In Situ Shear Test for Revealing the Mechanical Properties of the Gravelly Slip Zone Soil.

Slip zone soil is usually composed of clay or silty clay; in some special geological environments, it contains gravels, which make the properties of the slip zone soil more complex. Unfortunately, in many indoor shear tests, gravels are removed to meet the demands of apparatus size, and the in situ mechanical property of the gravelly slip zone soil is rarely studied. In this study, the shear mechanical property of the gravelly slip zone soil of Huangtupo landslide in the Three Gorges Reservoir area of China was investigated by the in situ shear test. The test results show that the shear deformation process of the gravelly slip zone soil includes an elastic deformation stage, elastic-plastic deformation stage, and plastic deformation stage. Four functions were introduced to express the shear constitutive model of the gravelly slip zone soil, and the asymmetric sigmoid function was demonstrated to be the optimum one to describe the relationship of the shear stress and shear displacement with a correlation coefficient of 0.986. The comparison between the in situ test and indoor direct shear test indicates that gravels increase the strength of the slip zone soil. Therefore, the shear strength parameters of the gravelly slip zone soil obtained by the in situ test are more preferable for evaluating the stability of the landslide and designing the anti-slide structures.

Application of Well Drainage on Treating Seepage-Induced Reservoir Landslides.

In the process of rapid drawdown of reservoir water level, the seepage force in the slide mass is an important factor for the stability reduction and deformation increment of many landslides in the reservoir areas. It is feasible to improve the stability of seepage-induced landslide by employing a drainage well to reduce or eliminate the water head difference that generates the seepage force. In this paper, the Shuping landslide, a typical seepage-induced landslide in the Three Gorges Reservoir area of China, is taken as an example. A series of numerical simulations were carried out to figure out the seepage field, and the Morgenstein-Price method was adopted to calculate the landslide stability. Then the influence of horizontal location of the drainage well, drainage well depth, drainage mode on the landslide treatment effect, and the applicability of drainage well were analyzed. The results show that: (1) landslide stability increases obviously with the well depth in the slide mass, while the increment of landslide stability with the well depth is limited in the slide bed; (2) the sensitivity of the stability improvement with the depth is greater than that with the horizontal positions of the drainage wells in the slide mass; (3) the drainage well is suggested to be operated when the reservoir water falls rather than operates all the time; and (4) the drainage method is most suitable for landslides with low and medium permeability. These results provide deep insights into the treatment of seepage-induced landslides.

Forecasting of Landslide Displacement Using a Probability-Scheme Combination Ensemble Prediction Technique.

Data-driven models have been extensively employed in landslide displacement prediction. However, predictive uncertainty, which consists of input uncertainty, parameter uncertainty, and model uncertainty, is usually disregarded in deterministic data-driven modeling, and point estimates are separately presented. In this study, a probability-scheme combination ensemble prediction that employs quantile regression neural networks and kernel density estimation (QRNNs-KDE) is proposed for robust and accurate prediction and uncertainty quantification of landslide displacement. In the ensemble model, QRNNs serve as base learning algorithms to generate multiple base learners. Final ensemble prediction is obtained by integration of all base learners through a probability combination scheme based on KDE. The Fanjiaping landslide in the Three Gorges Reservoir area (TGRA) was selected as a case study to explore the performance of the ensemble prediction. Based on long-term (2006-2018) and near real-time monitoring data, a comprehensive analysis of the deformation characteristics was conducted for fully understanding the triggering factors. The experimental results indicate that the QRNNs-KDE approach can perform predictions with perfect performance and outperform the traditional backpropagation (BP), radial basis function (RBF), extreme learning machine (ELM), support vector machine (SVM) methods, bootstrap-extreme learning machine-artificial neural network (bootstrap-ELM-ANN), and Copula-kernel-based support vector machine quantile regression (Copula-KSVMQR). The proposed QRNNs-KDE approach has significant potential in medium-term to long-term horizon forecasting and quantification of uncertainty.

The construction of a new oncolytic herpes simplex virus expressing murine interleukin-15 with gene-editing technology.

The treatment of tumors with oncolytic viruses is an important cancer immunotherapy strategy. Interleukin-15 (IL-15) can enhance the antitumor effect of natural killer cells and T cells. An oncolytic herpes simplex type II virus (oHSV2-mIL-15CherryFP) expressing mouse IL-15 was constructed using the CRISPR/Cas9 system, and its antitumor activity in vitro and in vivo was evaluated. In vitro, the mouse interleukin-15 (mIL-15) present in the culture supernatant expressed by oHSV2-mIL-15CherryFP was able to enhance the killing of CT26-GFP tumor cells by T cells. In addition, the intratumoral injection of oHSV2-mIL-15CherryFP inhibited tumor growth in the CT26-iRFP and BGC823-iRFP model. These results indicate that the use of oncolytic herpes simplex virus expressing IL-15 may be a potential therapeutic strategy in tumor immunotherapy.