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This paper proposes a flexible eddy current TMR (FEC-TMR) sensor to monitor the internal crack of metal joint structures. First, the finite element model of the FEC-TMR sensor is established to analyze the influence of the sensor’s crack identification sensitivity with internal crack propagation at different depths and determine the optimal location and exciting frequency of the sensor. Then, the optimal longitudinal spacing and exciting frequency of the sensor are tested by experiment. The experimental results are consistent with the simulation results, which verify the correctness of the simulation model. Finally, the experiment is carried out for internal cracks of different depths to verify that the sensor can monitor internal cracks, and the crack identification sensitivity gradually decreases with the increase in the depth of the crack from the surface.

Structural health monitoring is an important aspect of maintenance for bridge columns in areas of high seismic activity. In this project, recently developed piezoceramic-based transducers, known as smart aggregates (SA), were utilized to perform structural health monitoring of a reinforced concrete (RC) bridge column subjected to pseudo-dynamic loading. The SA-based approach has been previously verified for static and dynamic loading but never for pseudo-dynamic loading. Based on the developed SAs, an active-sensing approach was developed to perform real-time health status evaluation of the RC column during the loading procedure. The existence of cracks attenuated the stress wave transmission energy during the loading procedure and reduced the amplitudes of the signal received by SA sensors. To detect the crack evolution and evaluate the damage severity, a wavelet packet-based structural damage index was developed. Experimental results verified the effectiveness of the SAs in structural health monitoring of the RC column under pseudo-dynamic loading. In addition to monitoring the general severity of the damage, the local structural damage indices show potential to report the cyclic crack open-close phenomenon subjected to the pseudo-dynamic loading.

This paper shows how 2D digital image correlation (2D DIC) and region-based convolutional neural network (R-CNN) can be combined for image-based automated monitoring and assessment of surface crack development of concrete structural elements during laboratory quasi-static tests. In the presented approach, the 2D DIC-based monitoring enables estimation of deformation fields on the surface of the concrete element and measurements of crack width. Moreover, the R-CNN model provides unmanned simultaneous detection and localization of multiple cracks in the images. The results show that the automatic monitoring and evaluation of crack development in concrete structural elements is possible with high accuracy and reliability.

This study proposes a method that combines Histogram of Oriented Gradients (HOG) feature extraction and Extreme Gradient Boosting (XGBoost) classification to resolve the challenges of concrete crack monitoring. The purpose of the study is to address the common issue of overfitting in machine learning models. The research uses a dataset of 40,000 images of concrete cracks and HOG feature extraction to identify relevant patterns. Classification is performed using the ensemble method XGBoost, with a focus on optimizing its hyperparameters. This study evaluates the efficacy of XGBoost in comparison to other ensemble methods, such as Random Forest and AdaBoost. XGBoost outperforms the other algorithms in terms of accuracy, precision, recall, and F1-score, as demonstrated by the results. The proposed method obtains an accuracy of 96.95% with optimized hyperparameters, a recall of 96.10%, a precision of 97.90%, and an F1-score of 97%. By optimizing the number of trees hyperparameter, 1200 trees yield the greatest performance. The results demonstrate the efficacy of HOG-based feature extraction and XGBoost for accurate and dependable classification of concrete fractures, overcoming the overfitting issues that are typically encountered in such tasks.

Accurate evaluation of the influence range of borehole hydraulic fracturing (HF) in coal seam is crucial for optimizing the design scheme of HF. In this study, we adopted the microseismic (MS) monitoring technology to monitor and characterize the spatial shape of cracks caused by borehole HF in coal seam in an underground coal mine. And we also tested and analyzed the stress and moisture content changes of coal mass at different distances from the borehole after HF program. The number of MS waveforms and the energy of MS events show a good positive correlation with the water pressure curve. The response of the energy curve to the extension of the hydraulic cracks is ahead of the water pressure curve. Based on the short-time average/long-time average (STA/LTA), the interference signal recognition method (ISR) and the improved Akaike information criterion (AIC) method, we developed a comprehensive MS event detection and arrival time picking (CMDP) program that are suitable for the weak MS signals with low signal to noise ratio (SNR) induced by HF in coal seam. And then we were able to more accurately locate the MS events of the hydraulic cracks using the simplex source location method. We have conducted a comprehensive analysis of the relationship between the temporal and spatial distribution of MS events and hydraulic cracks propagation. The results show that there is an apparent correlation between the MS activities and HF operation. The expansion of hydraulic cracks generates MS events, and the larger the size of the cracks, the greater the energy of MS events. Based on the MS monitoring results, the HF produces a crack network of flat ellipsoid in the No. 6 coal seam, which indicates that there is obvious stimulated reservoir volume (SRV) fracturing effect during the borehole HF process. The influence radius of HF based on the moisture content is the smallest (about 20 m), followed by the stress monitoring (about 30 m), and the MS monitoring is the largest (about 40 m). The high-precision MS source location (location errors < 2.5 m) results combined with roadway roof watering phenomenon indicate that the influence radius of HF based on the moisture content and stress release may be underestimated. And the effective influence radius of the borehole HF is about 40 m for this HF program.

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.

