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Detection of leaks of fugitive greenhouse gases (GHGs) from landfills and natural gas infrastructure is critical for not only their safe operation but also for protecting the environment. Current inspection practices involve moving a methane detector within the target area by a person or vehicle. This procedure is dangerous, time consuming, labor intensive and above all unavailable when access to the desired area is limited. Remote sensing by an unmanned aerial vehicle (UAV) equipped with a methane detector is a cost-effective and fast method for methane detection and monitoring, especially for vast and remote areas. This paper describes the integration of an off-the-shelf laser-based methane detector into a multi-rotor UAV and demonstrates its efficacy in generating an aerial methane concentration map of a landfill. The UAV flies a preset flight path measuring methane concentrations in a vertical air column between the UAV and the ground surface. Measurements were taken at 10 Hz giving a typical distance between measurements of 0.2 m when flying at 2 m/s. The UAV was set to fly at 25 to 30 m above the ground. We conclude that besides its utility in landfill monitoring, the proposed method is ready for other environmental applications as well as the inspection of natural gas infrastructure that can release methane with much higher concentrations.

Roadways stability in longwall coal mining is critical to mine productivity and safety of the personnel. In this regard, a typical challenge in longwall mining is to predict roadways stability equipped with a reliable support system in order to ensure their serviceability during mining life. Artificial neural networks (ANNs) were employed to predict the stability conditions of longwall roadways based on roof displacements. In this respect, datasets of the roof displacements monitored in different sections of a 1.2-km-long roadway in Tabas coal mine, Iran, were set up to develop an ANN model. On the other hand, geomechanical parameters obtained through site investigations and laboratory tests were introduced to the ANN model as independent variables. In order to predict the roadway stability, these data were introduced to a multilayer perceptron (MLP) network to estimate the unknown nonlinear relationship between the rock parameters and roof displacements in the gate roadways. A four-layer feed-forward backpropagation neural network with topology 9-7-6-1 was found to be optimum. As a result, the MLP proposed model predicted values close enough to the measured ones with an acceptable range of correlation. A high conformity ( R 2  = 0.911) was observed between predicted and measured roof displacement values. Concluding remark is the proposed model appears to be a suitable tool for prediction of gate roadways stability in longwall mining.

With the widespread application of machine learning methods, the continuous improvement of forecast accuracy has become an important task, which is especially crucial for landslide displacement predictions. This study aimed to propose a novel prediction model to improve accuracy in landslide prediction, based on the combination of multiple new algorithms. The proposed new method includes three parts: data preparation, multi-swarm intelligence (MSI) optimization, and displacement prediction. In the data preparation, the complete ensemble empirical mode decomposition (CEEMD) is adopted to separate the trend and periodic displacements from the observed cumulative landslide displacement. The frequency component and residual component of reconstructed inducing factors that related to landslide movements are also extracted by the CEEMD and t-test, and then picked out with edit distance on real sequence (EDR) as input variables for the support vector regression (SVR) model. MSI optimization algorithms are used to optimize the SVR model in the MSI optimization; thus, six predictions models can be obtained that can be used in the displacement prediction part. Finally, the trend and periodic displacements are predicted by six optimized SVR models, respectively. The trend displacement and periodic displacement with the highest prediction accuracy are added and regarded as the final prediction result. The case study of the Shiliushubao landslide shows that the prediction results match the observed data well with an improvement in the aspect of average relative error, which indicates that the proposed model can predict landslide displacements with high precision, even when the displacements are characterized by stepped curves that under the influence of multiple time-varying factors.

Rock mass containing central locked segments is susceptible to freeze–thaw weathering and could result in rock instability and even serious geological hazards. This work conducts uniaxial compression and acoustic emission synchronization tests to experimentally investigate the rock bridge deformation and failure characteristics of central locked samples drilled from plateaus in China. The influences of freeze–thaw cycles and rock bridge angle on the fracturing and acoustic emission pattern were characterized. Results show that the physical and mechanical parameters deteriorate in varying degrees with the increase of freeze–thaw cycles. The compression stress curves display as fluctuation trend and multiple stress drops, indicating that the rock bridge improves the plastic properties of samples. The points of stress drops are in good agreement with sudden increment of acoustic emission count curve. The acoustic emission count and released acoustic emission energy decrease with the increase of freeze–thaw cycles. The damage model established based on acoustic emission parameters reflects the process of crack propagation and coalescence of central locked sample subjected to freeze–thaw cycles during uniaxial compression. Damage variable evolution rate decreases with increasing of freeze–thaw cycles. The damage evolution rate and the associated crack growth rate is the smallest for a rock with bridge angle of 30 °.

The pre-peak loading stages of rock in compression tests are divided into four stages (i.e., crack closure, elastic deformation, stable crack growth and unstable crack growth) by identifying the Crack Closure stress (CC), Crack Initiation stress (CI), and crack damage stress. A new method for determining the CC and CI is presented in this paper and compared with previous methods. The new method is called “Continuous Strain Deviation” (CSD), and it solves two problems associated with other methods: 1) determining the limits for the elastic range in laboratory data, and 2) identifying where crack closure or initiation occurs from the subtle changes in the stress-strain data. Starting from an initial point corresponding to 30% to 40% UCS, the proposed algorithm provides a distinct indicator for CC and CI. The CC and CI for Badaling granite and Äspö diorite are determined with the proposed method, results from which are similar to other methods. Sensitivity analyses of the CSD method show that stable CC and CI values could be estimated using any initial point from 30% to 40% UCS. Comparison studies show that the CSD method predicts a smaller stress range and gives a more distinct indicator for both CC and CI.

