Journal of Infrastructure Intelligence and Resilience最新文献

This paper presents an approach for structural damage quantification using a long short-term memory (LSTM) auto-encoder and impulse response functions (IRF). Among time domain responses-based methods for structural damage identification, using IRF is advantageous over the original time domain responses, since IRF consists of information of system properties and is loading effect independent. In this study, IRFs are extracted from the acceleration responses measured from different locations of structures under impact force excitations. The obtained IRFs are concatenated. Moving averaging with a suitable window size is performed to reduce random variations in the concatenated responses. Further, principal component analysis is performed for dimensionality reduction. These selected principal components are then fed to the LSTM auto-encoder for structural damage identification. A noise layer is added as an input layer to the LSTM auto-encoder to regularise the model. The proposed model consists of two phases: (1) reconstruction of the selected “principal components” to extract the features; and (2) damage identification of structural elements. Numerical studies are conducted to verify the accuracy of the proposed approach. The results demonstrate that the proposed approach can accurately identify and quantify structural damage for both single- and multiple-element damage cases with noisy measurements, as well as uncertainties in the stiffness parameters. Furthermore, the performance of the proposed approach is evaluated using the limited measurements from a few sensors.

With increasing availability of cost-effective and high-resolution cameras, their use as a non-contact sensing tool has rapidly progressed for structural health monitoring. The cameras offer unique capabilities to provide full-field measurement with high spatial density at low cost. However, extracting high-density temporal data is challenging, as a high-speed camera increases the monitoring cost with high-rate data processing. Recently, motion magnification (MM) has shown significant success in analyzing low-amplitude motion of structural systems. However, previous studies observed that MM methodology performs poorly at low frame rates for modal identifications. In this paper, the influence of low frame rate on phased-based motion magnification (PMM) has been investigated. A novel technique is proposed by combining PMM with zero mean-normalization cross-correlation tracker to determine vibrational responses, and then the spatial Wigner-Ville spectrum-based time-frequency blind source separation method is explored for modal identification using the extracted vibrational responses obtained from the video data. The experimental data of a lumped mass experimental model and a steel bridge is used to test the accuracy of the proposed method. The original and motion-magnified image response data is compared with accelerometer data for modal identification. The proposed method is able to extract the modal parameters with high accuracy for motion-magnified images, even for low frame rates.

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.

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.