An Indirect Method for the Quantitative Identification of Unstable Rock

Under the influence of continuous external factors (rainfall, earthquake, construction, etc.), the slope rock mass in a stable state gradually transited to an unstable rock, and then the unstable rock collapsed. However, a safety factor can identify the occurrence of failure but cannot identify the transition of stable rock to unstable rock; thus, it cannot realise the quantitative identification of unstable rocks. In this study, safety factor of adhesion (SFA ) and a relatively objective analysis method are proposed to effectively identify unstable rocks. SFA can be calculated by natural vibration frequency and applied as a mechanical index to judge unstable rock. When SFA is less than 1, the rock is defined as an unstable rock. Compared with the traditional method, the new method has the merits of simple operation, low cost and higher efficiency, and provides a relatively complete quantitative evaluation index and judgment criteria for quantitative identification of unstable rocks for engineers who are engaged in early warning and prevention of rock collapse.

A Large Old Landslide in Sichuan Province, China: Surface Displacement Monitoring and Potential Instability Assessment

Using advanced Differential Interferometric Synthetic Aperture Radar (InSAR) with small baseline subsets (SBAS) and Permanent Scatter Interferometry (PSI) techniques and C-band Sentinel-1A data, this research monitored the surface displacement of a large old landslide at Xuecheng town, Lixian County, Sichuan Province, China. Based on the MassMov2D model, the effect of the dynamic process and deposit thickness of the potentially unstable rock mass (deformation rate < −70 mm/year) on this landslide body were numerically simulated. Combined with terrain data and images generated by an Unmanned Aerial Vehicle (UAV), the driving factors of this old landslide were analyzed. The InSAR results show that the motion rate in the middle part of the landslide body is the largest, with a range of −55 to −80 mm/year on average, whereas those of the upper part and toe area were small, with a range of −5 to −20 mm/year. Our research suggests that there is a correlation between the LOS (line of sight) deformation rate and rainfall. In rainy seasons, particularly from May to July, the deformation rate is relatively high. In addition, the analysis suggests that SBAS can provide smoother displacement time series, even in areas with vegetation and the steepest sectors of the landslide. The simulation results show that the unstable rock mass may collapse and form a barrier dam with a maximum thickness of about 16 m at the Zagunao river in the future. This study demonstrates that combining temporal UAV measurements and InSAR techniques from Sentinel-1A SAR data allows early recognition and deformation monitoring of old landslide reactivation in complex mountainous areas. In addition, the information provided by InSAR can increase understanding of the deformation process of old landslides in this area, which would enhance urban safety and assist in disaster mitigation.

Toward Workable and Cost-Efficient Monitoring of Unstable Rock Compartments with Ambient Noise

Ambient Vibration-Based Structural Health Monitoring (AVB–SHM) studies on prone-to-fall rock compartments have recently succeeded in detecting both pre-failure damaging processes and reinforcement provided by bolting. The current AVB–SHM instrumentation layout is yet generally an overkill, creating cost and power issues and sometimes requiring advanced signal processing techniques. In this article, we paved the way toward an innovative edge-computing approach tested on ambient vibration records made during the bolting of a ~760 m3 limestone rock column (Vercors, France). First, we established some guidelines for prone-to-fall rock column AVB–SHM by comparing several basic, computing-efficient, seismic parameters (i.e., Fast Fourier Transform, Horizontal to Vertical and Horizontal to Horizontal Spectral Ratios). All three parameters performed well in revealing the unstable compartment’s fundamental resonance frequency. HHSR appeared as the most consistent spectral estimator, succeeding in revealing both the fundamental and higher modes. Only the fundamental mode should be trustfully monitored with HVSR since higher peaks may be artifacts. Then, the first application of a novelty detection algorithm on an unstable rock column AVB–SHM case study showed the following: the feasibility of automatic removing the adverse thermomechanical fluctuations in column’s dynamic parameters based on machine learning, as well as the systematic detection of clear, permanent change in column’s dynamic behavior after grout injection and hardening around the bolts (i1 and i2). This implementation represents a significant workload reduction, compared to physical-based algorithms or numerical twin modeling, and shows better robustness with regard to instrumentation gaps. We believe that edge-computing monitoring systems combining basic seismic signal processing techniques and automatic detection algorithms could help facilitate AVB–SHM of remote natural structures such as prone-to-fall rock compartments.

Numerical Simulation of Unstable Rock Failure Mechanisms Through Analysis of Energy Transformations

For rock specimen in uniaxial compression, the energy transformations from elastic strain energy in both the rock and the loading system to plastic strain work in the rock can be identified with the changes in these energy components, whose rates are also useful indicators for distinguishing stable and unstable rock failure. In this study, the influences of the loading system stiffness (LSS), the rock stiffness and the rock brittleness on rock failure modes are examined. The observed energy transformations during rock failure in numerical models are interpreted from an energy perspective. The results show that unstable rock failure tends to occur in rock with large brittleness and small stiffness under a soft loading system. A low LSS and rock stiffness will increase the magnitude of stored elastic strain energy before rock failure, while a brittle rock requires less elastic strain energy to be converted plastic strain work than a ductile rock during its failure. This energy-based approach is useful for investigating potential unstable rock failures that could ultimately be applied to analyze complex mine-scale rockburst cases.