub Date: 2025-06-21 DOI: 10.1016/j.iintel.2025.100156

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

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 : 2025-06-01 Epub 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":"2025-06-01","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

Intelligent control of structural vibrations based on deep reinforcement learning 基于深度强化学习的结构振动智能控制

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-06-01 Epub Date: 2024-12-15 DOI: 10.1016/j.iintel.2024.100136

Xuekai Guo, Pengfei Lin, Qiulei Wang, Gang Hu

{"title":"Intelligent control of structural vibrations based on deep reinforcement learning","authors":"Xuekai Guo, Pengfei Lin, Qiulei Wang, Gang Hu","doi":"10.1016/j.iintel.2024.100136","DOIUrl":"10.1016/j.iintel.2024.100136","url":null,"abstract":"<div><div>This paper explores the application of Deep Reinforcement Learning (DRL) in structural vibration control, aiming to achieve effective control of the dynamic response of building structures during natural disasters such as earthquakes. A DRL-based control strategy is proposed, and dynamic interaction between the OpenSees environment and the deep reinforcement learning environment is realized. By adjusting the parameters in the reward function, the control preference of the DRL algorithm for different metrics can be effectively modified. Additionally, an intelligent structural vibration control platform based on DRL has been developed to simplify the design process of DRL algorithms. Case studies conducted on the platform demonstrate that DRL can effectively suppress structural responses in both single-layer and multi-layer complex structures. Meanwhile, comparisons with PID and LQR algorithms that are based on linear analysis design, reveal the stability advantages of DRL in handling structural dynamic responses characterized by high nonlinearity, time delay, and large actuator output intervals.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 2","pages":"Article 100136"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146656","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 : 2025-06-01 Epub 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":"2025-06-01","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 : 2025-06-01 Epub 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":"2025-06-01","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

Automatic PAUT crack detection and depth identification framework based on inspection robot and deep learning method 基于检测机器人和深度学习方法的 PAUT 裂纹自动检测和深度识别框架

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-03-01 Epub Date: 2024-08-30 DOI: 10.1016/j.iintel.2024.100113

Fei Hu , Hong-ye Gou , Hao-zhe Yang , Huan Yan , Yi-qing Ni , You-wu Wang

{"title":"Automatic PAUT crack detection and depth identification framework based on inspection robot and deep learning method","authors":"Fei Hu , Hong-ye Gou , Hao-zhe Yang , Huan Yan , Yi-qing Ni , You-wu Wang","doi":"10.1016/j.iintel.2024.100113","DOIUrl":"10.1016/j.iintel.2024.100113","url":null,"abstract":"<div><div>Orthotropic steel bridge decks (OSD) are widely acclaimed for their lightweight, high load-carrying capacity, and adaptability, making them a popular choice in steel structure bridges. However, the complex nature of their structure makes them susceptible to fatigue cracking, posing significant safety concerns. To address the issues above, this study employs a robot equipped with an ultrasonic phased array probe to automate the detection of internal cracks within Orthotropic Steel Decks (OSD). A Deep Convolutional Generative Adversarial Network (DCGAN) is utilized to augment the training dataset of Phased Array Ultrasonic Testing (PAUT) images. The YOLO series algorithms are applied and compared for crack localization, with YOLO v7-tiny exhibiting the highest accuracy and speed. Integrating attention mechanisms into the YOLO v7-tiny algorithm to facilliate rapid and high-precision crack detection. Analyzing the echo region with an echo intensity bar enabled the identification of crack depth, with an identification error within 5%.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 1","pages":"Article 100113"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535895","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

An integrated management system (IMS) approach to sustainable construction development and management 可持续建筑开发和管理的综合管理系统（IMS）方法

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-03-01 Epub Date: 2024-10-18 DOI: 10.1016/j.iintel.2024.100126

