Numerical simulation on the dynamic mechanical response and fracture mechanism of rocks containing a single hole

Caverns and tunnels are constantly exposed to dynamic loads, posing a potentially significant threat to the safety of rock structures. To facilitate the understanding of dynamic fracture around openings, a series of discrete element models were established to numerically examine the effect of hole shape on dynamic mechanical properties and crack evolution. The results indicate that the existence of a hole greatly reduces dynamic strength, and the reduction is closely related to hole shape. The strain variation of pre-holed specimens is more complicated and even larger than the value of intact specimens. Although crack initiation differs for varying hole shapes, the entire structural collapse of specimens is controlled by macro shear cracks along the diagonal direction of the specimen, which are effectively identified by velocity trend arrows and contact force distribution. Finally, comparative analysis between failure pattern of pre-holed specimens under static and dynamic loads were conducted

Beyond Reward: Offline Preference-guided Policy Optimization

This study focuses on the topic of offline preference-based reinforcement learning (PbRL), a variant of conventional reinforcement learning that dispenses with the need for online interaction or specification of reward functions. Instead, the agent is provided with fixed offline trajectories and human preferences between pairs of trajectories to extract the dynamics and task information, respectively. Since the dynamics and task information are orthogonal, a naive approach would involve using preference-based reward learning followed by an off-the-shelf offline RL algorithm. However, this requires the separate learning of a scalar reward function, which is assumed to be an information bottleneck of the learning process. To address this issue, we propose the offline preference-guided policy optimization (OPPO) paradigm, which models offline trajectories and preferences in a one-step process, eliminating the need for separately learning a reward function. OPPO achieves this by introducing an offline hindsight information matching objective for optimizing a contextual policy and a preference modeling objective for finding the optimal context. OPPO further integrates a well-performing decision policy by optimizing the two objectives iteratively. Our empirical results demonstrate that OPPO effectively models offline preferences and outperforms prior competing baselines, including offline RL algorithms performed over either true or pseudo reward function specifications. Our code is available on the project website: https://sites.google.com/view/oppo-icml-2023

Energy efficiency and environmental degradation nexus: evidence from the Quantile-on-Quantile regression technique

The world is facing enormous challenge of climate change and global warming due to increased emission level. In order to overcome such challenges, economies are adopting energy efficient techniques to control the carbon emissions and improves environmental sustainability. This study analyses the influencing factors of environmental quality from a global perspective throughout the last three decades. In this regard, advanced time series approaches are used to identify the association between factors such as economic growth, energy efficiency (E.N.E.F.), and carbon emissions – covering global data over the period 1990Q4–2020Q4. From the time series methods, this study observed the stationarity of all variables at first difference. The empirical outcomes also validates the long-run equilibrium relationship between the variables. Due to asymmetric distribution of the variables, this study uses the novel Quantile-on-Quantile (Q.Q.) regression approach, which reveals that increasing economic growth harms environmental quality by increasing the carbon emissions level. However, E.N.E.F. is a prominent factor of environmental sustainability, that reduces the level of carbon emissions in the atmosphere. Employing the pairwise Granger causality test, this study observed the unidirectional causality from economic growth to carbon emissions, while a two-way causal nexus is found between economic growth – E.N.E.F. and E.N.E.F. – carbon emissions. Based on the empirical results, this study suggests that economic growth should be regulated in a sense that it contribute towards the improvement of E.N.E.F., which ultimately leads to reduce the emissions level and promote environmental sustainability

The Multimodal Information based Speech Processing (MISP) 2022 Challenge: Audio-Visual Diarization and Recognition

The Multi-modal Information based Speech Processing (MISP) challenge aims to extend the application of signal processing technology in specific scenarios by promoting the research into wake-up words, speaker diarization, speech recognition, and other technologies. The MISP2022 challenge has two tracks: 1) audio-visual speaker diarization (AVSD), aiming to solve ``who spoken when'' using both audio and visual data; 2) a novel audio-visual diarization and recognition (AVDR) task that focuses on addressing ``who spoken what when'' with audio-visual speaker diarization results. Both tracks focus on the Chinese language, and use far-field audio and video in real home-tv scenarios: 2-6 people communicating each other with TV noise in the background. This paper introduces the dataset, track settings, and baselines of the MISP2022 challenge. Our analyses of experiments and examples indicate the good performance of AVDR baseline system, and the potential difficulties in this challenge due to, e.g., the far-field video quality, the presence of TV noise in the background, and the indistinguishable speakers.Comment: 5 pages, 4 figures, to be published in ICASSP202

