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Numerous engineering case studies suggest that the complexity and oscillatory of landslide displacement types, influenced by diverse deformation patterns, monitoring errors, and varied observation time frames, present challenges for the application of traditional warning criteria in assessing landslide movement in practice. As such, this study attempts to propose a data-dependent early warning criterion through the dimensionless analysis of displacement for two typical landslide displacement patterns: exponential pattern and step-like pattern. The displacement trend in exponential pattern landslides is extracted using the simple moving average (SMA) method, with the onset of acceleration (OOA) determined through the application of the Pettitt test. The Deformation Standardized Anomaly Index (DSAI) is defined to quantitatively evaluate the alert levels corresponding to each monitored displacement data of both patterns. After validating the proposed criterion in ten landslide cases, results indicate that the landslides have a high probability of losing stability when DSAI exceeds four, and the landslide tends to stabilize as DSAI decreases. Compared to displacement speed ratio (DSR), the proposed DSAI is more applicable, aligning with the actual conditions of both landslides with exponential pattern and step-like pattern and exhibiting a relatively low false alarm rate, making it a valuable criterion for landslide early warning. [ABSTRACT FROM AUTHOR]

Timely evaluation of landslide risk based on displacement data is crucial for the effective warning of landslide events. However, the complexity and diversity of displacement curves obtained in the field make the selection of evaluation criteria and thresholds challenging and subjective. Thus, this study proposes a new early warning criterion called the deformation probability index (DPI) to overcome this limitation. By analyzing features of landslide displacement curves obtained from the field, four linetypes were identified, and differences in velocity data between pre-failure and post-failure landslides were summarized from a probabilistic perspective. Subsequently, the DPI is defined to assess the early warning level of a landslide by converting observed velocity values into standardized velocity values using cumulative distribution function standardization. The resulting DPI values are divided into five early warning intervals with three alerting levels to indicate the relative stability of the landslide. Applying the proposed method to 16 landslides from around the world demonstrates the effectiveness of the DPI criterion in evaluating landslide risk, with a high correlation between alerting levels and observed landslide occurrences. The DPI criterion demonstrates notable sensitivity exclusively to displacement velocity and monitoring frequency, exhibiting remarkable generality and accuracy, thus offering a new method for landslide early warning. [ABSTRACT FROM AUTHOR]

Analyzing and quantifying the deformation process of landslides is of paramount importance in facilitating landslide early warning. As such, this study is committed to proposing a universal phenomenological model for deformation stages division and early warning of landslides based on the kinematic features. First, five landslide deformation patterns were classified based on the creep theory, and suggestions for stage division of each deformation pattern are presented. Then, the statistical characteristics of landslide velocity were analyzed, and a probability-based deformation stage division method was proposed. Finally, the Comprehensive Standardized Deformation Index (CSDI) model, which includes the calculation of the CSDI M - M (Min-Max normalization) and CSDI M (Mean normalization) was proposed and verified in 24 landslides worldwide. The results show that, except for the oscillating pattern, the CSDI M - M is feasible in the stages division of all deformation patterns with a strong correspondence with the actual state of the landslides. The CSDI M is a reliable landslide warning criterion and threshold determination method, as it is effective in the early warning of imminent landslides with a low false alarm rate. The CSDI model provides new insight into the division of landslide deformation stages and landslide risk assessment. [ABSTRACT FROM AUTHOR]

A large number of laboratory investigations related to the permeability have been conducted on the sliding zones. Yet little attention has been paid to the particular sliding zones of the slide-prone Badong Formation. Here, we experimentally investigate the permeability nature and the mechanism of seepage in the viscous sliding zone of the Huangtupo Landslide. Saturated seepage tests have been performed first with consideration of six dry densities and thirteen hydraulic gradients, in conjunction with the mercury intrusion porosimetry test and scanning electron microscopy test for the microstructure analysis after seepage. The results show that seepage in the sliding zone soil does not follow Darcy's Law, since there is a threshold hydraulic gradient (i0) below which no flow is observed and a critical hydraulic gradient (icr) over which the hydraulic conductivity (K) tends to be stable. The percentage of bound water could be responsible for the occurrence of i0 and icr. Furthermore, pore size distributions (PSD) less than 0.6 µm and between 10 and 90 µm exhibit positive and negative correlations with the i0, respectively, indicating that the i0 is related to the PSD. The mechanism accounting for this result is that pore water pressure forces fine clay particles into the surrounding large pores and converts arranged particles to discretely distributed ones, thereby weakening the connectivity of pores. The seepages in the sliding zones behave differently from that in the sliding mass and sliding bed in response to the permeability. [ABSTRACT FROM AUTHOR]

A spoil dump slope is a kind of artificial slope formed by the accumulation of engineering waste residue from multiple sources, which results in high variability of its mechanical properties. Large amounts of groundwater inside such a structure can make it prone to slope instability. Although an air-injection drainage method has been developed in recent years to reinforce common soil slopes by facilitating drainage, research involving the underlying mechanisms still needs to be further strengthened. In this study, a gas–water two-phase flow theory was applied for unsaturated soil seepage in a spoil dump slope, and the uncertainty of the spoil’s physical and mechanical properties was considered in a reliability analysis to evaluate the reinforcement effect of the air-injection drainage method. Based on a case study, the changes in reliability index and failure probability were determined before and after injection under different air pressures. The results show that air-injection drainage methods can provide effective reinforcement for spoil dump slopes by significantly reducing porewater pressures in a potential slip zone, thus reducing the possibility of slope failure. Moreover, a spoil slope reinforced by this method can better resist the adverse impact of rainfall. [ABSTRACT FROM AUTHOR]

