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

Multi-modal deep fusion for bridge condition assessment 基于多模态深度融合的桥梁状态评估

Journal of Infrastructure Intelligence and Resilience Pub Date : 2023-10-02 DOI: 10.1016/j.iintel.2023.100061

Mozhgan Momtaz , Tianshu Li , Devin K. Harris , David Lattanzi

{"title":"Multi-modal deep fusion for bridge condition assessment","authors":"Mozhgan Momtaz , Tianshu Li , Devin K. Harris , David Lattanzi","doi":"10.1016/j.iintel.2023.100061","DOIUrl":"https://doi.org/10.1016/j.iintel.2023.100061","url":null,"abstract":"<div><p>Bridge condition rating is a challenging task as it largely depends on the experience-level of the manual inspection and therefore is prone to human errors. The inspection report often consists of a collection of images and sequences of sentences (text) explaining the condition of the considered bridge. In a routine manual bridge inspection, an inspector collects a set of images and textual descriptions of bridge components and assigns an overall condition rating (ranging between 0 and 9) based on the collected information. Unfortunately, this method of bridge inspection has been shown to yield inconsistent condition ratings that correlate with inspector experience. To improve the consistency among image-text inspection data and further predict the accordant condition ratings, this study first provides a collective image-text dataset, extracted from the collection of bridge inspection reports from the Virginia Department of Transportation. Using this dataset, we have developed novel deep learning-base methods for an automatic bridge condition rating prediction based on data fusion between the textual and visual data from the collected report sets.</p><p>Our proposed multi modal deep fusion approach constructs visual and textual representations for images and sentences separately using appropriate encoding functions, and then fuses representations of images and text to enhance the multi-modal prediction performance of the assigned condition ratings. Moreover, we study interpretations of the deployed deep models using saliency maps to identify parts of the image-text inputs that are essential in condition rating predictions. The findings of this study point to potential improvements by leveraging consistent image-text inspection data collection as well as leveraging the proposed deep fusion model to improve the bridge condition prediction rating from both visual and textual reports.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"2 4","pages":"Article 100061"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49891238","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

Identifying potentially dangerous areas of frost heaving and surfacing of the buried oil pipeline 识别潜在的冻胀危险区域和埋地输油管道的地表

Journal of Infrastructure Intelligence and Resilience Pub Date : 2023-09-09 DOI: 10.1016/j.iintel.2023.100054

Alla Yu. Vladova , Yury R. Vladov

{"title":"Identifying potentially dangerous areas of frost heaving and surfacing of the buried oil pipeline","authors":"Alla Yu. Vladova , Yury R. Vladov","doi":"10.1016/j.iintel.2023.100054","DOIUrl":"https://doi.org/10.1016/j.iintel.2023.100054","url":null,"abstract":"<div><p>This scientific study aims to automatically identify potentially dangerous areas of frost heaving and surfacing of a buried oil pipeline using the geological description of soil profile. The geological description of soil profile along the proposed route of a pipeline entails the study and identification of various layers of soil to determine the soil's suitability for pipeline installation and support. Enriching the geological description of soils in the first stage was achieved by creating a family of parameters that characterize the presence of water in two states and the interaction of the buried oil pipeline with soil layers. In the second stage, missed and erroneous soil parameters were restored by searching for similar patterns along the route of the pipeline using the enriched geological description of soil profile. Afterward, the selected areas of frost heaving and surfacing were ranked by potential danger in the third stage. The algorithm developed was shown to reduce the risk of damage to the oil pipeline and enrich the geological description of soil profile without additional field works. The results of the study allowed for the allocation of potentially dangerous areas where frost heaving and surfacing occur. The methodology described in the study can be applied in the midstream segment of the oil and gas industry to minimize the risk of pipeline damage.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"2 4","pages":"Article 100054"},"PeriodicalIF":0.0,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49891239","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

Structural displacement sensing techniques for civil infrastructure: A review 民用基础设施结构位移传感技术综述

Journal of Infrastructure Intelligence and Resilience Pub Date : 2023-09-01 DOI: 10.1016/j.iintel.2023.100041

Zhanxiong Ma, Jaemook Choi, Hoon Sohn

引用次数: 4

Design and implementation of sustainable solar energy harvesting for low-cost remote sensors equipped with real-time monitoring systems 为配备实时监测系统的低成本远程传感器设计和实现可持续太阳能收集

Journal of Infrastructure Intelligence and Resilience Pub Date : 2023-09-01 DOI: 10.1016/j.iintel.2023.100051

Kaveh Malek , Edgardo Ortíz Rodríguez , Yi-Chen Lee , Joshua Murillo , Ali Mohammadkhorasani , Lauren Vigil , Su Zhang , Fernando Moreu

{"title":"Design and implementation of sustainable solar energy harvesting for low-cost remote sensors equipped with real-time monitoring systems","authors":"Kaveh Malek , Edgardo Ortíz Rodríguez , Yi-Chen Lee , Joshua Murillo , Ali Mohammadkhorasani , Lauren Vigil , Su Zhang , Fernando Moreu","doi":"10.1016/j.iintel.2023.100051","DOIUrl":"https://doi.org/10.1016/j.iintel.2023.100051","url":null,"abstract":"<div><p>Data acquisition systems, such as Wireless Smart Sensor Networks (WSSNs) can increase the resilience of infrastructure by providing real-time monitoring and data collection of environmental parameters. Yet, sustainable energy supplies for sensor networks established in remote and inaccessible areas still present a challenge. Previously, researchers have attempted to address this difficulty by proposing different energy systems including solar energy harvesting, however, significant prolonged experimental data for the operation of extensive networks powered by solar energy has not been reported. This paper presents an original design and implementation of an energy system for a large WSSN and provides the sensors' power status data over a significant duration. A network of low-cost flood monitoring sensors, including twenty-six water level sensors, twenty rain gauges, and eight communication nodes were deployed and tested on summer and fall 2022 at six remote locations at the northern New Mexico Pueblo, Ohkay Owingeh. A thermometer and a humidity sensor were added to each communication node to record temperature and air's moisture level. In addition, a networked voltage monitoring system was deployed to observe the sensors energy status in real-time. The items of the WSSN are composed of two differing energy circuits suited for their energy demands. The sensors' energy circuits contain a photovoltaic panel, a lithium-polymer battery, a control device, and a DC-to-DC converter. Whereas the communication nodes contain another photovoltaic panel, a lead-acid battery, and a solar charging controller. The findings provide a perspective on the long-term field deployment of WSSNs consisting of low-cost sensors.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"2 3","pages":"Article 100051"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49879402","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

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