Rock Mechanics Bulletin最新文献

Enhanced geothermal systems (EGSs) in this study are classified as fracturing-EGS (F-EGS), pipe-EGS (P-EGS) and excavation-EGS (E-EGS) according to reservoir stimulation strategies. However, the heat extraction performances of three EGSs employing different stimulation strategies are not fully understood. Here, we define the region where the pore pressure increment calculated by a hydraulic fracturing process is higher than rock tensile strength as the stimulation region for establishing a more accurate F-EGS model, and then compare three geothermal systems to select a appropriate reservoir stimulation strategy. We find that the F-EGS model assuming an entire stimulated region significantly exaggerates the heat extraction results. The optimal conditions for P-EGS are low injection rates and short operation times, which is suiTablefor seasonal heating or multi-energy co-generation projects including a thermal recovery phase. Theoretically, E-EGS has better geothermal extraction performance than F-EGS based on existing model assumptions, but its construction feasibility and economics need further exploration. H₂O is more suiTableas a heat exchange fluid in E-EGS than supercritical CO₂. This study provides a reference for geothermal mining simulation and reservoir stimulation strategy selection.

The evaluation of engineering rock mass quality is fundamental work for the engineering activities of rock mass. The increasing scale of rock mass engineering necessitates higher intelligence, timeliness, and accuracy in engineering rock mass quality evaluation. As the core aspects of engineering rock mass quality evaluation, the structural characteristics, mechanical characteristics, and quality classification of rock mass have been innovated in recent years. The non-contact measurement technology for rock mass structure and rapid interpretation of rock mass structure information enables the intelligent extraction and analysis of rock mass structure parameters. The modular backpack laboratory system of rock mechanics provides an effective means to acquire rock mechanical parameters on-site conveniently. The theory of statistical mechanics of rock mass (SMRM) integrates various factors such as the rock mass properties, geological environment, and engineering disturbance, providing a theoretical basis for accurately evaluating the weakening and anisotropy of rock mass. The cloud computing platform established based on SMRM can provide technical support for the rapid calculation of rock mass parameters and instant evaluation of the rock mass quality. The development of intelligent evaluation method and technology is altering the conventional technical state of qualitative and semi-quantitative evaluation of engineering rock mass quality, supporting the realization of rock mass engineering construction with intellectualization and informatization.

As rainfall infiltrates into soil slopes, the hydraulic and mechanical behaviors of soils are interacted. In this study, an efficient probabilistic parameter estimation method for coupled hydro-mechanical behavior in soil slope is proposed. This method integrates the Polynomial Chaos Expansion (PCE) method, the coupled hydro-mechanical modeling, and the Bayesian learning method. A coupled hydro-mechanical numerical model is established for the simulation of behaviors of unsaturated soil slope under rainfall infiltration, following by training a cheap-to-run PCE surrogate to replace it. Probabilistic estimation of soil parameters is conducted based on the Bayesian learning technique with the Markov Chain Monte Carlo (MCMC) simulation. A numerical example of an unsaturated slope under rainfall infiltration is presented to illustrate the proposed method. The effects of measurement durations and response types on parameter estimation are addressed. The result shows that with the increase of measurement duration, the uncertainties of soil parameters are significantly reduced. The uncertainties of hydraulic properties are reduced significantly using the pore water pressure data, while the uncertainties of soil strength parameters are reduced greatly using the measured displacement data.

This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction (LSP), namely the spatial resolution, proportion of model training and testing datasets and selection of machine learning models. Taking Yanchang County of China as example, the landslide inventory and 12 important conditioning factors were acquired. The frequency ratios of each conditioning factor were calculated under five spatial resolutions (15, 30, 60, 90 and 120 ​m). Landslide and non-landslide samples obtained under each spatial resolution were further divided into five proportions of training and testing datasets (9:1, 8:2, 7:3, 6:4 and 5:5), and four typical machine learning models were applied for LSP modelling. The results demonstrated that different spatial resolution and training and testing dataset proportions induce basically similar influences on the modeling uncertainty. With a decrease in the spatial resolution from 15 ​m to 120 ​m and a change in the proportions of the training and testing datasets from 9:1 to 5:5, the modelling accuracy gradually decreased, while the mean values of predicted landslide susceptibility indexes increased and their standard deviations decreased. The sensitivities of the three uncertainty issues to LSP modeling were, in order, the spatial resolution, the choice of machine learning model and the proportions of training/testing datasets.