Ahsan Waqar , Saad Nisar , Muhammad Muddassir , Omrane Benjeddou

{"title":"An integrated management system (IMS) approach to sustainable construction development and management","authors":"Ahsan Waqar , Saad Nisar , Muhammad Muddassir , Omrane Benjeddou","doi":"10.1016/j.iintel.2024.100126","DOIUrl":"10.1016/j.iintel.2024.100126","url":null,"abstract":"<div><div>Construction is significantly contributing to the severe environmental crisis it is facing. The sector consumes over 3 billion tons of raw materials annually, and its activities account for 40% of global CO<sub>2</sub> emissions. Traditional integrated strategies toward fragmented sustainability cannot offer total optimization. In this respect, the present research presents an integrated management system (IMS) containing a composite of metrics for sustainable construction management (SCM). This research was specifically geared to test the relationship between the elements of IMS and SCM from the perspective of the construction industry. A quantitative survey tested through 119 professionals was used for data collection. It is established through structural equation modeling (SEM) that the internal consistency of Cronbach’s Alpha 0.72–0.95 and construct validity was strong. The Fornell-Larcker criterion was realized to affirm good discriminant validity. Crucial results identified the presence of significant impacts for quality management (QM) (β = 0.643, <em>p</em> < 0.001), risk management (RM) (β = 0.53, <em>p</em> < 0.001), and safety management (SM) (β = 0.439, <em>p</em> < 0.001). Therefore, this study further enhances the scalability of IMS so that it is practically applied to improve project quality and safety, along with risk management. Future research could also focus on studying the context of the integration of IMS with SCM and continue to work using objective performance measures to validate these findings.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 1","pages":"Article 100126"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142555002","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

Review on optimization strategies of probabilistic diagnostic imaging methods 概率成像诊断方法的优化策略综述

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-03-01 Epub Date: 2024-10-22 DOI: 10.1016/j.iintel.2024.100127

Ning Li , Anningjing Li , Jiangfeng Sun

{"title":"Review on optimization strategies of probabilistic diagnostic imaging methods","authors":"Ning Li , Anningjing Li , Jiangfeng Sun","doi":"10.1016/j.iintel.2024.100127","DOIUrl":"10.1016/j.iintel.2024.100127","url":null,"abstract":"<div><div>With the continuous development of intelligent infrastructure, structural health monitoring (SHM) and non-destructive testing (NDT) have become major research focuses. Ultrasonic-guided wave imaging technology not only integrates the global impact of damage on structures but also provides intuitive localization and severity characterization of the damage. Probabilistic diagnostic imaging (PDI) methods, which do not require direct interpretation of guided wave signals and can achieve high-quality imaging with sparse arrays, have garnered increasing attention. This paper introduces the principles, general processes, and technical advantages of PDI methods. Based on the process of the PDI, existing optimization strategies are categorized into two types: internal process optimizations, which include sensor layout, damage indices optimization, construction of the distribution weight function, and data fusion; and external process optimizations, which include spurious image suppression, on-site environment detection, and integration of methodologies, each analyzed in detail. With the affirmation of the value of these strategies, this paper also highlights the current issues within these methods and explores potential future developments by integrating emerging technologies such as intelligent sensing, big data, and artificial intelligence. These insights provide valuable reference suggestions for the continued optimization of these methods.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 1","pages":"Article 100127"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586147","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 review of artificial intelligence in dam engineering 人工智能在大坝工程中的研究进展

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-03-01 Epub Date: 2024-09-02 DOI: 10.1016/j.iintel.2024.100122

Wenxuan Cao , Xinbin Wu , Junjie Li , Fei Kang

引用次数: 0

A new method for predicting PM2.5 concentrations in subway stations based on a multiscale adaptive noise reduction transformer -BiGRU model and an error correction method 基于多尺度自适应降噪变压器-BiGRU模型和误差修正方法的地铁站点PM2.5浓度预测新方法

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-03-01 Epub Date: 2024-11-17 DOI: 10.1016/j.iintel.2024.100128

Dingyu Chen, Hui Liu

{"title":"A new method for predicting PM2.5 concentrations in subway stations based on a multiscale adaptive noise reduction transformer -BiGRU model and an error correction method","authors":"Dingyu Chen, Hui Liu","doi":"10.1016/j.iintel.2024.100128","DOIUrl":"10.1016/j.iintel.2024.100128","url":null,"abstract":"<div><div>PM2.5 is a significant contributor to air pollution, with a notable impact on human health. Subway stations, with their high pedestrian traffic, present a particular challenge in this regard. By monitoring PM2.5 levels, subway managers can take prompt action, such as optimizing the operation of air purification equipment in stations, to enhance air quality within stations and thereby enhance the passenger experience. This paper proposes an enhanced Transformer-BiGRU prediction model, which incorporates a MSHAM(Multiscale Hybrid Attention Mechanism)comprising a multi-scale convolutional attention mechanism and a VMD decomposition self-attention mechanism. Additionally, a ANR(Adaptive Noise Reduction) module has been integrated into the model to facilitate noise reduction. Finally, the prediction is performed by BiGRU. The resulting error sequence is predicted by BiGRU and the predicted sequence is corrected. In this paper, a dataset of pollutants from Seoul subway stations in South Korea is used to compare with the base model. The model presented in this paper achieves the highest accuracy.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 1","pages":"Article 100128"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130220","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