Journal of Infrastructure Intelligence and Resilience最新文献

Given the complex climate conditions in coastal areas and their role as key transportation hubs for hazardous chemicals, this study proposes a method to quantitatively and comprehensively evaluate transportation risks. Initially, accident data were analyzed to identify risk factors from five aspects: human, vehicle, materials, environment, and management, based on system safety theory. Subsequently, a risk analysis model was developed using Decision-making Trial and Evaluation Laboratory, interpretive structural model theory, and Bayesian theory to quantitatively assess accident risk levels. The model was applied to a case involving a hazardous chemical accident on a cross-sea bridge, where Bayesian backward reasoning was used to analyze the sensitivity and importance of risk factors. This approach facilitated the key risk factors affecting the safety of hazardous chemical transportation systems. Notably, the study incorporated scenarios involving hazardous material transport vehicles crossing sea bridges into the risk assessment framework, offering valuable insights for management authorities. It also considered the impact of strong side winds-a factor often overlooked-in hazardous material transport. The validation process demonstrated that the method accurately quantifies the risk of hazardous chemical transportation and identifies the key factors influencing accident occurrence. The research highlights that strong gusts of wind significantly impact safety, and human factors are crucial in the overall risk system.

The structural health of underwater infrastructure such as bridges, dams, and pipelines are constantly degrading due to aging, fatigue, unexpected loads, and environmental wear and tear. Historically, these structures have been inspected by human divers; however, the need for safe and cost-effective monitoring has fostered the development of unmanned underwater vehicles (UUVs) capable of performing subsea surveillance. This paper provides a concise and systematic review of emerging technologies and methodologies for deploying underwater vehicles to perform inspections. Literature is classified into two main groups: advancements to UUV designs and capabilities and advancements to instrumentation for underwater structural health monitoring. After a systematic review, the existing challenges to UUV development and implementation are discussed. Finally, recommendations for future areas of research are outlined. This systematic literature survey aims to provide researchers and practitioners with a holistic outlook on the current state and future trends of UUV-based infrastructure inspection.

In shield tunneling within karst formations, the vibrational effects often impact the safety of surrounding residents and buildings. The study of construction vibration mitigation measures holds significant importance. Based on the shield tunneling project in the Huang-Shang section of the Xuzhou Metro Line 6, this paper studies the causes, propagation characteristics and influencing factors of ground vibration caused by shield construction. Three effective mitigation measures were identified: (1) Optimization adjustment of shield tunneling parameters; (2) Grouting with mixed bentonite; (3) Layout of vibration reduction boreholes. Each mitigation measure was individually tested for its impact on ground vibration. The comprehensive application of the three measures in shield tunnel construction was analyzed to assess their combined effectiveness. The integration of actual engineering measurements indicates that boreholes provide the best damping effect. Furthermore, the application of multiple mitigation measures resulted in an overall 60% reduction in ground vibration, significantly mitigating the impact on residential structures on the ground. This study provides valuable references for vibration reduction measures in other engineering projects.

Structural damage identification (SDI) methods using incomplete modal information can avoid the extension for unmeasured degrees of freedom, but the absence of essential damage information often leads to the failure of SDI. To address this problem, a novel SDI method based on dual sensitivity analysis and optimal sensors placement technique is proposed in this study. Firstly, in the optimal sensor placement technique, an improved eigenvector sensitivity method combined with weighted modal kinetic energy is proposed, which enables the acquisition of eigenvector information related to damage sensitivity, and incorporates it into the modal strain energy sensitivity matrix to obtain the dual sensitivity analysis matrix. Then, the sparsity of structural damage is considered, and the L1 sparse regularization is selected and introduced into the dual sensitivity analysis damage equation for better SDI results. Finally, to assess the effectiveness of the proposed method, a series of numerical simulations and experimental verifications were carried out under different structural damage scenarios. The results indicate that the proposed method can efficiently localize and quantify the structural damage with minimal modal information in one single step.

