Journal of Civil Structural Health Monitoring最新文献

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).

The study presents the development of a structural monitoring system installed in a 45-m-high steel wind tower located in Italy. The installed monitoring system was composed by 16 strain gauges placed in the tower wall, in a pattern of four Wheatstone bridges at 45°, together with thermal couples, at 21 m from the ground (half-height of the tower). Moreover, several accelerometers were placed along the tower height (with one of them located next to the strain gauges). The wind velocity and directions were obtained directly from the turbine own monitoring system. Such a monitoring system was designed because, due to the decrement of the total height from the original design, the tower suffers from resonance problems. In fact, the investigated tower was originally designed with 65 m of height but then, to comply with local regulations, the height was decreased to the actual size. Therefore, to allow safe operation and avoid excessive fatigue due to the increased displacements, the velocity of the rotor has been manually limited causing an important reduction in the energy production. The results of the study show the importance of monitoring the resonance issue. The differences between the damage indexes obtained with two different working conditions are discussed: tower working with limited operational capacity and tower working at its maximum capacity (in resonance).

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.

The failure of non-structural components after an earthquake is among the most expensive earthquake-incurred damage, and may also have life-threatening consequences, especially in public buildings with very crowded facilities, because exposition is high and the risk increases accordingly. The assessment of existing non-structural components is particularly complex because in-depth in situ investigation is necessary to detect the presence of deficiencies or damage. This problem concerns interior and exterior partitions made of various materials (e.g., glass and masonry), as well as equipment and facilities in construction (building, industry, and infrastructure). Defining the boundary conditions of these components is of paramount importance. Indeed, external restraints (i) affect dynamic properties and, thus, the action experienced during an earthquake, and (ii) influence the capacity to detach the component before failure from the bearing structure (e.g., an infill wall connected to the main structural frame, or equipment connected to secondary structural members such as floors). The authors, therefore, conducted environmental vibration tests of an infill wall and refined a finite element model to simulate typical damage scenarios to be implemented on the wall. Selected damage scenarios were then artificially realized on the existing infill and further ambient vibration tests were performed to measure the accelerations for each of them. Finally, the authors used these accelerations to detect the damage by means of established OMA, as well as innovative machine learning techniques. The results showed that convolutional variational autoencoders (CVAE), coupled with a one-class support vector machine (OC-SVM), identified the anomaly even when the OMA exhibited limited effectiveness. Moreover, the machine learning procedure minimizes human interaction during the damage detection process.

Semantic image segmentation is extensively used for automatic concrete crack detection. In previous studies on semantic image segmentation, concrete images were usually labeled as crack and noncrack zones, and recognition models were then trained using artificial neural networks. However, there is not enough edge information in concrete images for the trained model to identify effectively fine concrete cracks (widths < 0.1 mm). Furthermore, complex backgrounds in concrete images can cause false detections. To improve efficiency and reduce false detections, this study develops a three-stage automatic crack-width identification method for fine concrete cracks. First, a full crack skeleton information identification based on image segmentation is proposed. The performance of the mainstream image segmentation architectures, PSP-Net, Seg-Net, U-Net, and Res-Unet, are compared and analyzed, demonstrating that the Res-Unet-based crack skeleton segmentation is the most accurate at fine-crack detection and able to solve the information loss problem that occurs when learning the imbalanced data of fine concrete cracks. Second, a fractal dimension (FD)-based false detection removal process is applied to discriminate true cracks and false detections. The results show that false detections (line-like curves, shadows, and surface stains) can be removed, increasing the matching rate from 0.6476 to 0.8351. Finally, the FD features of the crack skeleton with maximum widths < 0.1 mm, crack widths in the range of 0.1–0.2 mm, and crack widths > 0.2 mm are calculated. Findings illustrate that the values of the FD feature for the three crack-width ranges are suitable for quantitative characterization of identified crack widths.

The effectiveness of tunnel monitoring is a challenging task due to the limitations of monitoring gauges and lack of monitoring sections. To address this, a novel theoretical analysis-based monitoring method for tunnel structures was proposed in this study. A theoretical approach was employed to establish the correlation between external loads and structural stress–strain response in tunnel lining during grouting and stability periods. A method has been developed to derive the distribution of external loads and internal forces throughout the entire tunnel using strain monitoring at specific locations on the structure. This method has been further validated through a case study of the Liucun Tunnel, providing insights into the accuracy of the monitoring approach. It is found that during the grouting period, the segment ring is surrounded by grout, resulting in peak external loads and internal forces. As the tunnel lining enters the load stability period, both the external loads and internal forces gradually decrease and stabilize. Comparing the results of the monitored method for deriving tunnel external loads, structural bending moments and axial forces with the on-situ measurements, the new monitoring method yields errors in the response of tunnel external loads and internal forces. The average error in external loads is less than 12%, the average error in bending moments is less than 20%, and the average error in axial forces is less than 8%. The proposed monitoring method effectively addresses the issue of long-term failure of monitoring elements due to its replaceability. Additionally, utilizing theoretical methods for derivation allows obtaining more tunnel structural information based on limited monitoring data from the elements. This provides a new approach for long-term structural health monitoring. To address the existing errors in the monitoring method described in this study, the accuracy can be further improved by optimizing the model, incorporating more advanced monitoring techniques, and implementing standardized and improved construction practices.

Numerous studies have investigated the long-term monitoring of natural frequencies, primarily focusing on medium–large highway bridges, using expensive monitoring systems with a large array of sensors. However, this paper addresses the less explored issue of monitoring a footbridge, examining four critical aspects: (i) sensing system, (ii) frequency extraction method, (iii) data modelling techniques, and (iv) damage detection. The paper proposes a low-cost all-in-one sensor/logger unit instead of a conventional sensing system to address the first issue. For the second issue, many studies use natural frequency data extracted from measured acceleration for data modelling, the paper highlights the impact of the input parameters used in the automated frequency extraction process, which affects the number and quality of frequency data points extracted and subsequently influences the data models that can be created. After that, the paper proposes a modified PCA model optimised for computational efficiency, designed explicitly for sparse data from a low-cost monitoring system, and suitable for future on-board computation. It also explores the capabilities and limitations of a data model developed using a limited data set. The paper demonstrates these aspects using data collected from a 108 m cable-stayed footbridge over several months. Finally, the detection of damage is achieved by employing the one-class SVM machine learning technique, which utilises the outcomes obtained from data modelling. In summary, this paper addresses the challenges associated with the long-term monitoring of a footbridge, including selecting a suitable sensing system, automated frequency extraction, data modelling techniques, and damage detection. The proposed solutions offer a cost-effective and efficient approach to monitoring footbridges while considering the challenges of sparse data sets.