Journal of Infrastructure Intelligence and Resilience最新文献_第6页

Improvement of burst capacity model for pipelines containing surface cracks and its implication for reliability analysis 含表面裂纹管道爆破能力模型的改进及其对可靠性分析的意义

Journal of Infrastructure Intelligence and Resilience Pub Date : 2023-09-01 DOI: 10.1016/j.iintel.2023.100043

Haotian Sun, Wenxing Zhou

{"title":"Improvement of burst capacity model for pipelines containing surface cracks and its implication for reliability analysis","authors":"Haotian Sun, Wenxing Zhou","doi":"10.1016/j.iintel.2023.100043","DOIUrl":"https://doi.org/10.1016/j.iintel.2023.100043","url":null,"abstract":"<div><p>This paper presents the improvement of a widely used burst capacity model for steel oil and gas pipelines that contain longitudinal external surface cracks, namely the CorLAS model, through the addition of a correction factor that is quantified by the Gaussian process regression (GPR). The correction factor is assumed to depend on four non-dimensional input features that characterize both the crack geometry and pipe material properties. A database consisting of 212 full-scale burst tests of pipe specimens that contain longitudinal surface cracks is established based on the open literature, which is employed to train the GPR model and evaluate its performance. It is shown that GPR is highly effective in improving the accuracy of the CorLAS model predictions. The improvement is further shown to have a marked effect on the time-dependent probability of burst of pipelines containing growing surface cracks through two hypothetical pipeline examples: when employing the CorLAS model, the probabilities of burst are significantly higher, exceeding those obtained using the improved CorLAS model by more than one order of magnitude.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"2 3","pages":"Article 100043"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49879401","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

Visualization of structural health monitoring information using Internet-of-Things integrated with building information modeling 物联网与建筑信息建模相结合的结构健康监测信息可视化

Journal of Infrastructure Intelligence and Resilience Pub Date : 2023-09-01 DOI: 10.1016/j.iintel.2023.100053

Micheal Sakr, Ayan Sadhu

{"title":"Visualization of structural health monitoring information using Internet-of-Things integrated with building information modeling","authors":"Micheal Sakr, Ayan Sadhu","doi":"10.1016/j.iintel.2023.100053","DOIUrl":"https://doi.org/10.1016/j.iintel.2023.100053","url":null,"abstract":"<div><p>Structural Health Monitoring (SHM) has become a paramount necessity in civil engineering for improving the operational performance of aging infrastructure. Recent monitoring techniques have utilized emerging technologies in Industry 4.0, such as the Internet of Things, Big Data analytics, cloud computing, and cybersecurity, to automate SHM methodologies. However, they have found challenges in linking these technologies and developing an autonomous, well-established digital framework for applications of SHM. Visualizing processed SHM data in a real-time digital interface generates multiple obstacles, such as witnessing delays in data transfer and resorting to offline tools for manual data processing. This paper, therefore, explores the integration of Building Information Modeling (BIM) and the Internet of Things (IoT) through an Arduino micro-processing unit for tracking and visualizing the data from the time and frequency domains. Strategies for enabling data monitoring and processing are developed while continuously acquiring structural responses. The query of data is established in a web-based database instead of storing the data in offline resources that await manual intervention. The proposed real-time SHM methodology is validated experimentally using two practical applications: a dynamically moving vehicle over a simply-supported bridge prototype and a randomly excited three-story model with real-time visualization of both time- and frequency-domain information under undamaged and damaged conditions. The proposed research develops an early-phase Digital Twin (DT) to present static and real-time dynamic data in a rich-fed BIM database.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"2 3","pages":"Article 100053"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49879400","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: 1

Comparative analysis of machine learning techniques for predicting water main failures in the City of Kitchener 预测基奇纳市水管故障的机器学习技术的比较分析

Journal of Infrastructure Intelligence and Resilience Pub Date : 2023-09-01 DOI: 10.1016/j.iintel.2023.100044

Abdelhady Omar, Atefeh Delnaz, Mazdak Nik-Bakht

{"title":"Comparative analysis of machine learning techniques for predicting water main failures in the City of Kitchener","authors":"Abdelhady Omar, Atefeh Delnaz, Mazdak Nik-Bakht","doi":"10.1016/j.iintel.2023.100044","DOIUrl":"https://doi.org/10.1016/j.iintel.2023.100044","url":null,"abstract":"<div><p>The resilience of water main networks highly depends on the capacity for identifying and fixing structural failures in the system as fast as possible. Given the buried nature of such systems, this will be hard and costly through manual or semi-automated inspections. In this paper, a data-driven method is applied to predict the failure of water mains in the City of Kitchener. Six machine learning prediction models were developed under two scenarios: global models, which consider the three dominant material types in the network; and the homogenous model, which considers only cast-iron pipes. The water main’s condition score, length, and criticality score were the most influential factors on the pipe failure. The random forest models outperformed the other machine learning models with an accuracy of 97.3% and an F1-score of 80.4%; the homogenous modeling showed superior performance than the global one with an F1-score of 86.0%. The results showed that more than 72% of breaks could have been potentially prevented by monitoring and upgrading only 8% of the network. The superiority of the developed models lies in their ability to predict pipe failures with the least number of false alarms.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"2 3","pages":"Article 100044"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49879449","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: 2

