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This paper addresses how residents can safely access shelters from their buildings. Firstly, we investigated Xiaying Village, a typical community with severe buildings damage at the epicentre of the Ms5.0 magnitude earthquake in Tonghai that occurred on August 13, 2018, and found that the injured residents when escaping resulted from the damage to masonry buildings. Secondly, a probabilistic approach combining response spectrum and allowable maximum acceleration is proposed to estimate the damage risk of masonry buildings with inadequate seismic performance in old communities. Thirdly, three models are proposed to assess the risk of the escape path, which include all scenarios of the escape of residents to shelters. Finally, an accessibility analysis of the paths that residents may choose to escape to the shelter is presented. The results of the community escape case study in the Tonghai earthquake show that the probabilistic approach to estimating the escape risk is feasible, and the number of damaged buildings in the escape path dictates the accessibility of residents to the shelter. The proposed method is important for guiding old community residents to escape when a strong earthquake strikes, and it is an effective way to improve community disaster resilience. [ABSTRACT FROM AUTHOR]

To investigate the mechanical properties and fracture mechanism of rocks containing multiple flaws after freeze–thaw (F-T) treatment, sandstone specimens with multiple flaws were prepared and subjected to F-T treatment and uniaxial compression tests. The global strain field on the specimen surface during the loading process was calculated using the digital image correlation (DIC) method, and four grayscale parameters were introduced to quantitatively characterize the evolution of the strain field. Then, the differentiation rates (DRs) of these parameters were defined, and the correlation between these parameters and the rock fracture state was analyzed. The F-T cycling deteriorated the mechanical properties and increased the strain concentration of the specimens. The calculation results of the horizontal strains at the measurement points along the crack propagation path showed that the crack initiation of the specimens after F-T treatment occurred earlier, and their brittle failure characteristics were less pronounced. The specimen fracture process was successfully described by combining DIC and statistical grayscale parameters. The evolution pattern of each grayscale parameter of the specimens was generally similar before and after F-T treatment in that it exhibited disorderly fluctuation first, then steady development, and finally accelerated change. The grayscale parameters changed abruptly at the initiation of wing cracks and antiwing cracks, resulting in sharp points in the DR vs. strain curves. These points can be defined as precursor points P1 and P2 for identifying the deformation and fracture state of the rock. The F-T cycling led to the delayed occurrence of P1 and the advanced occurrence of P2. [ABSTRACT FROM AUTHOR]

To gain deep insights into the reinforcement effect of weak inclusions on jointed rock masses containing an opening, natural sandstone specimens containing multiple non-persistent joints and an opening were prepared, and two different cases, unfilled and filled openings, were considered. Digital image correlation (DIC) was applied to characterize the deformation fields of specimens during loading. Two measures extended from the DIC, the displacement vector and multivariate measure of strain dispersion, were proposed to identify and quantitatively analyze the cracking mechanism. Weak inclusions were found to improve the mechanical properties. The displacement vector fields around the newly formed cracks were calculated, and then, four types of cracks were recognized: direct tensile cracks, relative tensile cracks, shear cracks, and mixed cracks. Based on the fracture nature of cracks, nine types of crack coalescence among joints and six categories between openings and joints were summarized. The influence of weak inclusions on the cracking behavior of jointed sandstones was further clarified. Many more cracks were detected in the specimens containing a filled opening, which was validated by the fractal dimensions of the ultimate fracture surfaces. The differentiation rate of the effective variance (DREV) was defined to describe the dispersion of full-field strain data. The DREV was extremely close to 0 at the initial compacting and elastic deformation stage and then exhibited anomalous responses to cracking events. The first peak in the DREV–strain curve represents crack initiation, which can be regarded as a precursor. [ABSTRACT FROM AUTHOR]

Displacement prediction is critical for the early detection of landslides, and the empirical, statistical, and machine learning models have been commonly used. In the Three Gorges reservoir area (TGRA), many landslides experience step-like deformations due to the periodic change of influencing factors. In this study, a novel and dynamic model is proposed to predict the displacements of step-like landslides. Two typical landslides in the TGRA are taken as case studies. Variational mode decomposition (VMD) is used to decompose the cumulative displacements into stochastic, periodic, and trend components. The influencing factors are decomposed into low-frequency and high-frequency components. Two principles, including the physical connotation and minimum sample entropy, are employed to optimize the VMD parameters. The trend displacement is fitted and predicted by a polynomial expression with an optimized order, and the periodic and stochastic displacements are dynamically modeled by the bidirectional long short-term memory (LSTM) model. The cumulative displacement prediction is the addition of the three displacement components. The proposed model has been shown to exhibit superior performance in the displacement prediction of step-like landslides. To achieve acceptable prediction, a size ratio between the training and testing datasets greater than or equal to five is recommended. The min–max and zero-mean normalizations are applicable to the data preprocessing of this work. [ABSTRACT FROM AUTHOR]

