A weak interlayer is the key factor in controlling slope stability. It is of great significance to effectively identify the weak interlayer in the study of spatial and temporal distribution law and the internal structure characteristics of a landslide. Considering the limitations of traditional optical imaging and wave speed test methods, this paper presents a weak interlayer identification method based on borehole photo-acoustic combination measurement. By using the combination of optical imaging and acoustic wave scanning, the multi-source data collection of borehole rock wall and borehole surrounding rock is realized, which effectively captures the comprehensive response characteristics of the weak interlayer. This paper first constructs a multi-source data acquisition technology based on the borehole photo-acoustic combination measurement to realize the visualization of the image information and acoustic data of the target area on the borehole rock wall. Subsequently, the optical image features and the acoustic response characteristics of the weak interlayer are clarified based on the optical image and the acoustic scanning data. The hole wall texture characteristic response function, hole wall integrity characteristic response function, hole wall acoustic characteristic response function and hole wall contour characteristic response function are constructed. Finally, the landslide weak interlayer identification method considering the texture characteristics, complete characteristics, acoustic response characteristics and contour characteristics of the borehole rock wall is proposed, which effectively distinguishes the types of rock mass structural surface and realizes the automatic identification of the weak interlayer. Combined with the case analysis, the correctness and reliability of the present method are verified. The results show that the method can identify the weak interlayer and provide scientific basis for landslide management, which can provide a feasible and effective new way to identify the landslide weak interlayer in practical engineering, with a good application prospect and promotion value.

The internal cracks of concrete are very important in the safety evaluation of structures, but there is a lack of fine characterization methods at present. Borehole cameras are a piece of in situ borehole detection technology which can measure the structural elements of a borehole wall with high precision. In this paper, borehole camera technology is used to measure the concrete cracks of a tunnel floor, and the morphological characteristics (depth, width, and orientation) of the cracks are analyzed. The results show that the average extension depth of the crack extending from the orifice exceeds 1.195 m, and the width decreases with the increase in depth. The crack orientation is basically stable, with the maximum deviation of 19° at the orifice of different boreholes and 30° at different depths of the same borehole. The crack inside the concrete (not extending to the orifice) usually has a small extension depth and a relatively stable width, but the crack orientation changes greatly. The coarse aggregate and concrete interface have different effects on the extension direction of cracks. This paper also conducted a second measurement on two of the boreholes after an interval of 15 days, and found the difference in crack development in the two measurements. The work of this paper provides a new attempt for the detection and monitoring of concrete crack morphology.

Rock-socketed piles are commonly used in pile foundations for large buildings because of their excellent load-bearing characteristics. The roughness of the pile–rock interface affects the load transfer and the ultimate side resistance of the pile. In this work, a laser radar system is developed to measure the surface roughness of a dry bored pile and the shape of the borehole, and a three-dimensional model of the borehole is reconstructed based on the laser point cloud. The 3D surface model was used to extract the vertical contour lines in different directions and thus calculate the roughness of the pile. A numerical simulation of the real measured 3D model using FLAC3D is presented. A borehole of a real rock-socketed pile was measured and simulated. The results show that, although the working load is carried by both the side and base resistances, the former plays a major role. The slow-varying load-settlement curve indicates that the pile has a superior load–bearing capacity, and the maximum allowable settlement should be considered in the application. The simulations, using the actual piles tested, produced a more realistic load response and were able to predict the load-bearing performance of the piles more accurately. Furthermore, this approach offers a reference for the design of rock-socketed piles.

Borehole imaging technology is a critical means for the meticulous measurement of rock mass structures. However, the inherent issue of probe eccentricity significantly compromises the quality of borehole images obtained during testing. This paper proposes a method based on grayscale feature analysis for reverse positioning of imaging probes and image restoration. An analysis of the response characteristics of probe eccentricity was conducted, leading to the development of a grayscale feature model and a method for reverse positioning analysis. By calculating the error matrix using the probe’s spatial trajectory, this method corrects and restores grayscale errors caused by probe eccentricity in images. Quantitative analysis was conducted on the azimuthal errors in borehole images caused by probe eccentricity, establishing a method for correcting image perspective errors based on probe spatial-positioning calibration. Results indicate significant enhancement in the effectiveness and measurement accuracy of borehole images.

The accurate and efficient acquisition of rock mechanical properties is critical for ensuring the safety and efficiency of underground engineering construction. Traditional laboratory tests are characterized by long cycles, high costs, and an inability to reflect in situ mechanical properties, while existing deep learning models based on while-drilling data suffer from poor noise robustness, insufficient deep feature extraction, and low accuracy in synchronous multi-parameter prediction. To address these limitations, this paper proposes a hybrid deep learning model (CNN-LSTM-MoE) combining a convolutional neural network (CNN), a long short-term memory network (LSTM), and a mixture of experts (MoE) system. The model enables intelligent prediction of elastic modulus, Poisson’s ratio, and yield stress from while-drilling parameters. The proposed model integrates CNN’s local feature extraction capability, LSTM’s temporal dependency modeling capability, and the multi-expert dynamic fusion mechanism of MoE. Furthermore, it incorporates physical constraints from rock fragmentation mechanics and an adaptive multi-objective loss weight optimization strategy to comprehensively enhance the multi-parameter synchronous prediction performance. Experimental results demonstrate that the proposed model achieves coefficients of determination (R²) of 0.8965 for elastic modulus, 0.9193 for Poisson’s ratio, and 0.9813 for yield stress on the laboratory validation dataset, with a mean squared error (mse) of 4.0720. Its prediction performance significantly outperforms benchmark models such as TCN and Transformer time-series architectures. Ablation studies further validate the critical role of the integrated LSTM and MoE modules in improving model accuracy, with the MoE module contributing an average R² improvement of approximately 24%. This study not only provides an effective method for high-precision acquisition of rock mechanical parameters while drilling, but also offers a feasible solution based on numerical simulation for data augmentation to address the common issue of scarce labeled data in deep learning applications within engineering fields.

In-situ stress is natural stress existing in rock masses. As an important basic parameter, accurate in-situ stress is highly beneficial to the theoretical research and engineering application of rock mechanics. In-situ tests in boreholes are the most direct and effective way to obtain in-situ stress. However, the high temperature and high water pressure testing environment in boreholes limits the application scope of measurement sensors. In this study, a micro-optical imaging measurement method is proposed. On the basis of this method, an aperture measurement device is developed, and a complete set of in-situ stress measurement technology is formed. Laboratory and field application tests are carried out. The results of the temperature and pressure test in a laboratory show that the in-situ stress test technology improves service temperature and pressure and offers an extensive application range. The field test on a shallow borehole in an iron mine roadway and a deep borehole in a shale gas exploration well reveals the deformation of the test boreholes in the process of stress relief. This result proves the feasibility of the in-situ stress measurement technology. On the basis of the theory of elasticity, in-situ stress is calculated with reference to the in-situ diameter deformation of the boreholes and the mechanical parameters of the rock core. A number of in-situ stress tests are carried out under different borehole depths, and the testing times are recorded. Results show that the application of wireline coring drilling technology speeds up the test process. Hence, it can boost the operability of the in-situ stress test. In sum, the in-situ stress measurement technology based on the micro-optical imaging measurement method is feasible and operable and may thus improve the application depth of the in-situ stress measurement method based on the stress relief method.