A Novel Method of Multitype Hybrid Rock Lithology Classification Based on Convolutional Neural Networks

Rock lithology recognition plays a fundamental role in geological survey research, mineral resource exploration, mining engineering, etc. However, the objectivity of researchers, rock variable natures, and tedious experimental processes make it difficult to ensure the accurate and effective identification of rock lithology. Additionally, multitype hybrid rock lithology identification is challenging, and few studies on this issue are available. In this paper, a novel multitype hybrid rock lithology detection method was proposed based on convolutional neural network (CNN), and neural network model compression technology was adopted to guarantee the model inference efficiency. Four fundamental single class rock datasets: sandstone, shale, monzogranite, and tuff were collected. At the same time, multitype hybrid rock lithologies datasets were obtained based on data augmentation method. The proposed model was then trained on multitype hybrid rock lithologies datasets. Besides, for comparison purposes, the other three algorithms, were trained and evaluated. Experimental results revealed that our method exhibited the best performance in terms of precision, recall, and efficiency compared with the other three algorithms. Furthermore, the inference time of the proposed model is twice as fast as the other three methods. It only needs 11 milliseconds for single image detection, making it possible to be applied to the industry by transforming the algorithm to an embedded hardware device or Android platform

Experimental studies on permeability of intact and singly jointed meta-sedimentary rocks under confining pressure

Three different types of permeability tests were conducted on 23 intact and singly jointed rock specimens, which were cored from rock blocks collected from a rock cavern under construction in Singapore. The studied rock types belong to inter-bedded meta-sandstone and meta-siltstone with very low porosity and high uniaxial compressive strength. The transient pulse water flow method was employed to measure the permeability of intact meta-sandstone under a confining pressure up to 30 MPa. It showed that the magnitude order of meta-sandstone's intrinsic permeability is about 10-18 m2. The steady-state gas flow method was used to measure the permeability of both intact meta-siltstone and meta-sandstone in a triaxial cell under different confining pressures spanning from 2.5 to 10 MPa. The measured permeability of both rock types ranged from 10-21 to 10-20 m2. The influence of a single natural joint on the permeability of both rock types was studied by using the steady-state water flow method under different confining pressures spanning from 1.25 to 5.0 MPa, including loading and unloading phases. The measured permeability of both jointed rocks ranged from 10-13 to 10-11 m2, where the permeability of jointed meta-siltstone was usually slightly lower than that of jointed meta-sandstone. The permeability of jointed rocks decreases with increasing confining pressure, which can be well fitted by an empirical power law relationship between the permeability and confining pressure or effective pressure. The permeability of partly open cracked specimens is lower than that of open cracked specimens, but it is higher than that of the specimen with a dominant vein for the meta-sandstone under the same confining pressure. The permeability of open cracked rock specimens will partially recover during the unloading confining pressure process. The equivalent crack (joint) aperture is as narrow as a magnitude order of 10-6 m (1 μm) in the rock specimens under confining pressures spanning from 1.25 to 5.0 MPa, which represent the typical ground stress conditions in the cavern. The in situ hydraulic conductivity measurements conducted in six boreholes by the injection test showed that the in situ permeability of rock mass varies between 10-18 and 10 -11 m2. The lower bound of the in situ permeability is larger than that of the present laboratory-tested intact rock specimens, while the upper bound of the in situ permeability is less than that of the present laboratory-tested jointed rock specimens. The in situ permeability test results were thus compatible with our present laboratory permeability results of both intact and jointed rock specimens. © 2012 Springer-Verlag.Link_to_subscribed_fulltex

