Journal of Civil Structural Health Monitoring最新文献

The present-day road traffic with the persistent change in the type and volume of vehicles needs to be specifically investigated for effective safety management of aging highway infrastructures. Actual traffic data can be implemented in refined procedures for stochastic simulation of road infrastructure performance, structural health monitoring (SHM), definition of weight limits on highways, and traffic-informed structural safety checks. While weigh-in-motion (WIM) systems had been widely used in many countries, their installation on Italian highways was mostly discussed and carried out only after the catastrophic collapse of the Polcevera bridge in 2018. This study presents a statistical data analysis, probabilistic models, and a simulation procedure for highway traffic, based on measurements of two WIM systems located along European route E45 close to Naples, Italy. Different limitations to maximum gross vehicle weight (GVW) were enforced at the locations of the two WIM systems, according to the Italian road code and the Italian guidelines for risk classification, safety assessment and monitoring of existing bridges, respectively. WIM data sets were filtered to exclude erroneous traffic data and vehicle classes defined according to the number of axles and axle distance were statistically characterised, allowing the derivation of probabilistic models for all traffic parameters of interest. A simulation methodology to generate random traffic load from the WIM data is also presented for its possible use in probabilistic performance assessment and traffic informed SHM of road infrastructures such as bridges.

Wire breakage in prestressed cylinder concrete pipes (PCCPs) due to various factors, such as corrosion, hydrogen embrittlement, material defects and overload, may lead to structural failure. Real-time detection of acoustic waves generated by wire breakage is possible using fiber optic sensors. Accurate determination of the first arrival time (FAT) of acoustic wave is vital for localizing wire breakages. A novel method based on the Bayesian optimal detector is proposed to automatically identify the FAT of near-wall acoustic wave. The FATs are subsequently fed into a localization model of wire breakage. The localization results are compared for the FAT of the proposed method and human subjective picking via model tests. The results show that compared with human subjective picking, the wire breakage localization of the proposed method can ensure the accuracy of the results. The maximum errors of the longitudinal and circumferential positions of the proposed method are 0.15 m and 0.02 m, respectively. The experimental results demonstrate that the FATs determined by the Bayesian optimal detector enable the accurate localization of wire breakage with noisy measurements. The proposed method overcomes the limitation of traditional picking methods in determining the FAT, which provides a promising tool for real-time monitoring of wire breakage in PCCPs.

Preliminary assessment of the seismic performance of reinforced concrete (RC) buildings with placards can reduce the number of buildings that require a detailed and costly assessment. Although existing image-processing-based techniques can detect the existence of cracks and spalling in concrete, it remains difficult to define with damage levels of the damaged vertical members based on these techniques. This study aims to fill this gap by exploiting convolutional neural network (CNN) techniques for damage level classification of vertical components in RC buildings. The preliminary seismic assessment approach of existing RC buildings developed by the National Center for Research on Earthquake Engineering, Taiwan is employed in this study, and the residual strength factors for damage levels of vertical members are identified. The proposed CNN technique can estimate the damage levels of the vertical members, and the seismic capacity reduction of these damaged vertical members can be graded accordingly. Hence, the seismic resistance of the RC buildings with damaged members caused by an earthquake can be estimated. The earthquake reconnaissance data collected after recent earthquakes are used to train and validate the CNN network. The performance of the proposed approach is verified using the earthquake data with the necessary information for the preliminary seismic assessment approach. In general, the precision and recall values that we obtain for the identification of the damage in vertical members are acceptable. Based on the results of this study, performing a seismic evaluation of RC buildings by calculating the residual seismic capacity ratio with the help of machine learning appears to be an effective strategy.

Current scouring effects and additives increase the risk of failure in underwater structures, and poor observation complicates the identification and assessment of damage. We present a novel index for assessing non-dispersible underwater concrete columns using stress-wave and impedance. A piezoelectric lead zirconate titanate sensor was used to monitor the compression process of non-dispersible underwater concrete columns and ascertain the extent of damage. The proposed index divides the damage process into initial compaction, elastic deformation, and crack development and failure stages. Additionally, the proposed method quantifies and identifies damage, producing results that agree with those for the axial compression failure characteristics.