Objectives: Patients with T4 obstructive colorectal cancer (OCC) have a high mortality rate. Therefore, an accurate distinction between T4 and T1–T3 (NT4) in OCC is an important part of preoperative evaluation, especially in the emergency setting. This paper introduces three models of radiomics, deep learning, and deep learning-based radiomics to identify T4 OCC. Methods: We established a dataset of computed tomography (CT) images of 164 patients with pathologically confirmed OCC, from which 2537 slides were extracted. First, since T4 tumors penetrate the bowel wall and involve adjacent organs, we explored whether the peritumoral region contributes to the assessment of T4 OCC. Furthermore, we visualized the radiomics and deep learning features using the t-distributed stochastic neighbor embedding technique (t-SNE). Finally, we built a merged model by fusing radiomic features with deep learning features. In this experiment, the performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC). Results: In the test cohort, the AUC values predicted by the radiomics model in the dilated region of interest (dROI) was 0.770. And the AUC value of the deep learning model with the patches extended 20-pixel reached 0.936. Combining the characteristics of radiomics and deep learning, our method achieved an AUC value of 0.947 in the T4 and non-T4 (NT4) classification, and increased the AUC value to 0.950 after the addition of clinical features. Conclusion: The prediction results of our merged model of deep learning radiomics outperformed the deep learning model and significantly outperformed the radiomics model. The experimental results demonstrate that combining the peritumoral region improves the prediction performance of the radiomics model and the deep learning model. [ABSTRACT FROM AUTHOR]

Determination of the shear failure zones of rock joints leads to a better understanding of their shear mechanical behavior. In this paper, we present a low-cost approach to determining the shear damage zones of rock joints after direct shear tests. Experimental direct shear tests were performed on one pair of man-made and six pairs of natural samples to collect digital high-resolution images using a digital camera before and after the tests. For each sample, the images acquired before and after tests were brought into registration, and a disparity image was generated. Then, a binary image with the shear damage zones marked as true was generated, and the noise was filtered out with postprocessing methods. The traditional RISS and MN methods were employed to examine the performance of the proposed approach by comparing their accuracy, time consumption, adaptability and robustness. The results demonstrated that the proposed approach IR is comparable to the MN approach in terms of accuracy but saves 82.92% of the time, and has better accuracy, adaptability and robustness compared to the RISS approach. Highlights: A new approach based on the image registration technique was proposed to determine the shear damage zones of rock joints. The proposed approach showed better efficiency, accuracy, and robustness than the two traditional methods in the comparative study. The proposed approach has been integrated into the open-sourced SDZM toolbox, which facilitates engineering applications and comparative studies. [ABSTRACT FROM AUTHOR]

Accurate and reliable displacement forecasting plays a key role in landslide early warning. However, due to the epistemic uncertainties associated with landslide systems, errors are unavoidable and sometimes significant in traditional methods of deterministic point forecasting. Transforming traditional point forecasting into probabilistic forecasting is essential for quantifying the associated uncertainties and improving the reliability of landslide displacement forecasting. This paper proposes a hybrid approach based on bootstrap, extreme learning machine (ELM), and artificial neural network (ANN) methods to quantify the associated uncertainties via probabilistic forecasting. The hybrid approach consists of two steps. First, a bootstrap-based ELM is applied to estimate the true regression mean of landslide displacement and the corresponding variance of model uncertainties. Second, an ANN is used to estimate the variance of noise. Reliable prediction intervals (PIs) can be computed by combining the true regression mean, variance of model uncertainty, and variance of noise. The performance of the proposed hybrid approach was validated using monitoring data from the Shuping landslide, Three Gorges Reservoir area, China. The obtained results suggest that the Bootstrap-ELM-ANN approach can be used to perform probabilistic forecasting in the medium term and long term and to quantify the uncertainties associated with landslide displacement forecasting for colluvial landslides with step-like deformation in the Three Gorges Reservoir area. [ABSTRACT FROM AUTHOR]

Orientation statistics are prone to bias when surveyed with the scanline mapping technique in which the observed probabilities differ, depending on the intersection angle between the fracture and the scanline. This bias leads to 1D frequency statistical data that are poorly representative of the 3D distribution. A widely accessible estimator named after Terzaghi was developed to estimate 3D frequencies from 1D biased observations, but the estimation accuracy is limited for fractures at narrow intersection angles to scanlines (termed the blind zone). Although numerous works have concentrated on accuracy with respect to the blind zone, accuracy outside the blind zone has rarely been studied. This work contributes to the limited investigations of accuracy outside the blind zone through a qualitative assessment that deploys a mathematical derivation of the Terzaghi equation in conjunction with a quantitative evaluation that uses fractures simulation and verification of natural fractures. The results show that the estimator does not provide a precise estimate of 3D distributions and that the estimation accuracy is correlated with the grid size adopted by the estimator. To explore the potential for improving accuracy, the particular grid size producing maximum accuracy is identified from 168 combinations of grid sizes and two other parameters. The results demonstrate that the 2° × 2° grid size provides maximum accuracy for the estimator in most cases when applied outside the blind zone. However, if the global sample density exceeds 0.5°, then maximum accuracy occurs at a grid size of 1° × 1°. [ABSTRACT FROM AUTHOR]

Suggests a satellite system with sensors of visible and infrared spectrums for impending earthquake forecasts. Uses of infrared sensors on meteosat probes; Specifications of radiometers of the system; Conclusion.