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.

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.

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.

Telecommunication infrastructure (TI) is becoming increasingly vital in modern society, where information exchange is needed in almost all aspects of the built environment, business operations, and people's daily lives. The ongoing 5G rollout will lead to a paradigm shift in regional TI deployment landscape, with increased seismic hazard exposure particularly due to the densely deployed small cells. As TI is known to be vulnerable to seismic hazard impacts yet necessary for post-earthquake emergency response, this study carries out a pioneering effort in quantifying the post-earthquake TI failures and functionality to better support risk mitigation decision-making. We propose a novel seismic risk assessment framework for regional 5G TI, by holistically integrating regional seismic hazard analysis, infrastructure seismic exposure data, electric power infrastructure seismic fragility modeling and network connectivity analysis, as well as wireless TI functionality modeling. The proposed framework is evaluated based on a hypothetical regional infrastructure testbed located in Memphis, Tennessee, subjected to several earthquake scenarios. From a reference heterogeneous 5G TI deployment scenario, the results indicate that significant performance degradation of 5G TI is expected especially after major earthquake events. Enabled by the proposed framework, we further compared the efficacy of several risk mitigation strategies and pertinent implications are provided.

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.

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.

Pipelines subject to ground deformations generated by geohazard loads carry high importance on pipeline analysis, design, and assessment due to risk of structural damage or failure. Additionally, internal pressure and temperature variation within an operating pipe induce additional strains in combination with pipe strains generated by ground displacement. In this study, an enhanced predictive method is proposed founded upon methods employed by Zheng et al. (2022) to assess pipeline behaviour subject to permanent ground displacement, while considering effects of internal operating pressure and temperature variation. The finite difference-based method previously proposed for strain analysis of buried steel pipes subject to ground movement ignores the effects of internal pressure and/or temperature loading, limiting the applicability of this approach to exclude the operating conditions of pipelines. To address this limitation, the proposed enhanced method accounts for the initial thermal strains and biaxial stress state in the pipe due to hoop stress generated by internal pressure. These additional strains are considered within the expressions of internal axial force and bending moment, derived based on the actual stress distribution on the pipe cross-section. The accuracy of the proposed method is validated against the finite element method (FEM) with respect to results of strain and deformation demand using several indicative case studies. This research provides an effective method of incorporating temperature and internal pressure loads of pipelines subject to permanent ground displacements of varying types, magnitudes, and directions.

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.