Abstract
Classification of the surrounding rock is the basis of tunnel design and construction. However, conventional classification methods do not allow dynamic tunnel construction adjustments because they are time‐consuming and do not consider the randomness of rock mass. This paper presents a new reliability rock mass classification method based on a least squares support vector machine (LSSVM) optimized by a bacterial foraging optimization algorithm (BFOA). The LSSVM is adopted to express the implicit relationship between classification indicators and rock mass grades, which is a response surface function for reliability evaluation. LSSVM parameters were optimized by the BFOA to form a hybrid BFOA‐LSSVM algorithm. Using geological prediction and rock strength resilience results as classification indicators, samples were developed to train the LSSVM model using the hybrid algorithm. The Monte Carlo sampling method of reliability classification was implemented and applied to the Suqiao tunnel at the Puyan highway in the Fujian province of China; the influence of parameters on the performance of the algorithm is discussed. The results indicate that the new method is feasible for tunnel engineering; it can improve the classification accuracy of surrounding rock exhibiting randomness, to provide an effective means of classifying surrounding rock in the dynamic design of tunnel construction.
Highlights
Classification of surrounding rock is the basis of tunnel design and construction; a complete classification system should include two parts: a preconstruction survey classification and a modification classification during construction [1]
Samples were produced for least squares support vector machine (LSSVM) machine learning, including geological prediction and rock strength resilience results as classification indicators. e Monte Carlo sampling method was used for calculation
Rock masses surrounding the tunnel have certain variability, such as a local broken zone or a weak layer, which leads to a large variation in classification indicators and misjudgment of the rock mass grade. ere is a reliability issue in the rock mass classification process; the probability of success or failure of the final classification result is regarded as a two-category classification problem. e probability expression of the classification result, the rock mass classification reliability [35], is expressed by reliability theory to avoid prediction error caused by the randomness of the classification indicators
Summary
Classification of surrounding rock is the basis of tunnel design and construction; a complete classification system should include two parts: a preconstruction survey classification and a modification classification during construction [1]. Geological advanced prediction information is considered to reflect the properties of rock masses, and prediction results are used as classification indicators for the evaluation of rock mass grade [22,23,24]. Ese studies analyzed the advanced geological prediction characteristics and established a nonlinear mapping relationship between indicators and rock grade, partly solving the problem of acquiring indicators in the rock mass classification process. Research on surrounding rock classification has produced significant results, but there are still some problems to be solved: (1) Conventional classification uses traditional methods to obtain the classification indicators, which is time-consuming and slows down construction progress. Samples were produced for LSSVM machine learning, including geological prediction and rock strength resilience results as classification indicators. The new reliability classification method was applied to the Suqiao tunnel of the Puyan expressway in Fujian province, China
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