Intelligent Prediction Model of the Triaxial Compressive Strength of Rock Subjected to Freeze-Thaw Cycles Based on a Genetic Algorithm and Artificial Neural Network

Abstract

Rock compressive strength is an important mechanical parameter for the design, excavation, and stability analysis of rock mass engineering in cold regions. Accurate and rapid prediction of rock compressive strength has great engineering value in guiding the efficient construction of rock mass engineering in a cold regions. In this study, the prediction of triaxial compressive strength (TCS) for sandstone subjected to freeze-thaw cycles was proposed using a genetic algorithm (GA) and an artificial neural network (ANN). For this purpose, a database including four model inputs, namely, the longitudinal wave velocity, porosity, confining pressure, and number of freeze-thaw cycles, and one output, the TCS of the rock, was established. The structure, initial connection weights, and biases of the ANN were optimized progressively based on GA. After obtaining the optimal GA-ANN model, the performance of the GA-ANN model was compared with that of a simple ANN model. The results revealed that the proposed hybrid GA-ANN model had a higher accuracy in predicting the testing datasets than the simple ANN model: the root mean square error (RMSE), mean absolute error (MAE), and R squared ( R 2 ) were equal to 1.083, 0.893, and 0.993, respectively, for the hybrid GA-ANN model, while the corresponding values were 2.676, 2.153, and 0.952 for the simple ANN model.

Sensitivity analysis of seismic attributes and oil reservoir predictions based on jointing wells and seismic data – A case study in the Taoerhe Sag, China

“…Chinese scholars took a central paddy sandstone reservoir in a deep-water area as an example and predicted the distribution characteristics of favorable reservoirs using the GA method, which greatly reduced the exploration risk (Huang, A. M., Li, L., 2011). GA can also be applied to calculate rock compressive strength (Xiong et al, 2021).…”

Study on Prediction Model of Mechanical Parameters of Rock Frozen-Thawed Damage based on NMR Technology

“…Deng et al [10] used the fractal dimension of macropores as independent variable to perform linear fitting of compressive strength, macropore volume, and fractal dimension of macropore diameter, respectively, deduced the specific expression of the strength model, and established a strength prediction model for sandstone-like materials. Xiong et al [11] used genetic algorithm (GA) and artificial neural network (ANN) method to establish a database of rock triaxial compressive strength, including four model inputs and one output of p-wave velocity, porosity, confining pressure, and frozen-thawed cycles. Based on genetic algorithm, the structure, initial connection weight, and deviation of neural network were gradually optimized.…”