Experimental Study on Backfilling Mine Goafs with Chemical Waste Phosphogypsum

To explore the feasibility of cemented paste backfill with phosphogypsum (PG), bleeding water and rheological tests (slump and on-site pipeline loop tests) were performed with PG backfill slurry (PGBS). In the bleeding water test, the PGBS concentration with minimal bleeding water was measured between 60.87 and 67.61%; in the rheological slump test, values of 61 to 68% were determined for the on-site pipeline loop test. The rheological pipeline loop test demonstrated that the resistance coefficient is lowest when the concentration is no higher than 65%. Through industrial experiments, PG slurry with a concentration of 64%–65% backfill was successfully applied to the goaf. The experimental results demonstrate that PGBS with characteristics of “less bleeding water” and “improved pumpability” is obtained when its concentration is between 61 and 65%. Paste-like PG slurry was proven to be optimal for cemented PG backfilling technology

Dynamic Fracture Evolution and Mechanical Behavior of Sandstone Containing Noncoplanar Elliptical Flaws under Impact Loading

To investigate the effects of preexisting flaws with different geometries, including flaw inclination angle and ligament angle on dynamic strength, deformation properties, and fracture evolution of rock materials, a series of dynamic impact tests were conducted on green sandstone specimens containing double elliptical flaws using a 75 mm diameter split Hopkinson pressure bar (SHPB) testing device with a high-speed camera recording in real time. The experimental results show that dynamic strength of specimens with different flaw angles is reduced between 5.91% and 39.92% but from 18.50% to 28.44% for specimens with different ligament angles, indicating that the effect of the flaw angle on the dynamic strength is more significant than that of the ligament angle. However, the dynamic deformation properties are influenced greatly by the ligament angle. Macroscale cracks mostly initiate at or near the flaw tips and then propagate in different paths with varying flaw geometries, leading to the ultimate failure in five typical modes based on the crack coalescence. Shear crack coalescence and tensile crack coalescence are identified through both macroscopic fracturing photos taken by the high-speed camera and microscopic surface morphology obtained by the scanning electron microscope (SEM)

Resource Allocation of UAV-Assisted IoT Node Secure Communication System

To balance the information security and energy harvest for massive internet-of-things (IoT) devices, an unmanned aerial vehicle (UAV)–assisted secure communication model is proposed in this paper. We extend the secure transmission model with physical layer security (PLS) to simultaneous wireless information and power transfer (SWIPT) technology and optimize the UAV trajectory, transmission power, and power splitting ratio (PSR). The nonconvex object function is decomposed into three subproblems. Then a robust iterative suboptimal algorithm based on the block coordinate descent (BCD) method is proposed to solve the subproblems. Numerical simulation results are provided to show the effectiveness of the proposed method. These results clearly illustrate that our resource allocation schemes surpass baseline schemes in terms of both transmit power and ratio of harvesting energy, while maintaining an approximately instantaneous secrecy rate

Novel Ensemble Tree Solution for Rockburst Prediction Using Deep Forest

The occurrence of rockburst can cause significant disasters in underground rock engineering. It is crucial to predict and prevent rockburst in deep tunnels and mines. In this paper, the deficiencies of ensemble learning algorithms in rockburst prediction were investigated. Aiming at these shortages, a novel machine learning model, deep forest, was proposed to predict rockburst risk. The deep forest combines the characteristics of deep learning and ensemble models, which can solve complex problems. To develop the deep forest model for rockburst prediction, 329 real rockburst cases were collected to build a comprehensive database for intelligent analysis. Bayesian optimization was proposed to tune the hyperparameters of the deep forest. As a result, the deep forest model achieved 100% training accuracy and 92.4% testing accuracy, and it has more outstanding capability to forecast rockburst disasters compared to other widely used models (i.e., random forest, boosting tree models, neural network, support vector machine, etc.). The results of sensitivity analysis revealed the impact of variables on rockburst levels and the applicability of deep forest with a few input parameters. Eventually, real cases of rockburst in two gold mines, China, were used for validation purposes while the needed data sets were prepared by field observations and laboratory tests. The promoting results of the developed model during the validation phase confirm that it can be used with a high level of accuracy by practicing engineers for predicting rockburst occurrences