Journal of Civil Structural Health Monitoring最新文献

Damage location vector (DLV) method is a model-based structural health monitoring approach that needs the frequency response–function response of the structure. A review of the literature indicates that although the DLV method accurately identifies the damage location in the single-damage structures, it does not work properly for the multi-damage. Accordingly, the aim of this research is to advance the DLV approach to increase its accuracy to detect the damage locations and severities in the multi-damage structures. In this regard, experimental and numerical studies were performed on the two-fixed ends steel beam having multiple damages with different intensities. During laboratory tests, the vibration response of steel beam specimens with multiple defects stimulated by hammer impact was measured. Different sensor locations were considered in the tests. A finite-element model of the steel beam was developed to calculate the dynamic response of undamaged beam under impact loading. Based on the fundamentals of hypothesis testing and data fusion, a threshold was derived to advance the DLV approach to detect the multiple damages. Moreover, the effect of sensor position on the performance of the DLV approach was investigated. The proposed method was also applied to a long-span box-shaped bridge to investigate its accuracy and efficiency for detecting damages in realistic complex structures. Moreover, the results obtained from the advanced DLV method were compared with other conventional methods, considering the effect of noise and different damage scenarios. The findings reveal that the advanced DLV approach proposed in this study accurately detects the defect locations and severities in the structures having multiple damages.

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.