In this study, we adopt the proposed strength-based local maximum stress (SLMS) criterion to calculate the crack propagation of tensile and shear cracks in a plate with two parallel closed flaws at different rock bridge inclinations (beta) under compression. The effect of beta on the failure mode of rock bridges was investigated. Then, we modeled the three-section landslides caused by crack propagation under different friction coefficients to validate the effectiveness of SLMS in modeling crack propagation in slopes. Finally, the formation of sliding surfaces in four specific rock slopes with multi-flaws was modeled to investigate the effect of flaw distribution on the depth and shape of sliding surfaces of slopes. Results indicate that flaws located in the middle of the slope have a significant effect on the failure mode and the depth of the sliding surface at this location. Due to the small increments of displacement during the failure process of the slope, which are difficult to monitor, displacement monitoring methods are ineffective for landslide early warning. The instability of the rock slope is caused by the initiation, propagation, and coalescence of cracks, therefore, real-time monitoring of the micro-failure accumulation in the slope is beneficial for early warning of landslides. Moreover, the variation of contact status of crack surfaces during the formation of slope sliding surfaces was analyzed, and the effect of sliding surface formation on contact sliding distance and contact gap distance of crack surfaces was investigated.

During the construction of the Hanjiang-to-Weihe River Diversion Project, frequent extremely intense rockbursts posed a serious threat to the safety of personnel and equipment. A microseismic (MS) monitoring system, which can effectively predict rockbursts, was established to monitor the sprouting and propagation of microcracks within the rock mass surrounding the tunnel in real time. First, the MS activity characteristics of the first excavated section (K33 + 870 ~ K37 + 011) and the second excavated section (K39 + 596 ~ K41 + 602) in the south Qinling section were compared under different surrounding rock classifications. Then, the time distribution characteristics of MS events from May 14 to June 14, 2020 were analyzed in detail, focusing on the MS event energy and spatial distribution of the first extremely intense rockburst. In addition, the effects of the daily excavation distance and excavation speed on the characteristics of extremely intense rockburst and MS activity were explored. Finally, the waveform and time–frequency characteristics of rockbursts with different intensities in the different surrounding rock classifications are discussed. According to the characteristics of the time–frequency, the extremely intense rockbursts in the classification I surrounding rock are divided mainly into low-frequency sustained type, low-frequency discontinuity type, and high-frequency discontinuity type, and the extremely intense rockbursts in the classification II surrounding rock are divided mainly into high-frequency discontinuity type and low-frequency discontinuity type. The study results are of great significance for improving the excavation efficiency and rockburst prediction accuracy in deep-buried tunnels with frequent rockbursts.

The traditional fracture criterion performs well when calculating crack propagation under tensile conditions, but a zigzag crack propagation path was obtained under compression, which is inconsistent with experimental results. This study indicates that the positive and negative oscillations of the mode-II stress intensity factor (KII) under compressive loading during crack propagation is the reason for the zigzag trajectory of crack propagation. Such oscillation of KII can be effectively eliminated when the non-singular stress (T-stress) at the flaw tips is considered, and thus, the path of crack propagation can also be smoothed. However, due to the complicated calculations of the stress intensity factors and T-stress at each step of crack propagation, it is not suitable for simulation in fast, complex environments and multi-flaws. Also, the lack of a shear fracture criterion in traditional fracture methods leads to difficulty in studying shear crack propagation in rock. Therefore, a strength-based localized maximum stress (SLMS) criterion was proposed in this study to model both tensile and shear crack propagations in rock more efficiently, conveniently, and accurately. Then, the crack propagation processes in both plate and Brazilian disc specimens with a single flaw under uniaxial and biaxial compression were modeled to investigate the effects of the flaw inclination angle, friction coefficient, and loading level on the crack propagation path. The influence of the contact friction between the flaw surfaces on the contact status and the crack propagation path during the crack propagation process was also analyzed. Also, the crack propagation in a plate with a single flaw under biaxial tension was modeled, which shows bifurcation crack propagation when the lateral tension is several times larger than the axial tension. All the modeled results indicated that the proposed SLMS criterion can get better results when modeling crack propagations for open or closed flaws under tension or compression conditions.

