Journal of Civil Structural Health Monitoring最新文献

Inspections and condition monitoring of the stormwater pipe networks have become increasingly crucial due to their vast geographical span and complex structure. Unmanaged pipelines present significant risks, such as water leakage and flooding, posing threats to urban infrastructure. However, only a small percentage of pipelines undergo annual inspections. The current practice of CCTV inspections is labor-intensive, time-consuming, and lacks consistency in judgment. Therefore, this study aims to propose a cost-effective and efficient semi-automated approach that integrates computer vision technology with Deep Learning (DL) algorithms. A DL model is developed using YOLOv8 with instance segmentation to identify six types of defects as described in Water Services Association (WSA) Code of Australia. CCTV footage from Banyule City Council was incorporated into the model, achieving a mean average precision (mAP@0.5) of 0.92 for bounding boxes and 0.90 for masks. A cost–benefit analysis is conducted to assess the economic viability of the proposed approach. Despite the high initial development costs, it was observed that the ongoing annual costs decreased by 50%. This model allowed for faster, more accurate, and consistent results, enabling the inspection of additional pipelines each year. This model serves as a tool for every local council to conduct condition monitoring assessments for stormwater pipeline work in Australia, ultimately enhancing resilient and safe infrastructure asset management.

Delamination in reinforced concrete (RC) bridge decks can degrade the serviceability of entire bridges, leading to concrete spalling and steel rebar corrosion and eventually becoming a safety concern. Drone-based infrared thermography (IRT) offers a promising tool for rapid assessment of bridge deck delamination compared to labor-intensive coring and visual inspection methods. However, the performance of passive IRT in detecting the delamination of RC bridge decks at its minimum depth and size (i.e., spall 25 mm or less deep or 150 mm or less in diameter) stipulated under a ‘fair’ condition state in the 2019 AASHTO Manual for Bridge Element Inspection has not been verified adequately. In this study, four RC slabs of identical design were cast with embedded thin foam sheets to simulate a wide range of delamination in thickness, size, spacing, and depth. Together, the four slabs form a representative RC deck of a mark-up bridge. Controllable indoor active IRT tests of individual slabs were conducted to detect and quantify the foams that serve as a ground truth for the performance of drone-based passive IRT for deck delamination detection on the mark-up bridge as the embedded foams may be displaced during concrete slab casting and the slab support is altered during erection. Statistical analysis was carried out on the thermal contrasts of both passive and active IRT tests on the four slabs to investigate the effects of delamination geometry and embedment depth. Both the active and passive IRT methods proved successful in localizing delamination and identifying its equivalent thicknesses of as low as 1.63 mm and a size (150 mm in length or 25 mm in depth) corresponding to the ‘fair’ condition state in the AASHTO Manual for Bridge Element Inspection.

Excessive ground deformation caused by shield tunnelling is prone to irregular settlement and deformation cracking of the overlying building. Hence, accurately assessing the extent of damage to the building is crucial for the effective strengthening and repair of the structure. This paper presents a comprehensive case study of a metro shield tunnel conducted beneath a masonry building. We systematically monitored and investigated the settlement and crack development of the masonry building and discovered that the cracks in the masonry building were mainly situated at the maximum slope of the building settlement curve, rather than at the peak. After completion of the tunnel construction, the maximum settlement of the masonry building was 37 mm and the cracks were predominantly oblique cracks with a length of 0.6–7.6 m and a width of 0.5–5.0 mm. The maximum principal tensile strain in the walls of the masonry building was 0.153%, and the masonry building was evaluated to be moderately damaged according to the assessment criteria considering the extent of damage to the building surface. Then, we proposed a building damage assessment method that considers soil-structure interaction and subsequently verified it through finite-element results and field monitoring results. Finally, the effects of key parameters on the stiffness of the building were analyzed. The stiffness of the building was mainly affected by the opening ratio and the effective coefficient of the building cross section. These research results have significant guiding and reference values for safeguarding buildings during metro tunnel construction.

The analysis of the traffic-induced vibrations on the floors of the National Etruscan Museum of Villa Giulia in Rome is shown in this paper. The interest for this case study is related to the importance of this historic building and its contents, but also to the presence of particular vibration sources, i,e, a tram track and an underground train, in addition to vehicular and bus traffic. The differences between the two vibration sources and the comparison with the effects of ambient vibrations are analyzed, both in terms of amplitudes and frequency content. The measurements were done using seismometers, deployed in the portion of the museum adjacent to the tram line. The results show that the vibrations induced by the tram are much higher than the ambient vibrations and characterized by a different frequency content. The effects of the train are even much more evident but only in the portion of the building above the underground railway and frequencies even higher than those due to the tram are apparent in the recording spectra. The dynamic response of the structure is influenced very much by the vibration source features but also by its extremely long rectangular shape and the deformability of the floors. The results of this study are very useful to better manage the deployment of art objects, which are extremely vulnerable to vibrations at frequencies higher than those of interest for the building, in the museum or to design an antivibration protection system.

The large amount of data collected by structural health monitoring systems deployed in the bridge contains dynamic information about the structure. To enhance the prediction accuracy of the structural dynamic responses and to evaluate the frequencies from predicted restructured responses, this paper develops an approach of optimized variational mode decomposition (OVMD) combined with a genetic algorithm-back propagation (GA-BP) neural network. The procedure is first to establish the OVMD algorithm using relative root mean square error (RRMSE) and correlation coefficient to determine reasonable decomposition and retention of the intrinsic mode function (IMF) components in the response decomposition. Then each retained IMF component is used as input to the GA-BP for prediction. Finally, the frequencies and their characteristics of the structure are estimated from the predicted restructured responses. A damaged arch bridge test shows that OVMD overcomes the shortcomings of VMD, decomposes and reconstructs the signals effectively, and outperforms the other three methods in denoising. The experimental results of the long-span cable-stayed bridge prove that OVMD combined with GA-BP has higher prediction accuracy for the dynamic responses with high sampling rates. The structural frequencies are correctly determined from predicted recombined displacement and acceleration responses. This approach provides a useful tool for bridge dynamic response decomposition, reconstruction, prediction, and structural frequency evaluation.