Due to the increase in population, urbanisation, transportation development, and the aging of existing bridges, there is a growing need for new and rapid structural health monitoring (SHM) of bridges. To address this challenge, a method that stands out is the use of an interferometric radar system-based device, specifically Image by Interferometric Survey-Frequency for structures (IBIS-FS). Known for its portability and non-intrusive operation, IBIS-FS does not require direct contact with the bridge. This study utilised IBIS-FS to capture a pedestrian bridge’s natural frequencies and mode shapes. The data obtained were found to be consistent with results from finite element models, demonstrating the reliability of IBIS-FS in capturing modal parameters. Building upon this foundation, the study then explores the application of advanced ensemble-based machine-learning techniques. By leveraging the data acquired from IBIS-FS, algorithms such as Random Forest, Gradient-boosted Decision Trees (GBDT), and Extreme Gradient Boosting (XGBoost) are used for bridge damage detection. These machine-learning (ML) techniques are suited to analyse the incomplete modal parameters of bridges, as captured by IBIS-FS. The study focuses on using these algorithms to interpret the changes in modal parameters, specifically identifying damage as a reduction in the stiffness of elements. This approach allows for a comprehensive analysis, where the modal parameters, including mode shapes and natural frequencies altered by varying noise levels, are fed as input to the models. It was observed that all three ML methods, with Random Forest in particular, can effectively identify the location and severity of damage, demonstrating an efficient training process. The robustness of GBDT and XGBoost in handling complex data sets also shows great promise for their application in bridge damage detection. Collectively, these results underscore the potential of combining advanced ML techniques like Random Forest, GBDT, and XGBoost with the data acquired from IBIS-FS.

Structural capacity evaluation is essential to support the safety assessment and decision-making process of existing building structures after disastrous earthquakes. Current post-earthquake evaluation practices rely more on manual on-site inspections, which are labor-intensive and subjective. A simulation-based capacity evaluation could be a desired alternative when numerical models for these buildings are prior-identified and updated using structural health monitoring data. This study proposes a procedure for identifying the capacity curve and assessing the residual capacity of existing structures using seismic monitoring data. The mass-normalized spectral acceleration-displacement (AD format) relation is first defined in a single-degree-of-freedom system. Considering the post-event deterioration of structural capacity, a data-driven reduction factor for the capacity curve is introduced to quantify the potential structural degradation. With the aid of the updated capacity curve, the residual capacity of the earthquake-damaged structure is then predicted via incremental dynamic analysis. The feasibility and accuracy of the proposed method are analyzed via numerical simulations and further validated using a large-scale shaking table test and a real-world instrumented building. Results show that the proposed method could identify the capacity curve of the existing structure from seismic monitoring data and estimate the hysteresis responses with a favorable agreement. It could provide the residual capacity of the target structure and quantify its capacity reduction, which can informatively facilitate the post-earthquake structural safety management.

Longitudinal joints are the most vulnerable parts of prefabricated utility tunnels, susceptible to damage from external forces and foundation settlement. Currently, the shear mechanical properties of prefabricated double-cabin utility tunnel joints are unclear, preventing the evaluation of the structural or joint safety of utility tunnels. The shear mechanical response and failure characteristics of the joints of prefabricated double-cabin utility tunnels are investigated by combining model testing with numerical simulation. The results indicate that the shear deformation of utility tunnel joints can be categorized into elastic, crack propagation, and damage stages. In the course of joint-shear deformation, the middle utility tunnel sustains centrosymmetric failure. The degree of deformation of the large cabin is greater than that of the small cabin, while the damage to the small cabin is more severe. When the utility tunnel is subjected to the same load, the joint dislocation under the gravelly sand foundation is the smallest, but the damage range is the largest and the cracks are the most. Local strengthening and protection are needed at the chamfer, near the bolt hole, and the top and bottom. The stratum conditions have little effect on the shear stiffness of the joint during the elastic stage, but they have a significant impact during the crack propagation and damage stages. Finally, the joint damage area is approximately 15% of the total utility tunnel, and the deformation region of the longitudinal connectors is approximately 16% of its length.