Microseismic monitoring has become a well-known technique for predicting the mechanisms of rock failure in deeply buried energy exploration, in which noise has a great influence on microseismic monitoring results. We proposed an improved microseismic denoising method based on different wavelet coefficients of useful signal and noise components. First, according to the selection of an appropriate wavelet threshold and threshold function, the useful signal part of original microseismic signal was decomposed many times and reconstructed to achieve denoising. Subsequently, synthetic signals of different types (microseismic noise, microseismic current, microseismic noise current) and with various signal-to-noise ratios (SNRs, −10~10) were used as test data. Evaluation indicators (mean absolute error μ and standard deviation error σ) were established to compare the denoising effect of different denoising methods and verify that the improved method is more effective than the traditional denoising methods (wavelet global threshold, empirical mode decomposition and wavelet transform–empirical mode decomposition). Finally, the proposed method was applied to actual field microseismic data. The results showed that the microseismic signal (with different types of noise) could be fully denoised (car honk, knock, current and construction noise, etc.) without losing useful signals (pure microseismic), suggesting that the proposed approach provides a good basis for the subsequent evaluation and classification of rock burst disasters.

During the excavation of tunnels in the Hanjiang-to-Weihe River Diversion Project, many rockbursts occurred in water-rich (WR) areas. To study the mechanism of rockburst under such conditions, the features of microseismic events and rockbursts in WR tunnel sections excavated by both a tunnel boring machine (TBM) and “drilling and blasting” methods were statistically analysed and compared with those of adjacent water-poor (WP) areas. The results show that in both WR and WP areas, rockbursts are related to the classification of surrounding rock and are also affected by the excavation method. The rockbursts in most WP areas are more active than those in the nearby WR areas in sections excavated by TBM. However, the opposite is true under high in situ stress conditions. To explain the microseismic monitoring results, the mechanical behaviour of rock, including short- and long-term mechanical behaviour under different water and loading conditions, was also investigated, and the results show that under high stress conditions, water will accelerate the occurrence of rockburst. Therefore, the appropriate method to control rockburst is stress release, which can be achieved by advanced pilot tunnel excavation, hydraulic fracturing, and presplitting blasting, rather than high-pressure water spraying in the surrounding rock.

Automatic, quick and accurate picking of the arrival-times of both the P- and S-waves of microseismic (MS) events is essential to real-time data processing for MS monitoring. This study proposes a two-stage arrival-time picking method that involves extracting the region of interest of the waveform and P- and S-wave arrival-time prediction. The dataset collects 6107 MS signals with low and high signal-to-noise ratios to train and test the proposed method. The YOLO network is employed to extract the MS signal region to narrow the range of arrival-time picking and significantly improve the problem of serious interference caused by excessive negative samples in the MS signal. Then, the convolutional neural network is utilised further to precisely pick the arrival-times of both the P- and S-waves. The results showed that the proposed two-stage method is more accurate than the one-stage method in picking the arrival-time. The effects of different convolutional kernel sizes and fully connected layers on the two-stage model are also discussed. Furthermore, a new multi-index comprehensive evaluation method that considers both the accuracy and the size of the model is proposed to further evaluate the proposed model's practicality. This study presents a new method suitable for both P- and S-wave arrival-time picking to enable MS monitoring, which is valuable for various rock engineering applications.

The development of high-precision and interpretable automatic waveform classification algorithms with strong adaptability is becoming increasingly significant under the background of the big data era of microseismicity. Considering the deficiency of the existing network in waveform recognition and classification, an improved model which is suitable for microseismic (MS) monitoring waveform recognition was proposed in this study based on the LeNet framework. The improved model was applied to investigate thirteen kinds of MS monitoring signals that appear within 8 months of the Hanjiang-to-Weihe River Diversion Project. The results show that the accuracy of the best framework in the improved model is 0.98, which is 0.1 higher than original model. The average precision, recall and F 1 values of all improved models increased by 0.11, 0.12 and 0.12, respectively. Meanwhile, the improved model can visualize the entire waveform recognition process. A novel observation is that in some signal categories, the improved model mainly classified by focusing on the background information instead of the waveforms. It provides a reference for the intelligent classification of signals in MS monitoring engineering.