This paper introduces a new reinforced concrete column-steel beam (RCS) joint that employs asymmetric frictional connections (AFC) to improve energy dissipation and moment transfer, reducing stress concentrations within the joint’s core. Two RCS joint specimens with AFC and floor slabs were designed and tested under quasi-static loading to analyze the impact of bolt preload on seismic performance. The experimental results demonstrate that RCS joints with AFC and slabs exhibit favorable seismic behavior in terms of bearing capacity, energy dissipation, and stiffness degradation. Increasing bolt preload enhances the bearing capacity, stiffness, and energy dissipation capacity of the joints. The failure occurred at the steel beam splice connections, while only minor micro-cracks appeared in the reinforced concrete column when the joint's bearing capacity dropped below 80% of the peak load. Displacement at the column top was primarily influenced by steel beam and column deformation, with minimal contribution from joint core deformation. The use of AFC effectively reduced deformation in the joint core area, meeting seismic design code requirements for “strong columns-weak beams.”

Accurate evaluation of flutter probability is of paramount importance in the design of long-span bridges. In current engineering practice, at the design stage, flutter critical wind speed is usually estimated by the wind tunnel test with section model or aeroelastic model, which is sensitive to modal frequencies and damping ratios. After construction, structural properties of existing structures will change with time due to various factors, such as structural deteriorations and periodic environments. The structural dynamic properties, such as modal frequencies and damping ratios, cannot be considered as the same values as the initial ones, and the deteriorations should be included when estimating the life-cycle flutter probability. This paper proposes an evaluation framework to assess the life-cycle flutter probability of long-span bridges considering the deteriorations of structural properties, based on field monitoring data. Fast Bayesian approach is employed for modal identification of a suspension bridge with the center span of 1650 m, and the field monitoring data during 2010–2015 is analyzed to determine the deterioration functions of modal frequencies and damping ratios, as well as their inter-seasonal fluctuations. According to the historical trend, the long-term structural properties can be predicted. Consequently, the probability distributions of flutter critical wind speed for each year in the long term are calculated, conditionally based on the predicted modal frequencies and damping ratios.

The past ten years have witnessed the tremendous progress of structural health monitoring applications in civil infrastructures. This is particularly embodied in railway engineering. The increasing train speed brings greater challenges to safety and ride comfort, and the primary theme of maintenance has been gradually altered from offline inspection to online monitoring. Rail operators must get an in-time warning of potential structural defects before critical failure takes place. It is more favourable that the rail operators can take hold of the real-time status of the key components and infrastructures in railway systems. This paper summarizes a long-term research series by the authors’ research team on online monitoring of rail tracks at turnout areas utilizing acoustic emission-based sensing technique, and more importantly, successively advancing signal processing methods and data-driven analysing frameworks, covering Bayesian inference, convolutional neural networks, transfer learning and task similarity analysis. The proposed algorithms tackle noise interference brought by wheel-rail impacts, great uncertainties in an open environment, and insufficiency of monitoring data, and realize comprehensive monitoring of rail tracks in turnout areas from basic crack detection to regressive condition assessment step-by-step.

Software systems monitoring civil structures over their lifetime are exposed to the risk of aging much faster than the structures themselves. This risk can be minimized if we use models describing the structure, geometry, processes, interaction, and risk assessment as well as the data collected over the lifetime of a civil structure. They are considered as a unity together with the civil structure. These model-based systems constitute a digital twin of such a civil structure, which through appropriate operative services remain in permanent use and thus co-evolve with the civil structure even over a long-lasting lifetime. Even though research on digital twins for civil structures has grown over the last few years, digital twin engineering with heterogeneous models and data sources is still challenging. Within this article, we describe models used within all phases of the whole civil structure life cycle. We identify the models from the computer science, civil engineering, mechanical engineering, and business management domains as specifically relevant for this purpose, as they seem to cover all relevant aspects of sustainable civil structures at best, and discuss them using a dam as an example. Moreover, we discuss challenges for creating and using models within different scenarios such as improving the sustainability of civil structures, evaluating risks, engineering digital twins, parallel software and object evolution, and changing technologies and software stacks. We show how this holistic view from different perspectives helps overcome challenges and raises new ones. The consideration from these different perspectives enables the long-term software support of civil structures while simultaneously opening up new paths and needs for research on the digitalization of long-lasting structures.