Progressive Modelling of the Gravity-induced Landslide Using the Local Dynamic Strength Reduction Method

The failure of slope is a progressive process, and the whole sliding surface is caused by the gradual softening of soil strength of the potential sliding surface. From this viewpoint, a local dynamic strength reduction method is proposed to capture the progressive failure of slope. This method can calculate the warning deformation of landslide in this study. Only strength parameters of the yielded zone of landslide will be reduced by using the method. Through continuous local reduction of the strength parameters of the yielded zone, the potential sliding surface developed gradually and evolved to breakthrough finally. The result shows that the proposed method can simulate the progressive failure of slope truly. The yielded zone and deformation of landslide obtained by the method are smaller than those of overall strength reduction method. The warning deformation of landslide can be obtained by using the local dynamic strength reduction method which is based on the softening characteristics of the sliding surface.

Slope analysis based on local strength reduction method and variable-modulus elasto-plastic model

Employing an ideal elasto-plastic model,the typically used strength reduction method reduced the strength of all soil elements of a slope.Therefore,this method was called the global strength reduction method(GSRM).However,the deformation field obtained by GSRM could not reflect the real deformation of a slope when the slope became unstable.For most slopes,failure occurs once the strength of some regional soil is sufficiently weakened; thus,the local strength reduction method(LSRM)was proposed to analyze slope stability.In contrast with GSRM,LSRM only reduces the strength of local soil,while the strength of other soil remains unchanged.Therefore,deformation by LSRM is more reasonable than that by GSRM.In addition,the accuracy of the slope's deformation depends on the constitutive model to a large degree,and the variable-modulus elasto-plastic model was thus adopted.This constitutive model was an improvement of the Duncan–Chang model,which modified soil's deformation modulus according to stress level,and it thus better reflected the plastic feature of soil.Most importantly,the parameters of the variable-modulus elasto-plastic model could be determined through in-situ tests,and parameters determination by plate loading test and pressuremeter test were introduced.Therefore,it is easy to put this model into practice.Finally,LSRM and the variable-modulus elasto-plastic model were used to analyze Egongdai ancient landslide.Safety factor,deformation field,and optimal reinforcement measures for Egongdai ancient landslide were obtained based on the proposed method.

EXISTENCE OF PERIODIC SOLUTIONS FOR A DIFFERENTIAL INCLUSION SYSTEMS INVOLVING THE p(t)-LAPLACIAN

We study a nonlinear periodic problem driven by the p（t）-Laplacian and having a nonsmooth potential （hemivariational inequalities）. Using a variational method based on nonsmooth critical point theory for locally Lipschitz functions, we first prove the existence of at least two nontrivial solutions under the generalized subquadratic and then establish the existence of at least one nontrivial solution under the generalized superquadratic.

City level CO_(2) and local air pollutants co-control performance evaluation: A case study of 113 key envir on mental protection cities in China

‘Co-control',or synergistically reducing CO_(2)and local air polluta nt emissions,is an important strategy for cities to achieve'low carb on'and'blue sky'simultaneously.However,there were few studies to evaluate and compare the level of co-control of CO_(2) and local air pollutants in cities year.The present study proposed qualitative and quantitative methods to evaluate the level of co-control of CO_(2)and three local air pollutant(SO_(2).NOX,and particulate matter PM)emissions in key environmental protection cities in China over two periods(2012-2015 and 2015-2018).Statistical analysis found that,though three local air pollutant emissions positively correlated with CO_(2) emission,no significantly positive correlation was found between local air pollutants emission reductions and CO_(2) emission reductions,indicating that co-control effects were poor in general.By using the co-control effect coordinate system,qualitative evaluation showed that less than half of the sample cities could achieve co-control of the total amount of CO_(2) and local air pollutants.By employing the indicator of emission reduction equivalence(EReq),quantitative evaluation showed that the co-control level of the sample cities improved in 2015-2018 compared with 2012-2015.Further regression analysis showed that,reducing coal consumption and economic development significantly enhanced the co-control performance of the observed cities.The present case study proved that the proposed methods for evaluation and comparison of the city co-control performance works well,and can be used in other countries and regions to promote global cities racing to carbon and local air pollutants co-control.