As a kind of dynamic real-time monitoring technology, microseismic monitoring technology has been widely used for rockburst warning. Due to the complexity of the actual monitoring environment, the monitoring signals often contain different types of noise, affecting the earning warning of rockburst. In this study, an Autoencoder Convolutional Neural Network denoising model based on deep learning has been proposed to denoising of the complex signals. The unsupervised adaptive training method is used to train the model, which only needs to set its initial parameters. The importance of an enhanced training dataset is illustrated by the comparison experiment. The results indicate that the training and verification shows well performance during training. The denoising efficiency of the proposed model is studied by the denoising of the synthetic noise-containing signals. Furthermore, the dataset from the water conveyance tunnel in the Hanjiang-to-Weihe River water diversion project (HJ-Project) in Shaanxi Province is taken as an engineering example to evolute the performance of the proposed model for practical project. The denoising performance of the model is analysed through the visual denoising results and evaluation index. The model can effectively denoise the complex noised signal which separate it into pure microseismic signal and noise signal, and improve the signal-to-noise ratio, which is benefit for arrive-time picking and source locating then improve the performance of early warning of rockburst.

To reduce geological disasters caused by expansive soil, it is crucial to use a new type of modified material to rapidly improve soil strength instead of traditional soil improvement materials such as lime and cement. Nanographite powder (NGP) has excellent properties, such as high adsorption, conductivity, and lubrication, since it has the characteristics of small size, large specific surface area, and high surface energy. However, previous studies on the improvement of expansive soil with NGP are not processed enough. To study the improvement effect of NGP on expansive soil, non-load swelling ratio tests, consolidation tests, unconfined compressive strength tests, mercury injection tests, and micro-CT tests on expansive soil mixed with different NGP contents were performed. The results show that the non-load swelling ratio, mechanical properties, and porosity of expansive soil show some increasement after adding NGP. The strength of expansive soil reaches the maximum when the NGP content is 1.450％. The cumulative mercury volume and compressive strain of expansive soil reach the maximum with the 2.0％NGP content. Finally, the modification mechanism of swelling, compressibility, microstructure, and compressive strength of expansive soil by NGP is revealed.

The recognition and classification of microseismic (MS) waveforms detected using MS monitoring are of great importance for predicting instability in rock engineering. The MS waveform can be displayed as an original image of time-amplitude, or it can be converted into a spectrogram, and such images can be classified accurately with the deep learning method in computer vision. Deep learning models, including VGG16, ResNet18, AlexNet, and their ensemble model, are employed to identify and classify MS waveform images and spectrograms. The results show that these models perform well in learning each model of the nondenoised waveform image set with accuracies of AlexNet, VGG16, ResNet18 and the ensemble model on the original waveform data of 0.96, 0.98, 0.96 and 0.98, respectively. However, different models differ in recognising noise, electricity and MS events, which making it necessary to select the model according to the real scenes. The model feature maps can accu-rately explain not only the learning process of the convolution network but also the reason why the test results for the original waveform dataset and spectrogram dataset are similar, thereby realizing end-to-end recognition and classification of the original waveform images. Finally, the mixed signals of MS, noise and electricity are discussed to provide a reference for automatic classification of waveforms in MS monitoring engineering.

Water is believed to be a significant factor affecting the short- and long-term strength of rocks. To further understand the effect of water on the mechanical behavior of rocks, we first performed a series of water absorption tests and uniaxial compression tests (different soak times) to guide a time-dependent creep test. Then, uniaxial creep tests under different water-stress sequence conditions and the traditional creep test were performed. Finally, the effect of water distribution on rock strength and failure patterns is discussed. The obtained results show that the water-stress sequences would result in different mechanical behaviors of rock, i.e., the specimens were more likely to fail under the condition of loading followed by soaking than under the condition of soaking followed by loading and loading after soaking. Furthermore, the strength of the specimen with a nonhomogeneous water distribution is greater than that of the homogeneous specimen if they have the same water content. Because the nonhomogeneous water distribution is dry inside the specimen, the homogeneous water distribution affects the entire specimen, leading to greater softening. According to the obtained results, the sequence of loading and soaking should be considered when predicting the stability of rock mass engineering.

