Journal of Infrastructure Intelligence and Resilience最新文献

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.

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.

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.

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.

Currently, there are numerous drawbacks associated with infrastructure health monitoring and management, such as inefficiency, subpar real-time functionality, demanding data requirements, and high cost. Digital twin (DT), a hybrid of a computational simulation and an actual physical system, has been proposed to overcome these challenges and become increasingly popular for modeling civil infrastructure systems. This literature review summarized different methods to build digital twins in civil infrastructure. In addition, this review also introduced the current progress of digital twins in different infrastructure sectors, including smart cities and urban spaces, transport systems, and energy systems, along with detailed examples of their various applications. Finally, the current challenges in digital twin technologies for civil infrastructure are also highlighted.

A significant number of research papers focusing on the risk analysis of oil and gas pipelines have been published. The present study includes a bibliometric analysis and literature review, considering publications from 1982 to 2022, to provide a comprehensive overview of research contributions in the field of risk assessment for oil and gas pipelines. Various techniques, such as trend analysis, bibliographic coupling, co-occurrence analysis, network analysis, and citation analysis are used to study the published papers related to the subject topic. Based on the research's keywords, the co-occurrence analysis reveals the strong and weak connections between various topics in this domain, and as a result, future research areas can be identified.

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.

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.

The attention given to magnetorheological elastomers (MREs) has been on the rise over the last few decades. MREs feature a remarkable field-controllable modulus or mechanical characteristics that are influenced by an external magnetic field. Compared to its family member magnetorheological fluids (MRF), MREs offer advantages in terms of overcoming sealing and sedimentation issues. This makes them highly promising for the development of smart base isolation systems of buildings and other infrastructures. This review paper attempts to highlight the impactful progress of MRE base isolation in civil engineering over the past decades. It begins with a brief introduction of MREs including its fundamental principles, operation modes, and fabrication process. Then, the recent investigations of MREs and MRE base isolators are reviewed. Finally, discussions are made on the challenges and potential topics for further investigations.

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.