Journal of Infrastructure Intelligence and Resilience最新文献

Deep learning-based object detection methods are utilized for safety management at construction sites, which require large-scale, high-quality, and well-labeled datasets for training. The existing construction datasets are relatively small due to the high expense of labor-intensive annotation, and the varying quality of the construction images also affects the detection performance of the model. To address the limitations of datasets, this study proposes a new method for construction object detection by integrating super-resolution and semi-supervised learning. The proposed method improves the quality of construction images and achieves excellent detection performance with limited labeled data. First, the Real-ESRGAN model is introduced to improve the quality of construction images and make the construction objects visible. The proposed super-resolution method can enhance the texture details of low-resolution images, hence improving the performance of object detection models. Second, the mean-teacher network is adopted to expand the training set, thus avoiding the labor-intensive annotation work. To verify the effectiveness of the proposed method, the method is applied to the state-of-the-art Yolov5 object detection model, and construction images from the Site Object Detection Dataset (SODA) with different labeled data proportions (from 10% to 50% in 10% intervals with an extreme case of 5%) are used as the training set. By comparing with the existing supervised learning method, it is shown that the proposed method can achieve better detection performance. In particular, the method is more effective in enhancing detection performance when the proportion of the labeled data is smaller, which is of great practical value in real-world engineering. The experimental results show the potential of the proposed method in improving image quality and reducing the expense of developing construction datasets.

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.

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.

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.

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.”

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.

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.