以膨胀土为例,采用扫描电子显微镜(SEM)、压汞(MIP)、核磁共振(NMR)和Micro-CT试验对土样进行研究,揭示土体微观结构,对比分析4种方法研究土体微观结构特性的异同及适用性.结果表明:SEM得到的土体微观孔隙图片可以定性、直观地显示土中孔隙大小及其分布;MIP、NMR可以定量分析土体孔隙大小的分布,两者试验结果基本相同;Micro-CT能够将高精度数字图像作为数据分析的辅助手段,定量分析土体孔隙结构特征.

The soil pressure on the bottom surface of the foot blades is an important monitoring point during the sinking process of large underwater caissons. Complex soil-structure interactions occur during the sinking process, making it difficult to accurately predict the soil pressure of foot blades. Accurate construction processes often rely on data from the soil pressure of foot blades in the field. In this study, a data-driven approach is used to establish the relationship between the amount of sinking of the caisson and the soil pressure of foot blades. Furthermore, by improving the splitting method of the original Classification and Regression Tree (CART) algorithm, a single model’s numerical prediction of 80-foot blades soil pressures is realized. The improved CART model, multilayer perceptron (MLP), long short-term memory (LSTM), and a linear regression model are compared through a comprehensive multiparameter evaluation method. Finally, this article discusses the deployment scheme of the model by comparing and analyzing the data in the time period of 10 : 00 on July 29, 2020, and 23 : 00 on August 7, 2020. The experimental results can satisfy the engineering demands and provide a basis for further data-driven intelligent control of large caisson sinking.

To further understand the stress evolution and rockburst occurrence mechanism in geothermally rich areas in the Sichuan–Tibet railway project, this work presents a theoretical study of the influence of temperature change on the failure of rock, conducts numerical studies of the temperature and stress evolution in the surrounding rock during high-temperature tunnel excavation, and further studies the possibility of rockbursts under high in situ stress and high-temperature conditions. Rockbursts occur frequently at the junction of a face and tunnel wall, and ventilation and cooling of tunnels reduce the stress and sometimes reduce the possibility of rockbursts. Continuous cooling leads to a larger tensile stress and the possibility of failure of wall rock. In addition, the influence of the convection heat transfer coefficient, in situ stress and fault effect on the stress distribution and possibility of rockbursts are also discussed in detail. The results are beneficial for the prevention and control of rockbursts in high in situ stress and geothermally rich areas.

This study examined the mechanical properties, springback behavior from three-point bending loading–unloading tests and biocompatibility from human osteoblast cell adhesion and proliferation experiments in Ti-15Mo alloy with different microstructures. The springback ratio increased after the appearance of deformation microstructures including {332} < 113 > twins and dislocations, due to the increased bending strength and unchanged Young’s modulus. By contrast, the change in springback ratio was dependent on the competing effect of the simultaneous increase in bending strength and Young’s modulus after phase transformation, namely, the isothermal ω-phase formation. Good cell adhesion and proliferation were observed on the alloy surface, and they were not significantly affected by the deformation twins, dislocations and isothermal ω-phase. The diversity of deformation and phase transformation microstructures made it possible to control the springback behavior effectively while keeping the biocompatibility of the alloy as an implant rod used for spinal fixation devices.

Due to the different geological conditions and construction methods associated with different projects, rockbursts in deep-buried tunnels often present different precursor characteristics, bringing major challenges to the early warning of rockbursts. To adapt to the complexity of engineering, it is necessary to review the latest advancements in rockburst early warning and to discuss general early warning methods. In this article, first, microseismic monitoring and localization methods applicable under tunneling construction are reviewed. Based on the latest engineering examples and research progress, the microseismic evolution characteristics of the rockburst formation process are summarized, and the formation process and mechanism of structure-type and delayed rockbursts are analyzed. The different methods for predicting the risk and level of rockbursts using microseismic indices are reviewed, and the implementation methods and application cases for predicting potential rockburst areas and rockburst probability based on a mechanical model are expounded. Finally, combined with the new practice in early warning methods, development directions for the early warning of rockbursts are put forward.

