# Tunnel Geology Prediction Using a Neural Network Based on Instrumented Drilling Test
###### Abstract
[20] compared various machine learning algorithms to classify rock types based on drilling data, obtaining a classification accuracy of 91% at best. [PERSON] et al. [21] classified rock types using machine learning and gradient boosted decision trees based on MWD data. [PERSON] et al. [22] used MWD data and a machine learning algorithm model to detect anomalies in directional drilling. [PERSON] et al. [23; 24] classified rock types based on Gaussian processes and the unsupervised clustering of MWD data. They also illustrated a method for using unsupervised learning together with the specific energy of rock drilling to optimize the penetration rate. Many researchers have concluded that strong relationships exist between drilling test data and rock properties. Due to the uncertainties in the drilling process, such as variations in drilling devices, bit conditions, and operator habits, drilling test data usually exhibit randomness. Currently available drilling mechanics and drilling energy analysis methods show difficulty in obtaining a clear quantitative correspondence between drilling parameters and rock properties. Machine learning neural networks constitute an appropriate method of classification for systems with complex relationships among many variables. This technique can rapidly acquire the nonlinear mapping relationships between drilling test data and rock properties. Only a few researchers have applied machine learning to drilling tests for tunnel geology prediction in civil engineering. Most such research has used machine learning methods in the mining and petroleum industries, focusing on only one or two types of rock prediction. Geology prediction in tunnel drilling is more complicated than in the mining and petroleum industries, which must acquire more accurate and detailed rock information. Given that most machine learning studies have focused on elaborate laboratory drilling tests, the question of these methods' validity when applied to drilling data obtained from practical civil engineering warrants further study. Moreover, current instrumented drilling test applications of machine learning are based on basic machine learning model structures that lack an optimized learning model, notably for instrumented drilling tests in civil engineering. This paper presents a basic neural network and a deep neural network model using a genetic algorithm (GA) to predict geological conditions for tunneling. The deep neural network method contains two components: (1) a normalization layer of the drilling parameters and (2) a GA optimization of the hyperparameters. Batch normalization is used in the normalization layer to accelerate the training process, enable higher learning rates, and improve the generalization accuracy. Optimized hyperparameters can be obtained via the GA optimization approach. Here a case study of the Jiudingshan Tunnel on the Chuxiong-Dali Highway in Yunnan, China, is provided to demonstrate the application of the method in tunnel geology prediction. In the case study, the feasibility and effectiveness of the neural network model based on instrumented drilling test data are verified. The feature importance of each drilling parameter is also analyzed. This neural network method based on instrumented drilling test data can make tunnel geological condition prediction more efficient and economical.