Journal of Infrastructure Intelligence and Resilience最新文献

The simultaneous increase in natural disasters and human dependence on critical infrastructures for essential services such as water, electricity, etc., places ever-increasing demands on the reliable, safe, resilient design and operation of these infrastructures, with a trade-off between continuity of supply (safety and resilience) and quality of supply (reliability and efficiency) at limited cost. With this in mind, a new methodology for the analysis of electric power systems inspired by natural ecosystems is proposed here and applied to representative systems from literature. Information theory is used to quantify the results of the ecological network analysis (ENA) performed. The analysis shows that electric power systems are more efficient than reliable and vulnerable to disasters. A flow matrix is constructed from the available IEEE systems data, quantified and analyzed using information theory, and finally validated by contingency analysis and SCOPF analysis. The original network configurations are compared to random generated topologies. Comparisons are also made with ENA-inspired configurations. The latter show significantly fewer violations in each contingency scenario compared to the original configurations, further supporting the use of ENA to balance power system efficiency and resilience. Thus, ENA can be used to develop power systems with balanced efficiency and resilience.

Earthquake-induced vibrations of wind turbines may compromise structural serviceability and safety. Most previous studies adopted passive control devices to mitigate the seismic responses of wind turbines. However, their control effectiveness is heavily dependent on the mass ratio between control devices and wind turbines, and they were typically housed at the tower top or within the nacelle. The restricted space within the hollow tower and the nacelle imposes considerable challenges for the implementation of such devices, rendering the application of large-scale control devices unfeasible for structural vibration control of wind turbines. To this end, this paper integrates a negative stiffness element within a conventional tuned mass damper (TMD), termed KDamper, to mitigate vibrations of wind turbine towers under seismic loads. Specifically, the widely used NREL 5 MW wind turbine is selected as a prototype structure and its tower is modelled as a multiple-degree-of-freedom system. Then KDamper is incorporated into the developed model and its parameters are optimized based on the H₂ criterion. Subsequently, the control effectiveness of KDamper is investigated and compared with TMD in the frequency domain, and the control performances in terms of the effectiveness and robustness of KDamper are further examined under a series of earthquake records. Results show that KDamper has superior control effectiveness and robustness than TMD, indicating it has considerable potential for application in improving wind turbine performances against earthquake hazards.

This paper presents a causal analysis aimed at identifying and estimating causal effects with regard to bridge failures under extreme events. Observational data on about 299 bridge incidents were used to conduct this causal investigation and examine bridges’ performance. As causal investigations can also deliver counterfactual assessments of parallel worlds, a causal analysis can serve as a high-merit methodology to evaluate the performance of critical bridges. Our findings quantify the causal impacts of various factors spanning the characteristics of bridges, traffic demands, and incident type (i.e., fire, high wind, scour/flood, earthquake, and impact/collision). More specifically, our analysis reveals high causal effects related to the used structural system, construction materials, and demand served.

Wireless Smart Sensor Networks (WSSN) have seen significant advancements in recent years. They act as a core part of structural health monitoring (SHM) systems by facilitating efficient measurement, assessment, and hence maintenance of civil infrastructure. This paper presents the latest technology developments of WSSN in the last ten years, including ones for a single sensor node and those for a network of nodes. Focus is placed on critical aspects of such advancements, including event-triggered sensing, multimeric sensing, edge/cloud computing, time synchronization, real-time data acquisition, decentralized data processing, and long-term reliability. In addition, full-scale applications and demonstrations of WSSN in SHM are also summarized. Finally, the remaining challenges and future research directions of WSSN are discussed to promote the further development and applications.

The construction sector is now experiencing a significant transformation, primarily motivated by the need to enhance operational efficiency and promote sustainable practices. The emergence of blockchain technology has been seen as a disruptive factor that has the potential to fundamentally transform the field of supply chain management within the construction industry. Nevertheless, the extent to which this technology has revolutionized the sector has yet to be extensively investigated. The primary objective of this study is to address the existing research void by examining the impact of blockchain technology on enhancing the capabilities of building supply chains. This study employs a thorough examination of empirical case studies and a survey conducted among 136 industry professionals to explore the many functions of blockchain technology in augmenting efficiency, transparency, and traceability within building supply chains. The significant constructs were found having impact on blockchain implementation for construction supply chains are, Transparency and Traceability (β = 0.202, ρ = 0.000, t = 42.560), Smart Contracts for Automation (β = 0.232, ρ = 0.000, t = 62.596), Quality Assurance and Compliance (β = 0.230, ρ = 0.000, t = 64.704), Dispute Resolution and Accountability (β = 0.235, ρ = 0.000, t = 79.533), Supplier Management and Verification (β = 0.251, ρ = 0.000, t = 49.404).

Hyperloop research focuses on investigating the design and limitations of its complex subsystems. However, the existing literature overlooks the fatigue failure characteristics of the Hyperloop tube, leaving future engineers without informed estimates of its reliability. To address this gap, this study examined the occurrence and prediction of fatigue failure in the Hyperloop system. The findings revealed that both the underground and above-ground configurations showed resistance to fatigue failure, with the underground system showing greater resilience. Additionally, sensitivity analysis highlighted support spacing, tube ultimate tensile strength, and tube radius as the most influential design variables affecting fatigue sensitivity. Moreover, a reliability-based design cost optimization was performed, taking into account demand uncertainty and utilizing insights from previous analyses to determine ideal design parameters. This research sheds light on critical design aspects of the Hyperloop tube that require intensified attention to effectively mitigate the risk of fatigue failure.

Rail accidents caused by rail track derailments have been a growing concern due to repetitive thermal changes resulting from high temperature stresses in rails due to rail traction and environmental thermal variation. This leads to thermal buckling, which can result in catastrophic failure. Structural health monitoring (SHM) using the electromechanical impedance (EMI) technique has emerged as a promising technology to detect structural deterioration and its severity before it leads to failure. This study used piezoelectric sensors to collect piezo-coupled structural signatures of different rail-joint bars for high-temperature repetitive thermal cycles, which were then analyzed using an impedance analyzer. The results show that the piezo-coupled signatures could identify structural changes, and the damage metric, could be employed for continuous monitoring of structural rail defects due to excessive thermal stress and residual strain. The method also derived piezo-equivalent structural parameters, such as mass, stiffness, and damping, which were very satisfactory in detecting significant changes and consequent damage. Overall, this study presents a pre-emptive experimental method that can see thermal deterioration and instability in rails and rail joints, thereby reducing the risk of rail accidents caused by derailments.

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.

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.

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.

It is important to assess, monitor, and control civil infrastructure displacements, and extensive work has been done to develop structural displacement sensing techniques. This paper presents a comprehensive review of structural displacement sensing techniques, with particular focus on those for civil infrastructures. The working principles of structural displacement sensing techniques using thirteen different sensors are first reviewed, and the advantages and disadvantages of each sensor are briefly discussed. The disadvantages of single-mode sensor-based structural displacement estimation have been partially addressed by the use of multi-mode sensors. Thus, the studies on multi-mode sensor-based structural displacement estimation are reviewed. After that, field applications of these techniques to building structures, bridge structures, and other structures are briefly reviewed. The remaining challenges for the real application of these techniques are summarized, and future research directions are provided.