The effect of combining filler (carbon black) and fibrous materials (steel fiber and polypropylene fiber) with various sizes of coarse particles on the post-cracking behavior of conductive concrete was investigated in this study. Steel fibers (SF) and carbon black (CB) were added as monophasic, diphasic, and triphasic materials in the concrete to enhance the conductive properties of reinforced concrete. Polypropylene fiber (PP) was also added to steel fiber and carbon to improve the post-cracking behavior of concrete beams. This research mainly focused on the effects of macro fibers on toughness parameters and energy absorption capacity, as well as enhancing the self-sensing of multiple cracks and post-cracking behavior. Fractional changes in resistance and crack opening displacement (COD-FCR) and the relationship of load-deflection-FCR with different coarse aggregates of (5–10 mm and 15–20 mm) sizes were investigated, and the law of resistance signal changes with single and multiple cracking through load-time-FCR curves was explored. Results indicated that the smaller size coarse aggregates (5–10 mm) showed higher compressive strength: up to 8.3% and 14.83% with diphasic (SF + CB), respectively. The flexural strength of PC-10 increased 22.60 and 51.2%, respectively, with and without fibers, compared to PC-20. The diphasic and triphasic conductive material with the smaller size of aggregates (5–10 mm) increased the FCR values up to 38.95% and 42.21%, respectively, as compared to those of greater size coarse aggregates (15–20 mm). The hybrid uses of fibrous and filler materials improved post-cracking behavior as well as the self-sensing ability of reinforced concrete.

Crack monitoring in concrete structures is essential to maintaining structural integrity. Therefore, this paper proposes a mobile ground robot equipped with a 2-DoF manipulator and stereo vision sensors for autonomous crack monitoring and mapping. To facilitate crack detection over large areas, a 2-DoF motorized manipulator providing linear and rotational motions, with a stereo vision sensor mounted on the end effector, was deployed. In combination with a manual rotation plate, this configuration enhances accessibility and expands the field of view for crack monitoring. Another stereo vision sensor, mounted at the front of the robot, was used to acquire point cloud data of the surrounding environment, enabling tasks such as SLAM (simultaneous localization and mapping), path planning and following, and obstacle avoidance. Cracks are detected and segmented using the deep learning algorithms YOLO (You Only Look Once) v6-s and SFNet (Semantic Flow Network), respectively. To enhance the performance of crack segmentation, synthetic image generation and preprocessing techniques, including cropping and scaling, were applied. The dimensions of cracks are calculated using point clouds filtered with the median absolute deviation method. To validate the performance of the proposed crack-monitoring and mapping method with the robot system, indoor experimental tests were performed. The experimental results confirmed that, in cases of divided imaging, the crack propagation direction was predicted, enabling robotic manipulation and division-point calculation. Subsequently, total crack length and width were calculated by combining reconstructed 3D point clouds from multiple frames, with a maximum relative error of 1%.

Multilayer metallic connection structures assembled via bolts connecting multiple components represent one of the most common structural configurations in aircraft. Monitoring cracks in inner layers for multilayer metallic connection structures has consistently posed one of the most challenging issues in the field of structural health monitoring (SHM). This paper proposes a novel low‐frequency magnetosensitive (LFMS) sensor, comprising tunneling magnetoresistance (TMR) chips and planar excitation coils, to detect concealed internal cracks by capturing magnetic field variations. A finite element model of the multilayer metallic connection structure and the sensor was developed to analyze the influence of excitation frequency and axis gap on sensor output. Simulation results revealed an optimal configuration at 2.8 mm axis gap and 1.5 kHz excitation frequency, maximizing sensor sensitivity. The LFMS was fabricated and tested under various excitation conditions (0.5, 1.5, and 1.9 kHz). Experimental findings showed that the sensor output increased as the internal crack approached the coil edge and decreased beyond it. When the length of the inner structural crack reached 3 mm, the sensor signal began to show noticeable changes, indicating that the minimum detectable crack length for the sensor is 3 mm; when the crack length reached about 7 mm, the sensor output signal reached its maximum value. The maximum value of the sensor’s signal at 1.5 kHz was 27.1% and 45.5% higher than at 0.5 kHz and 1.9 kHz, respectively. These results validated the simulation model and demonstrated the LFMS sensor’s high sensitivity to crack progression. Therefore, compared to other SHM sensors, the LFMS sensor developed in this study enables timely detection of inner‐layer structural cracks in multilayer metallic connection structures.

In this study, we present an alternative solution for detecting crack damages in rotating shafts under torque fluctuation by directly estimating the reduction in torsional shaft stiffness using the adaptive extended Kalman filter (AEKF) algorithm. A dynamic system model of a rotating shaft for designing AEKF was derived and implemented. An AEKF with a forgetting factor (λ) update was then designed to effectively estimate the time-varying parameter (torsional shaft stiffness) owing to cracks. Both simulation and experimental results demonstrated that the proposed estimation method could not only estimate the decrease in stiffness caused by a crack, but also quantitatively evaluate the fatigue crack growth by directly estimating the shaft torsional stiffness. Another advantage of the proposed approach is that it uses only two cost-effective rotational speed sensors and can be readily implemented in structural health monitoring systems of rotating machinery.