Detecting cracks early benefits building maintenance by assessing structural safety, which in turn helps prevent potential severe damage and collapse, given that cracks in concrete surfaces often reflect underlying structural damage. However, the conventional method by human hands is time-consuming, inconvenient, and high risk for inspectors. In this present study, an improved framework for inspecting building surface cracks, which integrates digital innovations of Unmanned Aerial Vehicle (UAV) and deep learning technologies with wide-area coverage, high efficiency, and less intervention, is established. The feasibility of the proposed approach is demonstrated by conducting an experimental test on an in-service office building. The results show that not only can we achieve a prediction accuracy of over 97% on the validation dataset, but also that increasing the number and variety of images in the training dataset positively impacts the ability to detect concrete cracks. However, this improvement might not be as notable once the model has already learned sufficient features of concrete cracks. Additionally, a 3D model was created to virtually showcase the detection results. This opens up new possibilities for conducting building damage inspections by integrating these results into a virtual 3D space, which enhances overall structural health management and offers new insights for improving detection performance. Challenges and future directions to improve the effectiveness and address potential improvement approaches of the proposed framework in practice are also suggested.

As a wind-sensitive structure, long-span bridges are prone to the vibration excited by periodic shedding vortex called vortex-induced vibration (VIV). Timely warning and accurate identification of VIV are required for VIV detection and mitigation. To meet the above-mentioned requirements, the structural health monitoring system provides a wealth of field monitoring data, which serves as the basis for comprehensive analysis of bridge environmental conditions and structural states. In this paper, the wind field features and structural dynamic responses of a long-span suspension bridge were analyzed using field monitoring data from 2013, 2014, and 2017. First, the characteristic parameters with significant specificity, including the probability of wind speed, the probability of wind direction, root mean square (RMS), spectral peak difference rate, and energy proportion, were utilized as VIV early warning and identification indexes, the corresponding threshold of above index values was calculated based on the Pauta criterion. Meanwhile, different time intervals were selected to discuss early warning (identification)accuracy of the parameter thresholds. Then, the VIV early warning and identification strategy was established. Finally, the thresholds of each characteristic parameter were updated based on the VIV database and the accuracy of the strategy was verified. The results show that the probability of wind speed and direction in VIV ranges can provide early warning of the potential VIV. Based on the dynamic response characteristics, including the RMS of acceleration, power spectrum, and energy proportion, the proposed strategy can distinguish VIV from ambient vibration. The early warning and identification of VIV based on field monitoring data are successfully achieved by the proposed strategy, which can be applied to practical engineering.

There is a growing need for monitoring the structural health conditions of aging structures and for prioritizing maintenance works to extend their safe service life. This requires cheap, flexible, and reliable tools suitable for everyday use in engineering practice. This paper presents a computer vision-based technique combining motion magnification and statistical algorithms to calculate structural natural frequencies under environmental noise excitation, and its application to a reinforced concrete elevated water tank. Digital videos were recorded from various standpoints and post-processed by tracking in time either the variation of the grey-intensity or the motion of selected pixels. Computer vision-based outcomes were validated against accelerometric measurements and integrated to them to improve the understanding of the dynamic behaviour of the water tower, which, counterintuitively, resulted anything but trivial to predict.

In open-deck railway bridges, the timber ties constitute a major portion of the maintenance costs and must be replaced periodically. This procedure begins by sending surveyors to manually measure bridge and track geometry. The accuracy and efficiency of tie replacement procedures as part of bridge retrofitting projects can be significantly improved with the use of modern three-dimensional (3D) scanning technologies. This paper introduces a semi-automated geometric feature extraction framework specifically for the dapping process during tie replacement on railway bridges. First, a bridge must be 3D scanned to generate a point cloud. Next, the point cloud of the structure is pre-processed for alignment, sliced into 2D images for dimension reduction, and segmented into recognizable components. Finally, relevant features in every component are calculated and transformed into production tables or visualizable 3D models for manufacturing purposes. This framework is applied to an open-deck bridge in Lyndhurst, New Jersey. It is anticipated that with the introduction and further development of novel computer vision-based approaches, costly manual surveys of bridges can be avoided in the future.

An efficient and accurate two-stage approach, based on the artificial neural network (ANN) and an enhanced particle swarm optimization (EPSO) approach for model updating of structures using incomplete measurements, is proposed in this study. The first stage, preliminary model updating, employs the ANN to quickly learn the mapping relationship between the natural frequencies and stiffness of the structure using a few training, validation, and testing instances. The inputs and outputs of the ANN are the natural frequencies and stiffness of the structure, respectively. The ANN’s training, validation, and testing instances are extracted through Latin hypercube sampling. The ANN-predicted stiffness provides an excellent basis for determining and reducing the search space of the optimal stiffness in the second stage. The second stage, detailed model updating, searches for the optimal stiffness of the structure by using the EPSO approach. The EPSO approach improves particle swarm optimization (PSO) by employing an elite crossover strategy to avoid trapping in the local optimum and premature convergence. The feasibility and effectiveness of the proposed two-stage approach for stiffness updating of shear building structures using incomplete measurements are demonstrated by numerical and experimental examples. The results present that the proposed two-stage approach improves the computational efficiency and solution quality of the GA (Genetic Algorithm) and PSO for stiffness updating of shear building structures.