Literature review of digital twin technologies for civil infrastructure 民用基础设施数字孪生技术的文献综述

Journal of Infrastructure Intelligence and Resilience Pub Date : 2023-09-01 DOI: 10.1016/j.iintel.2023.100050

Cheng Liu, Peining Zhang, Xuebing Xu

引用次数: 3

Risk analysis of onshore oil and gas pipelines: Literature review and bibliometric analysis 陆上油气管道风险分析:文献综述与文献计量分析

Journal of Infrastructure Intelligence and Resilience Pub Date : 2023-08-14 DOI: 10.1016/j.iintel.2023.100052

Haile Woldesellasse , Solomon Tesfamariam

引用次数: 0

Unmanned aerial vehicle-based computer vision for structural vibration measurement and condition assessment: A concise survey 基于无人机的结构振动测量与状态评估计算机视觉研究综述

Journal of Infrastructure Intelligence and Resilience Pub Date : 2023-06-01 DOI: 10.1016/j.iintel.2023.100031

Kai Zhou , Zequn Wang , Yi-Qing Ni , Yang Zhang , Jiong Tang

{"title":"Unmanned aerial vehicle-based computer vision for structural vibration measurement and condition assessment: A concise survey","authors":"Kai Zhou , Zequn Wang , Yi-Qing Ni , Yang Zhang , Jiong Tang","doi":"10.1016/j.iintel.2023.100031","DOIUrl":"https://doi.org/10.1016/j.iintel.2023.100031","url":null,"abstract":"<div><p>With the rapid advance in camera sensor technology, the acquisition of high-resolution images or videos has become extremely convenient and cost-effective. Computer vision that extracts semantic knowledge directly from digital images or videos, offers a promising solution for non-contact and full-field structural vibration measurement and condition assessment. Unmanned aerial vehicles (UAVs), also known as flying robots or drones, are being actively developed to suit a wide range of applications. Taking advantage of its excellent mobility and flexibility, camera-equipped UAV systems can facilitate the use of computer vision, thus enhancing the capacity of the structural condition assessment. The current article aims to provide a concise survey of the recent progress and applications of UAV-based computer vision in the field of structural dynamics. The different aspects to be discussed include the UAV system design and algorithmic development in computer vision. The main challenges, future trends, and opportunities to advance the technology and close the gap between research and practice will also be stated.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"2 2","pages":"Article 100031"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49903749","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: 2

Comparison of multimodal RGB-thermal fusion techniques for exterior wall multi-defect detection 多模态rgb -热融合技术在外墙多缺陷检测中的比较

Journal of Infrastructure Intelligence and Resilience Pub Date : 2023-06-01 DOI: 10.1016/j.iintel.2023.100029

Xincong Yang , Runhao Guo , Heng Li

{"title":"Comparison of multimodal RGB-thermal fusion techniques for exterior wall multi-defect detection","authors":"Xincong Yang , Runhao Guo , Heng Li","doi":"10.1016/j.iintel.2023.100029","DOIUrl":"https://doi.org/10.1016/j.iintel.2023.100029","url":null,"abstract":"<div><p>Exterior wall inspections are critical to ensuring public safety around aging buildings in urban cities. Conventional manual approaches are dangerous, time-consuming and labor-intensive. AI-enabled drone platforms have recently become popular and provide an alternative to serving automated and intelligent inspections. However, current identification only investigates RGB image of visual defects or thermal images of thermal anomalies without considering the continuous monitoring and the conversion between multiple defects. To gain new insights with modality-specific information, this research therefore compares the performance of early, intermediate, and late multimodal RGB-Thermal images fusion techniques for multi-defect detection in facades, especially for detached tiles and missing tiles. Numerous RGB and thermals images from an ageing campus building were collected as a dataset and the classical UNet for image segmentation was modified as a benchmark. The comparative results regarding accuracy (mAP, ROC, and AUC) proved that early fusion model performed well in distinguishing detached tiles and missing tiles from complex and congested facades. Nevertheless, intermediate and late fusion models were proven to be more efficient and effective with an optimal architecture, achieving high mean average accuracy with much less parameters. In addition, the results also showed that multi-modal fusion techniques can significantly improve the performance of multi-defects detection without adding a large number of parameters to single-modal AI models.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"2 2","pages":"Article 100029"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49903752","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: 1