This paper proposes a new complex background segmentation method based on the modified fully convolutional network semantic segmentation for noncontact cable vibration frequency estimation. The estimation of frequency from video data is challenged by the presence of background object motion, which directly impacts the accuracy of the video-based method. To address this issue, image tests were carried out among the existing model (U2-Net) to explore the effect of the efficient channel attention (ECA) and convolutional block attention module (CBAM) on cable segmentation performance. As a result, a relative optimal model was identified. This modified model was then used to remove the complex background, while retaining the vibration signals specific to the cable. Subsequently, phase matrices encoding cable vibration were calculated using a phase-based motion estimation algorithm at various cable locations. The modal response of the cable vibration was estimated using the complexity pursuit (CP) algorithm from the segmented video. Finally, the vibration frequency of the cable was estimated. The proposed method was validated on a small-scale cable model. The results are in good agreement with the values sampled by the accelerometer, with an average relative error of 4.50%. This estimation shows the significant potential of the proposed method in structural health monitoring.

This study aims to assess the future structural performance of the Kozlu-Ulutan clay core rockfill (CCR) dam, one of the most significant water structures in the Black Sea region of Turkey, by utilizing 35 years of levelling measurements and 3D finite-difference analyses. Settlement measurements were obtained from five different points on the dam surface every 6 months. Subsequently, a three-dimensional (3D) model of the dam was created using the finite-difference method. Time-dependent creep analyses and seismic analyses were conducted sequentially, employing the Burger-Creep and Mohr–Coulomb material models, respectively. Non-reflecting boundary conditions were defined for the boundaries of the dam model. The 3D numerical analysis results were found to be highly compatible with the 35 years of levelling measurements. Additionally, the future seepage and settlement behaviors of the dam over a 100-year period (2023–2123) were analyzed, considering special time functions. Current and future seismic analyses were performed, taking into account the settlement results of the dam in 2023 and 2123. For seismic analyses, data from ten various earthquakes that occurred in Kahramanmaraş, Hatay, Malatya, and Gaziantep in Turkey in 2023 were utilized. The seismic analysis results provided significant information about the future seismic behavior of the Kozlu-Ulutan Dam, revealing notable differences between the current and future earthquake behaviors of the dam. Moreover, it was concluded that the clay core is the most crucial section concerning the current and future seismic behaviors of CCR dams. The study results emphasized the importance of continuous monitoring and periodic seismic evaluations for the safe operation of CCR dams.

Bridge authorities have been reticent to integrate structural health monitoring into their bridge management systems, as they do not have the financial and technical resources to collect long-term monitoring data from every bridge. As bridge authorities normally own huge amount of similar bridges, like the pedestrian ones, the ability to transfer knowledge from one or a small group of well-known bridges to help make more effective decisions in new bridges and environments has gained relevance. In that sense, transfer learning, a subfield of machine learning, offers a novel solution to periodically evaluate the structural condition of all pedestrian bridges using long-term monitoring data from one or more pedestrian bridges. In this paper, the applicability of unsupervised transfer learning is firstly shown on data from numerical models and then on data from two similar pedestrian prestressed concrete bridges. Two domain adaptation techniques are used for transfer learning, where a classifier has access to unlabeled training data (source domain) from a reference bridge (or a small set of reference bridges) and unlabeled monitoring test data (target domain) from another bridge, assuming that both domains are from similar but statistically different distributions. This type of mapping is expected to improve the classification accuracy for the target domain compared to a procedure that does not implement domain adaptation, as a result of reducing distributions mismatch between source and target domains.