Deep convolutional neural network for fast determination of the rock strength parameters using drilling data

Accurate, fast, and reliable estimation of field rock strength is significant for the assessment of rock mass strength. This paper presents a novel method to continuously estimate the field strength parameters of rock by incorporating a deep convolutional neural network (DCNN) technique into the drilling process. A DCNN model is established based on the stochastic pooling method and softmax loss. The proposed CNN model framework includes: (1) collecting drilling performance parameters of intact rock, (2) establishing a strength parameter database via standard laboratory tests, (3) training the proposed CNN model using images created from the dataset of drilling performance parameters and rock strength parameters (unconfined compressive strength (UCS), cohesion, and internal friction angle), and (4) performing a new estimation to evaluate the reliability of the trained CNN model. The experimental results show that the predicted rock strength parameters are within the accepted error range of 10% compared to the results from the conventional standard test. The proposed CNN shows superior performance for UCS estimation of various rock types and achieves higher accuracy than the Mohr-Coulomb criterion. In particular, the proposed method can overcome major limitations of analytical models based on the force limit and energy equilibrium method. This method enables continuous and reliable field measurements of rock strength parameters at a speed several orders of magnitude faster than the standard test. This continuous and practical technique has been applied in an engineering problem in the Hanjiang-toWeihe River Project of China and shows potential for field applications in rock engineering.