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 squared () 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.

中文翻译：

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基于遗传算法和人工神经网络的冻融循环岩石三轴抗压强度智能预测模型

岩石抗压强度是寒冷地区岩体工程设计、开挖和稳定性分析的重要力学参数。准确快速地预测岩石抗压强度对于指导寒冷地区岩体工程的高效施工具有重要的工程价值。在这项研究中，使用遗传算法 (GA) 和人工神经网络 (ANN) 提出了对经受冻融循环的砂岩的三轴抗压强度 (TCS) 的预测。为此，建立了一个数据库，包括四个模型输入，即纵波速度、孔隙度、围压和冻融循环次数，以及一个输出，岩石的 TCS。ANN 的结构、初始连接权重和偏差基于 GA 逐步优化。在得到最优的 GA-ANN 模型后，将 GA-ANN 模型的性能与简单 ANN 模型的性能进行比较。结果表明，所提出的混合 GA-ANN 模型在预测测试数据集方面比简单的 ANN 模型具有更高的准确度：均方根误差 (RMSE)、平均绝对误差 (MAE) 和混合 GA-ANN 模型的平方 ( )分别等于 1.083、0.893 和 0.993，而简单 ANN 模型的相应值分别为 2.676、2.153 和 0.952。