Dongdong Chen

In response to the challenge of changes in the physical and mechanical properties of red sandstone when it comes into contact with water during construction projects, this paper proposes a moisture content detection method for red sandstone based on the knocking method. Taking red sandstone as the research object, this study explores a moisture content detection approach by combining the knocking method with Convolutional Neural Network and Support Vector Machine algorithms (CNN-SVM). Specifically, this research involves knocking the surface of red sandstone specimens with a knocking hammer and precisely capturing the acoustic signals generated during the knocking process using a microphone. Subsequently, an effective detection of the moisture content in red sandstone is achieved through a method based on feature extraction from knocking sound signals and a Convolutional Neural Network classification model. This method is easy to operate. By utilizing modern signal processing techniques combined with the CNN-SVM model, it enables accurate identification and non-destructive testing of the moisture content in red sandstone even with small sample datasets. Mel Frequency Cepstral Coefficients (MFCCs) and Continuous Wavelet Transform (CWT) were separately used as features for detecting red sandstone specimens with different moisture contents. The detection results show that the classification accuracy of red sandstone moisture content using MFCCs as the feature reaches as high as 94.4%, significantly outperforming the classification method using CWT as the feature. This study validates the effectiveness and reliability of the proposed method, providing a novel and efficient approach for rapid and non-destructive detection of the moisture content in red sandstone.

The debonding failure of the glass fiber reinforced plastic (GFRP) rock bolt structure has become one of the most important failures. The accuracy of debonding status detection is very important to ensure the safety of engineering structure. Due to the complex factors such as groundwater infiltration and soil intrusion in the actual engineering environments, the debonding void may be filled with water, sand and other media. At present, the debonding void in the study of debonding status is mainly air, so the influence of different media on debonding status detection is explored. This study aims to explore how different media in the debonding void affect the detection of GFRP rock bolt debonding. Initially, the debonding detection model of GFRP rock bolt structure is simplified, and the influence of different debonding void media on debonding detection is analyzed. Secondly, the numerical simulation test of the influence of debonding void medium on debonding detection is carried out. Then, the theoretical calculation and numerical simulation experiment are verified by laboratory test. Lastly, using water and seawater, we examined how the volume of water content affects the detection of debonding status. It is found that the medium in the void affects the signal amplitude. As the medium’s wave impedance increases, the signal amplitude decreases. The highest amplitude of the focused signal is air, which is 0.0812 V, the lowest amplitude of the focused signal is sand, which is 0.0717 V, and the amplitude of the focused signal is attenuated by 11.7%. This is related to the interface of wave impedance change on the propagation path. The closer the wave impedance on both sides, the greater the influence of the focused signal. The increase of water content volume leads to the increase of wave impedance on the right side of the interface and the decrease of the amplitude of the focused signal. This study reveals the medium’s influence on debonding detection, crucial for enhancing accuracy, adaptability, and preventing hidden failures in the anchorage system.

This paper proposes a novel approach for monitoring multi-bolt looseness using guided waves and the cross-correlation of the wavelet energy envelope. By assessing variations in the wave packet, the looseness in multi-bolt assemblies can be estimated. First, the dispersion effects of Lamb waves were theoretically analyzed using the Rayleigh–Lamb equation. Next, the wavelet energy was derived through wavelet transform, and the Lamb wave envelope was obtained as a criterion for accurately separating the wave packet. Cross-correlation analysis was employed to quantitatively evaluate the dispersion of wave packets for varying levels of bolt looseness. A looseness index, termed the normalized decorrelation coefficient of wavelet energy (NDCWE), was defined. Then, validation experiments were conducted using a joint with five M8 bolts, each tightened to a standard torque of 42 N·m. Two piezoelectric transducers were attached to the periphery of the bolt group. Three preload conditions were tested for each bolt: fully tightened, 80% of the standard torque, and 10% of the standard torque, corresponding to no looseness, minor looseness, and significant looseness, respectively. Results showed that when significant looseness occurs, the NDCWE value exceeds 0.4, confirming the effectiveness of NDCWE in detecting substantial reductions in bolt preload. Experiments assessing the effect of temperature revealed that temperature has a negligible effect on the waveforms of the S ₀ and A ₀ mode waves. Finally, to quantitatively evaluate the efficiency of the ultrasonic transducers, the bolt-to-sensor ratio (BSR) was introduced. In this study, the BSR reached 2.5, indicating that a single piezoelectric transducer can monitor the preload of 2.5 bolts. The proposed approach shows great potential for multi-bolt looseness monitoring.