Physical modeling of rock masses containing complex fracture geometries is an important but very challenging task in rock mechanics and rock engineering. In this work, a novel integration of 3D printing and water-soluble casting is presented for the preparation of rock-model specimens with fracture networks, which is similar to the lost-wax casting process. The fracture network is created with water-soluble polyvinyl alcohol (PVA) material by 3D printing. The intact rock matrix is prepared with cement paste. Uniaxial compressive tests combined with the digital image correlation (DIC) technique are conducted to investigate the mechanical properties and failure process of the prepared rock-model specimens. The differentiation rate of the effective variance is defined to recognize the precursory anomalies during the loading process. The mechanical anisotropy is further investigated by a series of rotated models. The results show that the proposed methodology has distinct advantages for replicating rocks with complex fracture geometries in terms of high accuracy for fabricating fracture networks, high repeatability of mechanical properties, and high strength and brittleness of rock matrices. Due to the presence of complex fracture geometries, the nonlinear mechanical responses of the fractured rock-model specimens, including strain-softening and anisotropy, are observed in our experiments. [ABSTRACT FROM AUTHOR]

Shear-box experiments, Rock Failure Process Analysis (RFPA) simulations, and box-counting fractal analysis on rock-like models are conducted to investigate the influence of intermittent artificial crack density on the shear fracturing and fractal behavior of rock bridges in jointed rock slopes. The artificial crack geometry of the conceptual rock bridge model is a combination of two edge-notched artificial cracks and imbedded artificial cracks with different intermittent artificial crack densities. By numerical shear-box tests, deep insight into the mesoscopic mechanism of crack evolution is gained, and the simulated failure patterns are in accordance with experimental results. Three types of failure patterns are identified: shear mode, mixed shear/tensile mode, and tensile mode. The RFPA simulations demonstrate that macroscale shear cracks form as damage belts consisting of many tensile/shear mesocracks, as typically observed in microscopic experimental work. The failure pattern is mostly influenced by the intermittent artificial crack density, whereas the peak shear strength is related to the failure pattern. As the intermittent artificial crack density increases, the failure pattern changes from shear mode to mixed shear/tensile mode and then to tensile mode, resulting in a decrease in the peak shear strength. The regression analysis shows that the relationship between the peak shear strength and intermittent artificial crack density can be expressed by an exponential decay model. Furthermore, digital image processing and box-counting fractal analyses are performed on the shear fracture surfaces of the physical and numerical models to describe the fractal behavior. The relationships between the fractal dimension and peak shear strength are analyzed, and strong correlations that display an exponential decay function are found. [ABSTRACT FROM AUTHOR]

Detecting suspicious behavior from high definition (HD) videos is always a complex and time-consuming process. To solve that problem, a fast suspicious behavior recognition method is proposed based on motion vectors. In this paper, the data format and decoding features of HD videos are analyzed. Then, the characteristics of suspicious activities and the ways of obtaining motion vectors directly from the video stream are concluded. Besides, the motion vectors are normalized by taking the reference frames into account. The feature vectors that display the inter-frame and intra-frame information of the region of interest are extracted. Gaussian radial basis function is employed as the kernel function of the support vector machines (SVM). It also realizes the detection and classification of suspicious behavior in HD videos. Finally, an extensive set of experiments are performed and this method is compared with some of the most recent approaches in the field using publicly available datasets as well as a new annotated human action dataset including actions performed in complex scenarios. [ABSTRACT FROM AUTHOR]

Finger-vein recognition is an increasingly promising biometric identification technology in terms of its high identification accuracy and prominent security performance. The main challenge faced by finger-vein recognition is the low recognition performance caused by segmentation error and local difference. To tackle this challenge, a finger-vein recognition method with modified binary tree (MBT) model is proposed in this paper. MBT model is used to describe the relationship and spatial structure of vein branches quantitatively. Based on the MBT model, four stages including rough selection, model correction, segment matching, and comprehensive judgment are presented to achieve a robust matching for finger-vein. Experiments demonstrate that the proposed method can boost the performance of finger-vein recognition that is degraded by segmentation error and local difference. While maintaining low complexity, the proposed method achieves 0.12 % equal error rate in the introduced dataset with 8,100 finger-vein images from 150 participants, which outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]