In underground mining, the dip angle is one of the widely recognized factors that cause the asymmetric deformation of the goaf/stope roof, but characterizing the degree of asymmetric roof deformation ...is still a challenge. The goal of this research is to try to solve this problem with a theoretical model and numerical method. In an inclined ore seam, the mining load produces both normal and tangential effects on the inclined roof. A theoretical model was developed employing thin plate theory for enabling describe the asymmetric deformation of the roof caused by inclination. The proposed model describes not only the bending deformation state of the roof but also the deformation characteristics. Subsequently, the law of asymmetric deformation of roofs with varying inclinations was presented by numerical method. Under the same conditions, the numerical results of the asymmetric deformation of the roof are consistent with the theoretical results. Finally, the degree of asymmetrical deformation was characterized and quantified by the distance between the maximum subsidence point and the center of the roof. There exist three modes of asymmetric deformation, which are controlled by both dip angle and in-situ stress ratio. The results show that the shear load caused by dip angle is the root cause of asymmetric deformation of the roof. This study provides a theoretical basis for the asymmetric deformation control of the inclined roof.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The joint morphology is multiscale. The effect of each asperity order on the mechanical properties of joints is different. The shear mechanical properties of joint specimens are related to its ...surface damage characteristics. At present, there are still few studies on the effect of roughness on the shearing mechanical properties of joint from the perspective of damage of each asperity order. In this paper, the standard roughness profile was chosen as initial morphology. The standard roughness profile was decomposed into waviness and unevenness by the method combine the ensemble empirical mode decomposition (EEMD) and the cut-off criterion. Then, the joint specimen which contains waviness and unevenness and the specimen which only contains waviness were prepared by the 3D engraving technology. The 40 sets of joint specimens with different asperity order were subjected to direct shear tests under different normal stresses. Based on the 3D scanning technology and ICP iterative method, the damaged area and the damage volume were calculated. Based on the damage volume data and the acoustic emission (AE) data, the effect of asperity order to the joint mechanical behaviour was studied. The results indicate that (1) under low normal stress, the unevenness plays a control role in the failure mode of the joint specimen. Under low normal stress, the joint surface containing only waviness exhibits slip failure, and the joint surface with unevenness exhibits shear failure. With the increase of the normal stress, the failure mode of the specimen containing only waviness changes from slip failure to shear failure; (2) the unevenness controls the damage degree of the joint specimen. The damaged area, damage volume, AE energy rate, and accumulative AE energy of the joint specimen with unevenness are larger than those of the specimen with only waviness, and this difference increases with the normal stress increase; (3) the difference between the joint specimen with unevenness and specimen with only waviness mainly exists in the prepeak nonlinear stage and the postpeak softening stage. The characteristic parameters of acoustic emission generated in the postpeak softening stage of the joint specimen with unevenness are greater than those of the specimen with only waviness. This phenomenon can be used to explain the stress drop difference at the postpeak softening stage; (4) the AE b value can be used to evaluate the damage of joint specimens. Analysing the damage difference of each asperity order under different normal stresses is of great significance to the analysis of the influence of the morphology of the joint surface on the mechanical properties of the joint.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The morphology of the joint surface is multi-scale, and it can be divided into first-order asperity (waviness) and second-order asperity (unevenness). At present, the joint roughness characterization ...formula considers only the morphology contribution of waviness and unevenness components and does not fully consider their mechanical contribution. At same time, the relationship between the mechanical contribution and the morphology contribution is still unclear. Thus, the characterization formula considering the mechanical contribution of waviness and unevenness needs to be further studied. In this study, the standard joint roughness coefficient (JRC) profiles were first decomposed into waviness and unevenness. Then, three types of joint specimens with different asperity orders (flat, the standard JRC profile, and the profile containing only waviness) were prepared by the 3D engraving technique. Finally, direct shear tests were carried out on 39 sets of red sandstone joint specimens under three normal stresses. The mechanical contributions of waviness and unevenness were studied, the relationship between the mechanical contribution and the morphology contribution of waviness and unevenness was analyzed, and the characterization formula considering the mechanical contribution of waviness and unevenness was established. The results showed that the following: (1) the method combining the ensemble empirical mode decomposition (EEMD) and the critical decomposition level could be used to separate the waviness and unevenness from the joint surface; (2) the mechanical contribution of the waviness and unevenness decreased with the increase in normal stress; (3) the relationship between the mechanical contribution ratio and the statistical parameter ratio of the waviness and unevenness can be describe by power function; and (4) the roughness characterization formula considering the mechanical contribution and morphology contribution was established. This study will enhance the accurate evaluation of the roughness coefficient and shear strength of the joint specimen.