The root mean square deviation is a common way to measure the electromechanical impedance of piezoceramic transducers that are used for traditional preload monitoring. However, due to the impedance signal’s high sensitivity to temperature, most current research is carried out under the same temperature conditions to avoid its effects. Even so, it is impossible to ignore the temperature factor in practical engineering, which hinders the application of impedance technology. Therefore, this paper proposes a novel approach for preload monitoring in actual engineering: using a wireless impedance signal acquisition platform to collect long-distance impedance signals and then establishing a predicted model based on machine learning (ML) considering the temperature. The wireless impedance signal acquisition platform includes a smart washer, a piezoelectric impedance wireless sensing device, and the host computer software. This test established a dataset under various operating conditions and developed a machine-learning-based predictive model. After comparing five typical ML algorithms, it was discovered that XGBoost performed better in prediction accuracy and generalization ability. Moreover, the Shapley additive explanation method is utilized to further analyze and interpret the XGBoost model. It indicates that ML primarily relies on numerical features (such as the area of each subinterval) to identify the impedance signal and predict the prestress, whereas information features (such as temperature value, peak, etc.) have little influence on the model’s output. Finally, the results above demonstrate that the ML-based models can predict the preload at different temperatures, effectively reducing temperature interference.

A bolt may undergo a process from slight preload perturbation to significant preload reduction during its service life, it is a challenge to simultaneously detect both slight and significant bolt looseness based on the same waveform. Previous studies were prone to use direct wave or coda waves individually, which is suitable for a narrow range of bolt preload and cannot realize the full-range bolt preload reduction. To address this challenge, this article proposes a novel full-range bolt preload monitoring approach with multi-resolution using the time shifts of the direct wave and coda waves. Due to the difference in propagation paths, preload reduction induced time shift varies considerably between the direct wave and coda waves. By calculating the time shifts of the direct wave and coda waves, bolt looseness, including the slight preload perturbation and the significant preload reduction, can be simultaneously estimated. Based on the acoustoelastic effect and the wave path summation, a theoretical model is established, and the monitoring principles are presented. The analytical results show that the time shifts of the direct wave and coda waves are proportional to the preload variations. Validation experiments are carried out, and the results show that at specific preloading levels, the ultimate detectable resolutions of bolt preload (DRBP) using direct wave and coda waves are 1.68% and 0.32%, respectively. Therefore, the ultimate DRBP of the direct wave is 5.25 times that of the coda waves, indicating that the coda waves can be used as the “probe” with high resolution to detect tiny perturbations of preload, and the direct wave can function as a low-resolution “probe” to monitor significant preload deterioration. By calculating the time shifts of the direct wave and the coda waves, bolt looseness with different degrees can be monitored simultaneously.

In this paper, a novel method to analyze the mechanical behavior of the coin-tap test using the principle of pounding mechanics is proposed. Based on the Hertzian contact theory, the linearized contact law is applied to simplify the mathematical model of the coin-tap test. By modeling the stiffness of the defect as an equivalent spring in a serial connection with the contact stiffness, the impact force and impact time can be theoretically derived. The derived expressions show that, the closer the defect is to the surface of the structure, the longer the impact duration time and the smaller the impact force will be. In addition, with the increase of defect radii, the peak force and duration of the impact will decrease and increase, respectively. Another innovation of this paper is that an equivalent impact stiffness (EIS)-based damage index (DI) is proposed to reveal the severity and location of the near-surface damages. Two validation experiments were carried out to validate the influences of the damage’s radii and under-surface depths. In the first experiment, the impact force and time of the central point for eight plates are measured to validate the accuracy of the proposed model. In the second experiment, 18 points on the horizontal centerline of eight specimens were tapped at an interval of 1 cm. The recorded pounding force and time were processed by EIS-based DI. Experimental results show that the EIS-based DI can effectively predict the position and severity of the defects and it holds the potential for practical application.