Magnetorheological elastomer base isolation in civil engineering: A review 土木工程中磁流变弹性体基础隔震:综述

Journal of Infrastructure Intelligence and Resilience Pub Date : 2023-06-01 DOI: 10.1016/j.iintel.2023.100039

Shida Jin , Jian Yang , Shuaishuai Sun , Lei Deng , Zexin Chen , Liping Gong , Haiping Du , Weihua Li

引用次数: 0

Application of LS-PCP model based on EWM in predicting settlement of high-speed railway roadbed 基于EWM的LS-PCP模型在高速铁路路基沉降预测中的应用

Journal of Infrastructure Intelligence and Resilience Pub Date : 2023-06-01 DOI: 10.1016/j.iintel.2023.100037

Dejun Ba , Guangwu Chen , Peng Li

{"title":"Application of LS-PCP model based on EWM in predicting settlement of high-speed railway roadbed","authors":"Dejun Ba , Guangwu Chen , Peng Li","doi":"10.1016/j.iintel.2023.100037","DOIUrl":"https://doi.org/10.1016/j.iintel.2023.100037","url":null,"abstract":"<div><p>Accurate prediction of roadbed settlement is of great significance to the maintenance of high-speed railway roadbeds and the safe operation of trains. This study proposes a long- and short-term parallel combined prediction (LS-PCP) model based on the prediction characteristics of the LSTM model, GM(1.1) model, and ESP model and applies it to the prediction of roadbed settlement of high-speed railways. First, according to the spatiotemporal characteristics, slow-varying characteristics, and short valid data characteristics of the settlement process of a high-speed railway roadbed, this study designed a combined form of long-term LSTM prediction and short-term GM(1.1) and ESP sliding prediction to overcome the problem of large prediction errors when roadbed settlement enters different stages. Next, the mutual inclusiveness of the member models’ prediction results is tested by the principle of inclusiveness test, and the combination weights are determined by considering the information entropy of the member models through the entropy weighting method. Finally, the combined prediction results of the proposed LS-PCP model are verified from the actual monitoring data of a high-speed railway in Hebei Province and the Guiguang High-speed Railway. The results prove that the proposed LS-PCP combined model has higher prediction accuracy, and the prediction data of this model have important reference significance for the maintenance of high-speed railway roadbeds and safe vehicle operation.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"2 2","pages":"Article 100037"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49903751","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

Generative adversarial network for predicting visible deterioration and NDE condition maps in highway bridge decks 基于生成对抗网络的公路桥面可见劣化和NDE状态预测

Journal of Infrastructure Intelligence and Resilience Pub Date : 2023-06-01 DOI: 10.1016/j.iintel.2023.100042

Amirali Najafi , John Braley , Nenad Gucunski , Ali Maher

{"title":"Generative adversarial network for predicting visible deterioration and NDE condition maps in highway bridge decks","authors":"Amirali Najafi , John Braley , Nenad Gucunski , Ali Maher","doi":"10.1016/j.iintel.2023.100042","DOIUrl":"https://doi.org/10.1016/j.iintel.2023.100042","url":null,"abstract":"<div><p>Bridge decks tend to degrade faster than other bridge components due to environment exposure and vehicular loading. Periodic degradation monitoring is needed for timely rehabilitation measures and development of service life models in bridge decks. Surface degradation are identified through visual inspection (VI) and post-processing of high-definition imagery. Although VI is the primary NDE method employed by most transportation authorities, many anomalies (e.g., cracking, corrosion, and delamination) remain hidden under the surface until deteriorations have grown large enough to surface (e.g., spalling). Subsurface degradation is best identified through other forms of non-destructive evaluation (NDE). Inferences can be made between the various NDE methods, as the mechanisms behind the damages sensed by each method are shared. For instance, condition map from an NDE method may infer future visible deterioration, as well as condition maps for other NDE methods. In this paper, a deep learning approach based in a conditional generative adversarial network is presented for modeling of plausible visible deterioration and NDE condition maps. Two applications are explored: (i) visualization of plausible future deterioration based on current NDE condition map, and (ii) visualization of condition maps for NDE methods from other NDE methods. Field and experimental data from the BEAST facility at Rutgers University are used to develop the training databases for each application.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"2 2","pages":"Article 100042"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49903754","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