Intelligent Classification of Surrounding Rock of Tunnel Based on 10 Machine Learning Algorithms

Abstract

The quality evaluation of the surrounding rock is the cornerstone of tunnel design and construction. Previous studies have confirmed the existence of a relationship between drilling parameters and the quality of surrounding rock. The application of drilling parameters to the intelligent classification of surrounding rock has the natural advantages of automatic information collection, real-time analysis, and no extra work. In this work, we attempt to establish the intelligent surrounding rock classification model and software system driven by drilling parameters. We collected 912 samples containing four drilling parameters (penetration velocity, hammer pressure, rotation pressure, and feed pressure) and three surrounding rock (grade-III, grade-IV, and grade-V). Based on the python machine learning toolkit (Scikit-learn), 10 types of supervised machine learning algorithms were used to train the intelligent surrounding rock classification model with the model parameter selection technology of grid search cross validation. The results show that the average accuracy is 0.82, which proves the feasibility of this method. Finally, the tunnel surrounding rock intelligent classification system was established based on three models with better comprehensive performance among them. The classification accuracy of the system was 0.87 in the tunnel test section, which indicates that the system has good generalization performance and practical value.

The Construction and Application of a Deep Learning-Based Primary Support Deformation Prediction Model for Large Cross-Section Tunnels

“…During the large cross-section tunnel construction, challenges include poor geological conditions, support structure parameters, and effects on the surrounding rocks [3,4]. To overcome all of these difficulties, artificial intelligence (AI) technology has been widely adopted in the tunnel construction process, specifically in the area of AI for poor geological prediction [5], disaster risk evaluation [6], construction decision-making [7], and deformation prediction [8]. Under tunnel construction in complex construction conditions, the values and the times of the structure deformation can directly reflect the stability of the surrounding rock and support structure.…”

Research on a Multi-Objective Optimization Method for Transient Flow Oscillation in Multi-Stage Pressurized Pump Stations

“…With the rapid development of emerging technologies, machine learning algorithms have been widely applied in engineering, achieving significant results in water science [16][17][18][19][20][21]. Data-driven machine learning continually drives fluid mechanics from linear models and methods to nonlinear domains [22].…”

Prediction of jumbo drill penetration rate in underground mines using various machine learning approaches and traditional models

“…Some of the research findings include empirical models based on the uniaxial compressive strength of rocks, the impact of geological features on penetration rate, and the use of Artificial intelligence techniques to predict rock classification around tunnels. The studies also cover topics related to percussive and rotary drilling, the influence of different parameters on penetration rate, and the development of new models for predicting drilling performance [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51] .…”

“…The application of machine learning techniques in underground mines extends beyond the study of ROP (Rate of Penetration), and nowadays, it is also utilized in other fields of engineering. With the widespread use of jumbodrills equipped with Measurement While Drilling (MWD) systems, it has become customary to employ machine learning and deep learning techniques for classifying the rock mass in tunnels, Predictive modeling of drilling rate, as well as detecting deviations in bore holes within the face 51 . In addition to the aforementioned applications, given the paramount importance of safety in underground mines, the utilization of these techniques in various areas such as fire prediction, column stability, androckburst in underground engineering structures is rapidly expanding [59][60][61] .…”