Deterministic and probabilistic analysis of tunnel face stability using support vector machine

This paper develops a convenient approach for deterministic and probabilistic evaluations of tunnel face stability using support vector machine classifiers. The proposed method is comprised of two major steps, i.e., construction of the training dataset and determination of instance-based classifiers. In step one, the orthogonal design is utilized to produce representative samples after the ranges and levels of the factors that influence tunnel face stability are specified. The training dataset is then labeled by two-dimensional strength reduction analyses embedded within OptumG2. For any unknown instance, the second step applies the training dataset for classification, which is achieved by an ad hoc Python program. The classification of unknown samples starts with selection of instance-based training samples using the k-nearest neighbors algorithm, followed by the construction of an instance-based SVM-KNN classifier. It eventually provides labels of the unknown instances, avoiding calculate its corresponding performance function. Probabilistic evaluations are performed by Monte Carlo simulation based on the SVM-KNN classifier. The ratio of the number of unstable samples to the total number of simulated samples is computed and is taken as the failure probability, which is validated and compared with the response surface method.

Automated summary

This paper presents a convenient approach for deterministic and probabilistic evaluations of tunnel face stability using support vector machine classifiers, consisting of constructing the training dataset and determining instance-based classifiers. The method utilizes orthogonal design to generate representative samples, labels the training dataset through two-dimensional strength reduction analyses, and applies ad hoc Python program for classification. Probabilistic evaluations are conducted through Monte Carlo simulation based on SVM-KNN classifier, computing the ratio of unstable samples to total simulated samples as failure probability, validated and compared with response surface method.