Geotechnical design codes and guidelines are all switching from traditional factor of safety design to modern load and resistance factor design (LRFD) or partial factor design (PFD), in the belief that the latter two bring more flexibility and reliability consistency across various design scenarios, thus produce safe and cost-effective design outcomes. This paper first reviews the LRFD and PFD developed for geotechnical applications. A total of seven methods to calibrate the load and resistance factors are also introduced. The ability of the LRFD and PFD to produce designs with consistent reliability is examined and compared to that of a traditional factor of safety method using two examples of the bearing capacity of strip footings and the global stability of soil nail walls. Results showed that the framework of LRFD offers no apparent advantages over working stress design (WSD) in achieving more consistent reliability for geotechnical structures; the dispersion in design probabilities of failure could be five to seven orders of magnitude difference. The variation will be reduced to three orders if using the PFD. Neither reducing the variability in soil shear strength parameters nor allocating partial resistance factors with respect to soil types would efficiently harmonize the reliability levels when dealing with multiple soil layer conditions. In addition, the uniformity of reliability levels is insensitive to calibrations with or without presetting the load factors. This study provides insights into the LRFD and PFD frameworks currently developed for geotechnical applications.

We apply binary logistic regression to correlate fracture shear-slip criticality to hydraulic conductivity using data from four deep scientific boreholes in fractured crystalline rocks. In each borehole, an optimized decision boundary is obtained by maximizing the joint probability of classifying all fractures in consideration as critical or not. All four cases feature an optimized decision boundary close to the empirical rock friction (μ ​= ​0.6), corroborating the applicability of laboratory-derived friction coefficients to faults in situ. Utilizing this statistical technique, we demonstrate that one can determine the in situ stress orientation and relative magnitude based only on whether fractures of varied orientations are hydraulically conductive, or not. The stress inversion results are consistent with independent stress measurements in each of the four case studies.

Traditionally, the factor of safety (FOS) is widely used to account for uncertainties in the design of slopes within the framework of working stress design. As the uncertainties involved in the design of slopes vary, the same FOS may correspond to the different levels of reliability. In this study, the advanced first order reliability method is used to determine the resistance factors for design of slopes in a homogenous soil layer. It is found that the resistance factors depend on the target reliability index, the height of the slope, and the variability of the soil strength parameters. It is difficult to suggest a unique set of resistance factors for design of slopes. Analytic solutions are developed to determine the resistance factors for design of slopes assuming the random variables are normally distributed. An approximate method based on the concept of equivalent target reliability index is also suggested to determine the resistance factors for design of the slope when the soil strength parameters are lognormally distributed. The method suggested in this paper provides a practical way to perform load and resistance factors design of slopes.

Geotechnical engineering is characterized by many uncertainties, including soil material properties, environmental effects, and engineering design and construction, which bring a significant challenge to geotechnical monitoring. However, conventional sensors with several inherent limitations, such as electromagnetic interference, signal loss in long-distance transmission, and low durability in harsh environments cannot fully meet current monitoring needs. Recently, fiber optic sensing technologies have been successfully applied in geotechnical monitoring due to the significant advantages of anti-electromagnetic interference, stable signal long-distance transmission, high durability, high sensitivity, and lightweight, which can be considered an ideal replacement for conventional sensors. In this paper, the working principle of different fiber optic sensing technologies, the development of fiber optic-based sensors, and the recent application status of these sensing technologies for geotechnical monitoring were comprehensively reviewed and discussed in detail. Finally, the challenges and countermeasures of the sensing technologies in geotechnical monitoring were also presented and discussed.

The slope stability assessment is a classical problem in geotechnical engineering. This topic have attracted many researcher’s attention and various theoretical models for predicting critical slope heights or safety factors in the light of the limit equilibrium (LE) method and the kinematical approach of limit analysis (LA) method. Meanwhile, a large number of experimental studies have been conducted to check the slope stability. Using centrifuge testing results, this paper aims to employ Bayesian method to characterize the model uncertainties of the classical three-dimensional rotational failure mechanism proposed by Michalowski and Drescher (2009) to predict critical slope heights in frictional soils, by incorporating the test uncertainties and parameter uncertainties. The obtained results show that the LA three-dimensional rotational failure mechanism overestimates the critical slope height compared with the LE method, and the experimental observational uncertainty has negligible influences on the posterior statistics of model uncertainty.

The failure of rocks is a complicated process as the mechanical properties of the rock are governed by loading history and cumulative ruptures. The geometric aspects of fractures, such as the size and shape of the fractures, the spatial distribution of the fracture networks, and the relations among these aspects also depend on the loads acting on rock mass. In general, the fractures are randomly generated in space which is difficult to be described using mathematical methods. In this paper, the failure processes of rock have been analyzed using the percolation theory. The results indicate that the failure process of rock is a transition from a stable state to an unstable state. This phenomenon is essentially consistent with the phase transition in the percolation theory. Based on this consistency, a theoretical model of percolation for earthquake prediction is proposed. A large number of seismic data provided strong evidence in support of the reliability and applicability of this model.