Landslides often occur within the reservoir area behind dams. In China, a common strategy for stabilizing these landslides is to install large piles through the landslide and into the stable ground below. The piles interact with the landslide and constitute a landslide-stabilizing pile system. The deformation of this system under the reservoir operation is more complicated than the deformation of the landslide itself. Understanding the behaviour of this system is very important to the long-term safety of landslides stabilized with piles in reservoirs. The Majiagou landslide, which was selected as a case study, was triggered by the first impoundment of the reservoir behind the Three Gorges dam. A row of anti-slide piles was installed in the landslide in 2007, but monitoring results found these were ineffective at stabilizing the landslide. Subsequently, in 2011, two longer test piles and an integrated monitoring system were installed in the landslide to better understand the failure mode of the landslide and to measure the deformation characteristics of the landslide-stabilizing pile system. Monitoring results show that the Majiagou landslide is a translational landslide with three slip surfaces. The test piles provided local resistance and partially slowed down the sliding mass behind the piles, and the landslide deformation response to external factors decreased for a time. However, after 2 years, the deformation of the landslide-stabilizing pile system reverted to seasonal stepwise cumulative displacements influenced by cycles of reservoir drawdown and rainfall. The monitoring results provide fundamental data for evaluating the long-term performance of anti-slide piles and for assessing long-term stability of the stabilized landslide under the reservoir operation.

A new methodology is presented for 3-D automated mapping of joints that are exposed primarily as traces in a rock face as opposed to planar facets. The method identifies 3-D points in a photogrammetry or a LiDAR derived point cloud that corresponds to the traces of the joints as observed in image data. First, the 2-D trace texture is extracted from image data using a hybrid global and local threshold method and integrating a series of image-processing algorithms. Second, data matching links the pixel locations corresponding to the identified traces in an image to the 3-D coordinates in the point cloud. This matching is accomplished by a coordinate transformation between the image coordinates and point cloud coordinates. Finally, a 3-D discontinuity trace map is acquired by analysing the 3-D spatial features of the traces. A case study of a rock slope along a highway is presented using the proposed method. The results demonstrate that the fusion of image data and point cloud data improves the mapping of discontinuities that primarily appear as traces in outcrops versus that achieved by existing methods that rely only on point cloud data.

Understanding hydraulic fracturing mechanisms in heterogeneous laminated rocks is important for designing and optimizing well production, as well as for predicting shale gas production. In this study, a micromechanics-based numerical approach was used to understand the physical processes and underlying mechanisms of fracking for different strata orientations, in-situ stresses, rock strengths, and injection parameters. The numerical experiments revealed a very strong influence of the pre-existing weakness planes on fracking. Geological models for rock without weakness planes and laminated rock behave very differently. Most simulated fractures in the rock without weakness planes were caused by tensile failure of the rock matrix. In an intact rock model, although a radial damage zone was generated around the injection hole, most of the small cracks were isolated, resulting in poor connectivity of the fracture network. For rock models with pre-existing weakness planes, tension and shear failure of these structural planes formed an oval-shaped network. The network was symmetrically developed around the injection well because the strength of the pre-existing weakness planes is generally lower than the rock matrix. The research shows that the angular relations between the orientation of the structural planes and the maximum horizontal stress, as well as the in-situ stress ratios, have significant effects on the morphology and extent of the networks. The strength of the pre-existing weakness planes, their spacing, and the injection rate can dramatically influence the effectiveness of hydraulic fracturing treatments.

Failure mechanisms of the rock mass in the regions of maximum stress concentrations around a longwall face were assessed. In this respect, seismic events that result from changes in the stress field were analyzed to gain more knowledge about rock failure mechanisms in the proximity of the face area. A deep longwall mine developed at depths of about 3–3.5 km in South Africa was selected as a case study. Seismic moment tensor solutions were obtained for 32 seismic events with moment magnitudes in the range of 0.49 and 2.10. Through moment tensor decomposition, the dominant failure mechanisms were investigated by drawing focal mechanism plots. Further analysis was implemented by depicting the corresponding 3D radiation patterns of P-wave particle motions. Although the results cover various failure mechanisms, the dominant mechanisms are shear, implosional, and compressional failures. According to the results, most of the maximum principal stresses in the mine are compressive and oriented nearly vertical, which are in accordance with the gravitational collapses of the mined out areas. The results obtained from this research show that measuring and analyzing mining-induced seismicity can be a reliable measure to characterize the dominant failure mechanisms in a nondestructive manner and to provide a useful assessment of the stability of the longwall face in advance of extraction.

Brinell indentation tests were performed on Montney siltstone, and the results were compared with discrete element indentation simulations that use the micro-parameters calibrated using compression test data from the same siltstone samples. The simulated proppant indentation into the rock surface can be 15% less than the laboratory measurements. A lower effective particle–particle modulus and thus a lower Young’s modulus are needed in discrete element models for proper simulation of indentation. An equation to find the appropriate value of Young’s modulus for indentation simulation is proposed using Brinell indentation tests including 198 laboratory tests and 32 discrete element simulations. This equation can improve the prediction of Young’s modulus and thus the particle–particle effective modulus for indentation simulations to match the measured force–indentation depth curve in the laboratory. Using the improved micro-parameters, a parametric analysis of the influence of rock Young’s modulus and proppant particle size on proppant embedment was performed. An equation to estimate Brinell hardness as a function of Young’s modulus and closure stress was derived. A practical procedure was developed to predict proppant embedment from the estimated hardness. The predictions agree with the laboratory measurements in a case study on the Montney Formation.