Journal of Civil Structural Health Monitoring最新文献

In this study, an existing six-storey reinforced concrete building with an asymmetric structural plan and soft storey irregularity was used as a test specimen and subjected to three-step progressive structural damages to detect the variations in its dynamic properties. Mode shapes and dominant frequencies of the undamaged building were determined by the ambient vibration survey (AVS) and it was seen that its first three modes were torsion coupled. Besides, soft storey irregularity was evident due to the lack of masonry infill walls on its ground floor. Later on, three-step progressive damages were applied to the building. The first step targeted three columns and one beam of the building, located on a corner region of its ground floor to peel off their clear covers. The second step razed two adjacent corner columns which were already moderately damaged in the first step, while the third step knocked the third moderately damaged column down. After each damage step, AVS was repeated with the same details as applied for the undamaged building. The obtained dynamic properties for the four phases of the building were evaluated with the sustained damage. Numerical analyses with the finite element model of the building representing its four different phases were also performed and the unique responses due to damage effects on the structure were investigated numerically. As a result of induced damage, the quantified amount of frequency change in modes and the new mode observed after particularly column loss scenarios can be utilized for efficient structural health-monitoring strategies of plan-asymmetric buildings and post-earthquake assessment of partially damaged buildings where timely objective assessment is important.

Structural Health Monitoring (SHM) of civil engineering structures is experiencing an increasing progress in the last decades. The present work focuses on the static behavior of a highway bridge due to environmental temperature effects. The goal of the present study was to test the applicability of the satellite-based synthetic aperture radar interferometry (InSAR) for deformation monitoring of a large, curved highway bridge and to compare the obtained results with alternative measurement techniques like classical geodesy surveying and with an advanced computer simulation. Such a comparison is quite rare and provides an important insight into the accuracy, efficiency and limitations of the InSAR technique in the context of SHM. Especially interesting was the question whether the InSAR technique is suitable for blind monitoring of a cluster of bridges in the region of interest. The present study shows that a pre-knowledge about each structure can be very important for a reliable interpretation of the InSAR measurement results. The second challenge of the study was to overcome several objective difficulties of combining and comparing quite different monitoring techniques that result from different sampling rates, measurement points and other specific features and sensitivities. Nevertheless, a suitable approach has been developed and implemented in the present study for the InSAR and total station measurements, providing new results and important knowledge about novel SHM techniques.

Aiming at the problem that earth-rock dam structure is susceptible to non-stationary signal interference in the process of collecting vibration information, this paper proposes a feature information extraction method based on the fusion of Dispersion Entropy Variational Mode Decomposition (DVMD) and Variance Dedication Rate (VDR) improved by Dispersion Entropy. First, multi-channel vibration signals are dynamically fused using the variance dedication rate to extract the complete vibration information of the dam body; then the entropy value of each modal component (Intrinsic Mode Function) under different decomposition layers is calculated by using Dispersion Entropy, and the entropy turning point is selected to determine the number of decomposition modes of DVMD, to compensate for the insufficiency of blind selection of decomposition modes in Variational Mode Decomposition. The entropy value turning point is selected to determine the number of decomposition modes of DVMD, which can make up for the deficiency of blindly selecting decomposition modes in Variational Mode Decomposition. To verify the accuracy and effectiveness of the method in this paper, three groups of simulated signals are constructed for numerical simulation, and it is found that its noise reduction effect is better than that of digital filtering, wavelet thresholding and Improved Variational Mode Decomposition, and the signal feature information can be effectively extracted. Combining the measured data of the embankment dam of HeLong dam site under the excitation of natural environment, the operational characteristic information of the dam body is analyzed and compared with the finite element simulation results, and the study shows that the DVMD–VDR method can efficiently extract the complete vibration characteristic information of the structure, which has a good engineering practicability, and it can provide the basis for the on-line monitoring of the structural operational status of the embankment dam.

Abstract

This paper includes a review of the several calculation methods available to calculate the modal damping ratio from dynamic events obtained in structural health monitoring of bridges. A comparative analysis among methods involving the logarithmic decrement, bandwidth applied over the spectrum, adjustment to the theoretical curve and random decrement technique is included. Additionally, an alternative calculation method based on signal spectra reduction has been formulated, whose main advantage lies in the fact that it directly allows the study of multiple-degrees-of-freedom systems, which describe typical structures, without the need for applying filters; this method gives extremely precise values despite simplification of the calculations. First, a comparative analysis is carried out on a set of theoretically simulated waves. In these cases, although there are differences in detail, the obtained results are reasonably close to the theoretical values. Second, an analysis of the registered accelerograms during a test campaign of a real structure, the viaduct “The Arches of Alconétar”, is performed. It has been observed that in this case, unlike what happens with the theoretical simulations, the obtained results significantly vary depending on the selected calculation method. For this reason, it is important to know which method has been used to calculate the modal damping ratio in structural health monitoring systems, as well as to be cautious in setting thresholds.

Existing structures and infrastructures are exposed worldwide to different types of hazards during their service life, such as earthquakes or landslides, especially in countries characterized by high seismicity and hydrogeological risk, as Italy. Mitigation risk and safeguarding existing structures are tasks of great interest for structural engineering. Recently, advanced multi-temporal differential synthetic aperture radar interferometry (DInSAR) products have been used to monitor the evolution in time of ground movement that affects structures. This paper proposes a methodological approach to integrate DInSAR data, visualized in the GIS environment, with on-site measurements. DInSAR and terrestrial laser scanning (TLS) are purposely combined to facilitate the spatial interpretation of displacements affecting cultural heritage sites. An insight into the proposed approach is provided through the study of the Capitoline Museums in Rome (Italy) focusing on Marcus Aurelius Exedra, by exploiting the data archive (ascending and descending acquisitions) collected during the 2012–2020 time interval. Identifying possible critical situations for the analyzed structure is carried out through the analysis of DInSAR-based displacements time series and mean deformation velocity values. Ascending and descending data are combined to extract the components of ground motions and reveal the presence of predominant components in the vertical direction. This is also confirmed by comparing the “as-build” model (obtained from TLS) and the “as-design” model (obtained from the original technical drawing). Therefore, the DInSAR–TLS combination allows supporting structural health monitoring early warning procedures of structures.

