Slope stability analysis based on big data and convolutional neural network

The Limit Equilibrium Method(LEM)is commonly used in traditional slope stability analyses,but it is time-consuming and complicated.Due to its complexity and nonlinearity involved in the evaluation process,it cannot provide a quick stability estimation when facing a large number of slopes.In this case,the convolutional neural network(CNN)provides a better alternative.A CNN model can process data quickly and complete a large amount of data analysis in a specific situation,while it needs a large number of training samples.It is difficult to get enough slope data samples in practical engineering.This study proposes a slope database generation method based on the LEM.Samples were amplified from 40 typical slopes,and a sample database consisting of 20000 slope samples was established.The sample database for slopes covered a wide range of slope geometries and soil layers’physical and mechanical properties.The CNN trained with this sample database was then applied to the stability prediction of 15 real slopes to test the accuracy of the CNN model.The results show that the slope stability prediction method based on the CNN does not need complex calculation but only needs to provide the slope coordinate information and physical and mechanical parameters of the soil layers,and it can quickly obtain the safety factor and stability state of the slopes.Moreover,the prediction accuracy of the CNN trained by the sample database for slope stability analysis reaches more than 99%,and the comparisons with the BP neural network show that the CNN has significant superiority in slope stability evaluation.Therefore,the CNN can predict the safety factor of real slopes.In particular,the combination of typical actual slopes and generated slope data provides enough training and testing samples for the CNN,which improves the prediction speed and practicability of the CNN-based evaluation method in engineering practice.

Optimal CNN-based semantic segmentation model of cutting slope images

This paper utilizes three popular semantic segmentation networks,specifically DeepLab v3+,fully convolutional network(FCN),and U-Net to qualitively analyze and identify the key components of cutting slope images in complex scenes and achieve rapid image-based slope detection.The elements of cutting slope images are divided into 7 categories.In order to determine the best algorithm for pixel level classification of cutting slope images,the networks are compared from three aspects:a)different neural networks,b)different feature extractors,and c)2 different optimization algorithms.It is found that DeepLab v3+with Resnet18 and Sgdm performs best,FCN 32s with Sgdm takes the second,and U-Net with Adam ranks third.This paper also analyzes the segmentation strategies of the three networks in terms of feature map visualization.Results show that the contour generated by DeepLab v3+(combined with Resnet18 and Sgdm)is closest to the ground truth,while the resulting contour of U-Net(combined with Adam)is closest to the input images.