The deformation and failure of composite rock masses at the contact zone can cause serious safety hazards in underground engineering. Therefore, uniaxial compression experiment and numerical studies ...of composite samples were conducted. Combined with theoretical analysis, the effects of differences in the mechanical properties of two media on the mechanical properties and failure form of composite samples were investigated. The results show that the difference in mechanical properties of two media weakened the strength and elastic modulus of the composite sample and resulted in complex failure form of the sample. Additionally, the peaks of the AE frequencies of the two media occurred successively after the peak stress of the sample. At the macrolevel, the two media failed successively. These effects are more pronounced as the degree of difference in the mechanical properties of the two media increased. The difference in mechanical properties of the two media resulted in uncoordinated deformation and constrained stress near the contact interface, and the difference in Poisson’s ratio is the main factor. The influence (enhanced or weakened) of uncoordinated deformation on the mechanical properties of the composite sample is affected by differences in the elastic modulus and Poisson’s ratios of the two media.
Full text
Available for:
EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Traditional rock mass is opaque and the internal deformation and fracture process cannot be observed directly, using transparent materials to make similar models is an effective solution. E-44 type ...epoxy resin is used as aggregate, triethanolamine is used as curing agent, and alcohol rosin saturated solution (RSS) is added to adjust brittleness. Based on comprehensive experimental methods, the influence of the ratio of epoxy resin to triethanolamine and the content of RSS on the density, uniaxial compressive strength, peak strain, elastic modulus, tensile strength, cohesion and friction angle of transparent rock mass are studied in series. The results show that the ratio of epoxy resin to triethanolamine plays a leading role in the compressive strength, elastic modulus, tensile strength, cohesion and friction angle of similar transparent materials, while the content of the RSS plays a leading role in the density and peak strain. The density and peak strain is positively correlated with the ratio of epoxy resi
Bridge detection from Synthetic Aperture Radar (SAR) images has very important strategic significance and practical value, but there are still many challenges in end-to-end bridge detection. In this ...paper, a new deep learning-based network is proposed to identify bridges from SAR images, namely, multi-resolution attention and balance network (MABN). It mainly includes three parts, the attention and balanced feature pyramid (ABFP) network, the region proposal network (RPN), and the classification and regression. First, the ABFP network extracts various features from SAR images, which integrates the ResNeXt backbone network, balanced feature pyramid, and the attention mechanism. Second, extracted features are used by RPN to generate candidate boxes of different resolutions and fused. Furthermore, the candidate boxes are combined with the features extracted by the ABFP network through the region of interest (ROI) pooling strategy. Finally, the detection results of the bridges are produced by the classification and regression module. In addition, intersection over union (IOU) balanced sampling and balanced L1 loss functions are introduced for optimal training of the classification and regression network. In the experiment, TerraSAR data with 3-m resolution and Gaofen-3 data with 1-m resolution are used, and the results are compared with faster R-CNN and SSD. The proposed network has achieved the highest detection precision (P) and average precision (AP) among the three networks, as 0.877 and 0.896, respectively, with the recall rate (RR) as 0.917. Compared with the other two networks, the false alarm targets and missed targets of the proposed network in this paper are greatly reduced, so the precision is greatly improved.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
•Employing deep learning techniques for bridge detection from SAR imagery.•Proposing multi-scale attention mechanism for effective feature extraction.•Solving the sample imbalance problem using the ...Gradient Harmonizing Mechanism.•Our framework outperforms state-of-the-art deep learning methods in experiments.