Surface crack detection for concrete bridge is a practical but challenging task, owing to the inherent large variety of crack images and the complexity of the background. Many recent approaches formulate crack detection as a pixel-level binary classification problem. However, tiny cracks present a low contrast with the surrounding background, which is hard to be found by current methods. In this paper, the CrackFlux is proposed with a learning-based data-driven methods, which detects cracks via the learning context flux field. In precise, a ConvNets is trained to predict the two-dimensional vector field and each pixel is projected onto candidate crack points. The proposed “context flux field” representation has two major superiorities. First of all, it uses the spatial context of the image points to encode the relative position of the crack pixels. Besides, because the context flux is a region-based vector field, it performs better to tackle cracks with extreme widths. To demonstrate the effectiveness of the proposed method, it is compared with recent state-of-the-art crack detection methods on four datasets under the standard evaluation metric. These experiments demonstrate that the proposed method of “the crack detection via context flux field” exceeds the existing methods and build the new baseline for crack detection.

Machine vision offers distinct advantages, such as enhanced efficiency and precision, in the segmentation and assessment of corrosion on hydraulic steel gates. This study addresses the challenge of demanding a substantial amount of pixel-level annotated data in machine vision-based corrosion segmentation and assessment approaches. To tackle this issue, a novel weakly supervised method for corrosion segmentation and assessment in hydraulic steel gates is proposed, leveraging class labeling. The technique employs a class activation map to pinpoint regions containing corrosion seeds and to train a network to capture semantic affinity relations. Subsequently, the concept of region growing is adopted to propagate semantic information across the entire image. The average feature vector of the seed region serves as the corrosion feature, enabling precise segmentation of corroded areas and circumventing the laborious pixel-level annotation process. Additionally, a fine-grained corrosion classification network is established and trained using salt spray corrosion test data to accurately evaluate the corrosion severity. To validate the proposed method's accuracy, a dataset of steel gate corrosion images is curated based on real-world operational scenes. Experimental results demonstrate that, in practical scenarios, the segmentation method presented in this paper achieves a segmentation intersection ratio of 62.37% in corrosion, without pixel-level annotation. This performance closely approaches the performance of mainstream fully supervised methods. Additionally, the corrosion grade evaluation method proposed in this study achieves an accuracy of 95.77%.

In addressing the challenges of analyzing seismic response data for high-speed railroads, this research introduces a hybrid prediction model combining convolutional neural networks (CNN) and long short-term memory networks (LSTM). The model's novelty lies in its ability to significantly improve the precision of fiber grating monitoring for high-speed railroads. Employing quasi-distributed fiber optic gratings, seven grating monitoring points were strategically placed on each fiber to capture responses of the track plate, rail, base plate, and beam during seismic activities. Using data from peripheral gratings, the model predicts the central point's response. A continuous feature map, formed via a time-sliding window from the rail's acquisition location, undergoes initial feature extraction with CNN. These features are then sequenced for the LSTM network, culminating in prediction. Empirical results validate the model's efficacy, with an RMSE of 0.3753, MAE of 0.2968, and a R² of 0.9371, underscoring its potential in earthquake response analysis for rail infrastructures.

Dam health monitoring is an important method for quantitative evaluation of dam safety. After long-term operation, there have missing data in dam monitoring data series inevitably due to the sensor damage or monitoring system failure problem which seriously affects the correctness of dam safety evaluation. The imputation accuracy of missing value is affected by data decomposition, reconstruction, and prediction methods. Therefore, in view of the high-precision imputation model of missing data in dam health monitoring, this paper proposes a data-driven fusion imputation model based on novel mode decomposition and deep learning method. First, the fusion imputation model is constructed based on extreme-point symmetric mode decomposition (ESMD), permutation entropy (PE), and bidirectional gate recurrent unit neural network (BiGRU). The ESMD-PE data preprocessing module can decompose the original data into a series of stable subsequences which can be input into the advanced deep learning BiGRU model to improve the interpolation accuracy. Then, the types of dam missing data and interpolation steps are studied. The engineering example illustrates that the root mean square error of the proposed model is decreased by 55.32% on average compared with four classical imputation models. The ESMD-PE–BiGRU fusion model can effectively simulate the inherent law of dam monitoring data and predict the missing data, which provides complete monitoring data for dam safety analysis.

Crack monitoring is an important task in structural health monitoring. In this study, a procedure is developed to assess the crack width based on the strain curve of distributed fiber optic sensors (DFOS), taking into account of the strain redistribution of the structural substrate after cracking. Fifteen aluminum alloy plates with two or three pre-cut cracks spaced at varying intervals were installed with DFOS and subjected to a tensile test. During the test, the width of the cracks was measured using an optical microscope. The results revealed that cracks caused a peak value in the strain curve of DFOS, which is dependent on the spacing of the cracks. The peak strains overlap when the cracking spacing is less than 20 mm, as there is a significant strain interference between the two adjacent strain peaks. Depending on the number and location of cracks, thirteen scenarios are classified and a corresponding procedure is proposed to evaluate the crack width by considering the strain redistribution of the cracked substrate. Validation tests demonstrated that the proposed procedure reduced the relative measurement error to 6.64%. Therefore, the developed procedure improves the accuracy of crack width evaluation based on DFOS in practical engineering applications.