Fracturing behavior of rock is significantly affected by water. To study the influence of water on the critical values of stress intensity factors (the SIFc, including K(If )and K-IIf) corresponding to the onset of fracture of rocks, edge-cracked semicircular bend (SCB) testing was performed on sandstone specimens (collected at a site in Linyi City, Shandong Province, China) with different water contents. First, water absorption was tested to study the evolution of the water content in the specimens. Then, the SIFc of SCB specimens after different soaking durations in water (yielding conditions ranging from dry to saturation) was tested under three-point loading. The results showed a linear decrease in SIFc with increasing water content for all the specimens with different notch angles; however, with increasing notch angle, the K-If exponentially decreased, while the K(IIf )increased. To study the effect of sustained load on SIFs of rock in water, a new experiment was designed on the specimen subjected to loading while soaking for a certain duration, then the SIFc was tested. Comprehensive predictive empirical relationships are established between SIFc and water content/soaking duration for the sandstone. A novel observation is for two specimens with the same water soaking duration, the SIFc of a specimen with loading during soaking is lower than that when it was tested after soaking, which is attributed to the fact that water is more likely to penetrate into the rock, because the bending load opens pores and cracks then leads to an increase in water content under such conditions. In other words, the water content of sandstone specimens used in this study is the key factor affecting its SIFc.

To study the effects of different concentrations of zinc ions on the mechanical strength, material composition, and microstructure of red clay, a triaxial test, an x-ray diffraction test, an x-ray fluorescence spectrometry test, a scanning electron microscopy test, and a mercury intrusion test were carried out on contaminated soil to investigate the mechanisms of zinc ion-contaminated red clay. The results show that the higher the concentration of zinc ions, the smaller the shear strength and cohesion of the red clay. The internal friction angle is increased first and then decreased. From material composition, zinc ion makes montmorillonite and hemite disappear in red clay. With the increase of zinc ion concentration, quartz semiquantitative increase and kaolinite semiquantitative decrease and the content of SiO2, Fe2O3, and Na2O reduces. Microscopically, the structure of red clay changes from floc structure to granular and aggregate structure after the zinc ions are added, while the contact of the particles is converted to point contact. With the increase of the concentration of zinc ions, the porosity and the fractal dimension of the red clay gradually increase, and the stability of the granular structure is weakened.

Particle size exerts a significant influence on the mechanical behavior of soil. However, insufficient research has been carried out on red clay formations, which are widespread in some Chinese provinces. Here, using unconfined compressive strength (UCS) tests, wetting–drying (WD) tests, and low-temperature nitrogen adsorption tests, we examined the relationship between the particle size and a number of mechanical and microstructural characteristics of a red clay outcropping at a construction site in China. Our results suggest that, depending on the surface area, porosity, particle size, and dry density, the failure mechanism in UCS tests will be different. That is, as the particle size increases, the failure mode of soil changes from split failure into shear failure. In addition, as the dry density increases, the UCS of the soil sample is significantly improved, and its total porosity and fractal dimension decrease. We also evaluated a dependence on particle size and the number of WD cycles on the distribution and size of cracks in WD tests. We conclude by suggesting that particle size, dry density, and WD behavior should all be taken into account in roadbed designs in red clay formations.

The ω-phase formation and its collapsed structures in metastable β-type Ti-Mo alloys were illustrated by first-principles calculations and experimental evidence of a partially collapsed ω-phase in the nano-scale Mo-depleted region under a rapid cooling via high-angle annular dark-field scanning transmission electron microscopy. The ease of ω-phase formation within -Mo-Ti-Mo- poor cluster structure was not only due to the low energy barrier in the collapse pathway, which was caused by the reduced lattice distortion, but also due to the softening of the shear modulus (G111) as a result of the small charge density difference. The most stable collapsed structure of the ω-phase strongly depended on the minimum stacking fault energy among different collapse degrees in accordance to the smallest charge density difference. Therefore, the concurrent compositional and structural instabilities of the ω-phase was attributed to the coupling effect of the cluster structure with stacking fault from the atomic and electronic basis.