Automatic river bridge detection is a typical and valuable application for SAR image analysis. However, the background information of SAR image is complex, and there are many specious targets with similar features, such as road, ponds and ridges, which usually cause false alarms. And current river bridge detection methods fail to handle these interference efficiently. Therefore, this paper applies deep learning to SAR and proposes a new river bridge detection algorithm, which is named as Single Short Detection-Adaptively Effective Feature Fusion (SSD-AEFF). It can effectively reduce the interference of noisy information, and achieve fast and high-precision detection of river bridges in complex SAR imagery. SSD-AEFF is based on SSD, and AEFF module is innovated to enhance the multi-scale feature maps together with effective Squeeze-Excitation (eSE) module to further fuse effective features and decrease the interference of background information. Additionally, Non-Maximum Suppression (NMS) is used to screen out redundant candidate boxes to produce the final detection result. Moreover, Gradient Harmonizing Mechanism (GHM) loss function is introduced to solve the problem of sample imbalance in the training process. Experimental results on TerraSAR data compared with existing baseline models demonstrate the superiority of the proposed SSD-AEFF algorithm.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The detection of airports from Synthetic Aperture Radar (SAR) images is of great significance in various research fields. However, it is challenging to distinguish the airport from surrounding ...objects in SAR images. In this paper, a new framework, multi-level and densely dual attention (MDDA) network is proposed to extract airport runway areas (runways, taxiways, and parking lots) in SAR images to achieve automatic airport detection. The framework consists of three parts: down-sampling of original SAR images, MDDA network for feature extraction and classification, and up-sampling of airports extraction results. First, down-sampling is employed to obtain a medium-resolution SAR image from the high-resolution SAR images to ensure the samples (500 × 500) can contain adequate information about airports. The dataset is then input to the MDDA network, which contains an encoder and a decoder. The encoder uses ResNet_101 to extract four-level features with different resolutions, and the decoder performs fusion and further feature extraction on these features. The decoder integrates the chained residual pooling network (CRP_Net) and the dual attention fusion and extraction (DAFE) module. The CRP_Net module mainly uses chained residual pooling and multi-feature fusion to extract advanced semantic features. In the DAFE module, position attention module (PAM) and channel attention mechanism (CAM) are combined with weighted filtering. The entire decoding network is constructed in a densely connected manner to enhance the gradient transmission among features and take full advantage of them. Finally, the airport results extracted by the decoding network were up-sampled by bilinear interpolation to accomplish airport extraction from high-resolution SAR images. To verify the proposed framework, experiments were performed using Gaofen-3 SAR images with 1 m resolution, and three different airports were selected for accuracy evaluation. The results showed that the mean pixels accuracy (MPA) and mean intersection over union (MIoU) of the MDDA network was 0.98 and 0.97, respectively, which is much higher than RefineNet and DeepLabV3. Therefore, MDDA can achieve automatic airport extraction from high-resolution SAR images with satisfying accuracy.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The automatic extraction of airport runway areas from high-resolution Synthetic Aperture Radar (SAR) images is of great research significance in the military and civilian fields. However, it is still ...challenging to distinguish the airport from surrounding objects in SAR images. In this article, a new framework is proposed to extract airport runway areas (runways, taxiways, packing lots, and aircrafts) in a fast and automatic manner. The framework is based on the Geospatial Contextual Attention Mechanism (GCAM) for geospatial feature learning and classification, which is employed together with the down-sampling and coordinate mapping modules. To evaluate the performance of the proposed framework, three large-scale Gaofen-3 SAR images with 1m resolution are utilized in the experiment. According to the results, Mean Pixels Accuracy (MPA) and Mean Intersection Over Union (MIOU) of the GCAM are 0.9850 and 0.9536, respectively, which outperform RefineNet, DeepLabV3+, and MDDA. The training time of GCAM for the dataset is 2.25h, and the average testing time for the five SAR images is only 18.15s. Therefore, GCAM can offer rapid and automatic airport detection from high-resolution SAR images with high accuracy, which can further be employed to mark the airport to greatly improve the detection accuracy of the aircrafts.
PDF
