A back-propagation neural-network-based displacement back analysis for the identification of the geomechanical parameters of the Yonglang landslide in China

Xigeda formation is a type of hundredmeter-thick lacustrine sediments of being prone to triggering landslides along the trunk channel and tributaries of the upper Yangtze River in China. The Yonglang landslide located near Yonglang Town of Dechang County in Sichuan Province of China, which was a typical Xigeda formation landslide, was stabilized by anti-slide piles. Loading tests on a loading-test pile were conducted to measure the displacements and moments. The uncertainty of the tested geomechanical parameters of the Yonglang landslide over certain ranges would be problematic during the evaluation of the landslide. Thus, uniform design was introduced in the experimental design,and by which, numerical analyses of the loading-test pile were performed using Fast Lagrangian Analysis of Continua(FLAC3D) to acquire a database of the geomechanical parameters of the Yonglang landslide and the corresponding displacements of the loadingtest pile. A three-layer back-propagation neural network was established and trained with the database, and then tested and verified for its accuracy and reliability in numerical simulations. Displacement back analysis was conducted by substituting the displacements of the loading-test pile to the well-trained three-layer back-propagation neural network so as to identify the geomechanical parameters of the Yonglang landslide. The neuralnetwork-based displacement back analysis method with the proposed methodology is verified to be accurate and reliable for the identification of the uncertain geomechanical parameters of landslides.

Displacement-based back analysis of mitigating the effects of displacement loss in underground engineering

Displacement-monitoring-based back analysis is a popular method for geomechanical parameter estimation.However,due to the delayed installation of multi-point extensometers,the monitoring curve is only a part of the overall one,leading to displacement loss.Besides,the monitoring and construction time on the monitoring curve is difficult to determine.In the literature,the final displacement was selected for the back analysis,which could induce unreliable results.In this paper,a displacement-based back analysis method to mitigate the influence of displacement loss is developed.A robust hybrid optimization algorithm is proposed as a substitute for time-consuming numerical simulation.It integrates the strengths of the nonlinear mapping and prediction capability of the support vector machine(SVM)algorithm,the global searching and optimization characteristics of the optimized particle swarm optimization(OPSO)algorithm,and the nonlinear numerical simulation capability of ABAQUS.To avoid being trapped in the local optimum and to improve the efficiency of optimization,the standard PSO algorithm is improved and is compared with other three algorithms(genetic algorithm(GA),simulated annealing(SA),and standard PSO).The results indicate the superiority of OPSO algorithm.Finally,the hybrid optimization algorithm is applied to an engineering project.The back-analyzed parameters are submitted to numerical analysis,and comparison between the calculated and monitoring displacement curve shows that this hybrid algorithm can offer a reasonable reference for geomechanical parameters estimation.

Landslide susceptibility mapping using the infinite slope,SHALSTAB,SINMAP,and TRIGRS models in Serra do Mar,Brazil

Slope failure triggered by heavy rainfall is very common in tropical and subtropical regions and a cause of major social and economic damage.Landslide susceptibility maps can be generated using geographical information systems(GIS)and limit equilibrium slope stability models coupled or not to hydrological equations.This study investigated the efficacy of four models used for slope stability analysis in predicting landslide-susceptible areas in a GIS environment.The selected models are the infinite slope,the shallow slope stability model(SHALSTAB),the stability index mapping(SINMAP),and the transient rainfall infiltration and grid-based regional slope-stability(TRIGRS).For comparisons,the authors(a)included the infinite slope equation in all models,(b)clearly defined input parameters and failure triggering mechanisms for each simulation(soil depth,water table height,rainfall intensity),(c)determined appropriate values for each model to obtain stability levels that represented similar hydrogeotechnical conditions,and(d)considered upper-third areas of landslide scars to estimate the reliability of susceptibility maps using validation indices.An intense rainfall event occurred in Serra do Mar,Brazil in January 2014 triggered hundreds of landslides and was used for back analysis and evaluation of the slope stability analysis models.When rainfall intensity is not considered,the four models produced very similar results.The most reliable landslide susceptibility map was generated using TRIGRS and considering the granite residual granite soils geological-geotechnical unit,subjected to a rainfall intensity of 210 mm for 2 h under unsaturated conditions.

Empirical analysis for the characterization of geo-mechanical strength and pressure regime:Implications on hydraulic fracturing stimulation

Among the several activities involved in oil exploration are the determination of hydrocarbon in-place and mechanical competency of the oil reservoir.The pressure regimes of the formation have also become vital properties which must be well known to ensure preliminary awareness of the hydraulic fracturing.This study seeks to adopt a prediction strategy of the overall geo-mechanical competency and strength of the formation,using a less stressful computational process and an empirical analysis,developed using three wells from ED BON area in parts of Niger Delta.Elastic constants such as Poisson Ratio,Young's,Shear and Bulk moduli which are the parameters for characterizing rock mechanical properties were estimated,as well as the subsurface formation pressures and the associated fracture gradient using P-wave sonic and density logs.The results from the analysis showed that there is correlation between elastic strength and fracture pressure.