Journal of Civil Structural Health Monitoring最新文献

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.

During the construction of a new tunnel overcrossing existing tunnels at close proximity, the existing tunnels should be protected by protective structures and/or ground improvement measures. However, the construction of these structures and ground improvement may cause movement or deformation to the existing tunnels, potentially jeopardizing their operational safety, particularly under soft soil and sensitive ground conditions. This study presents the results of a year-long field monitoring program focusing on the movement of two underlying subway tunnels during different construction phases of an overcrossing cut-and-cover tunnel. Protective structures/measures for the existing subway tunnels included the construction of H-pile walls, deep soil mixing, cast-in-situ bored piles, and staged excavation for the new tunnel. In terms of construction-induced movement to the existing subway tunnels, it was found that the construction of H-pile walls induced the largest vertical settlement, the deep soil mixing operations induced the largest horizontal displacements, and the staged excavation induced the largest uplift. Although the maximum horizontal displacement at the springline of a subway tunnel near the center of the construction area slightly exceeded the alarm value, the implemented protective structures/measures were effective in reducing the total horizontal and vertical displacements of the existing tunnels.

Damages to various building structures often occur over their service life and can occasionally lead to severe structural failures, threatening the lives of its residents. In recent years, special attention has been paid to investigating various damages in buildings at the early stage to avoid failures and thereby minimize maintenance. Structural health monitoring can be used as a tool for damage quantification using vibration measurements. The application of various sensors for measuring accelerations, velocity and displacement in civil infrastructure monitoring has a long history in vibration-based approaches. These types of sensors reveal dynamic characteristics which are global in nature and ineffective in case of minor damage identification. In a practical application, the available damage detection approaches are not fully capable of quickly sensing and accurately identifying the realistic damage in structures. Research on damage identification from strain data is an interesting topic in recent days. Some work on the cross-correlation approach is now a centre of attraction and strictly confined to bridge or symmetric structures. The present paper uses strain data to validate the cross-correlation approach for detecting damage to building structures. The effectiveness of the methodology has been illustrated firstly on a simply supported beam, then on a 5-storey steel frame and a 6-storey scaled-down reinforced concrete shear building and lastly on a frame structure with moving load as a special case. The results show that this approach has the potential to identify damages in different kinds of civil infrastructure.

Convenient and helpful defect information within the magnetic field signals of an energy pipeline is often disrupted by external random noises due to its weak nature. Non-destructive testing methods must be developed to accurately and robustly denoise the multi-dimensional magnetic field data of a buried pipeline. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is an innovative technique for decomposing signals, showcasing excellent noise reduction capabilities. The efficacy of its filtration process depends on two variables, namely the level of additional noise and the number of ensemble trials. To address this issue, this paper introduces an adaptive geomagnetic signal filtering approach by leveraging the capabilities of both CEEMDAN and the salp swarm algorithm (SSA). CEEMDAN generates a sequence of intrinsic mode functions (IMFs) from the measured geomagnetic signal based on its initial parameters. The Hurst exponent is then applied to distinguish signal IMFs and reproduce the primary filtered signal. SSA fitness, representing its peak value (excluding the zero point) in the normalized autocorrelation function, is utilized. Ultimately, optimal parameters that maximize fitness are determined, leading to the acquisition of their corresponding filtered signal. Comparative tests conducted on multiple simulated signal variants, incorporating varied levels of background noise, indicate that the efficacy of the proposed technique surpasses both EMD denoising strategies and conventional CEEMDAN approaches in terms of signal-to-noise ratio (SNR) and root mean square error (RMSE) assessments. Field testing on the buried energy pipeline is performed to showcase the proposed method’s ability to filter geomagnetic signals, evaluated using the detrended fluctuation analysis (DFA).

In this study, a method of finite element model updating is proposed to quantitatively identify bridge boundary constraints using the high-resolution mode shapes of a bridge. The high-resolution mode shapes are first identified from the responses measured by few randomly distributed sensors using the compressive sensing theory, which is innovatively implemented in the spatial domain with a proposed basis matrix. To speed up finite element updating, the frequency and modal assurance criterion Kriging models are then established to approximate the implicit relation between boundary constraints and bridge modal parameters including frequencies and mode shapes, serving as surrogate models for the bridge finite element model. By adopting the surrogate models in finite element updating, the objective functions of frequencies and mode shape indicators are optimized by a multi-objective genetic algorithm. The numerical examples as well as an actual laboratory experiment have shown that the mode shapes and boundary constraints of a bridge can be identified precisely and efficiently by the proposed method, even for a continuous and variable cross-sectional bridge.