To design the tunnel excavations, the most important parameters are the engineering properties of rock, e.g., Young’s modulus (E) and unconfined compressive strength (UCS). Numerous researchers have attempted to propose methods to estimate E and UCS indirectly. This task is complex due to the difficulty of preparing and carrying out such experiments in a laboratory. The main aim of the present study is to propose a new and efficient machine learning model to predict E and UCS. The proposed model combines the extreme gradient boosting machine (XGBoost) with the firefly algorithm (FA), called the XGBoost-FA model. To verify the feasibility of the XGBoost-FA model, a support vector machine (SVM), classical XGBoost, and radial basis function neural network (RBFN) were also employed. Forty-five granite sample sets, collected from the Pahang-Selangor tunnel, Malaysia, were used in the modeling. Several statistical functions, such as coefficient of determination ( R 2 ), mean absolute percentage error (MAPE) and root mean square error (RMSE) were calculated to check the acceptability of the methods mentioned above. A review of the results of the proposed models revealed that the XGBoost-FA was more feasible than the others in predicting both E and UCS and could generalize.

中文翻译：

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一种基于 XGBoost-firefly 算法的新型系统进化方法预测岩石杨氏模量和无侧限抗压强度

为了设计隧道开挖，最重要的参数是岩石的工程特性，例如杨氏模量 (E) 和无侧限抗压强度 (UCS)。许多研究人员试图提出间接估计 E 和 UCS 的方法。由于在实验室中准备和进行此类实验很困难，因此这项任务很复杂。本研究的主要目的是提出一种新的高效机器学习模型来预测 E 和 UCS。所提出的模型将极限梯度提升机（XGBoost）与萤火虫算法（FA）相结合，称为 XGBoost-FA 模型。为了验证 XGBoost-FA 模型的可行性，还采用了支持向量机 (SVM)、经典 XGBoost 和径向基函数神经网络 (RBFN)。四十五个花岗岩样品组，从马来西亚彭亨 - 雪兰莪隧道收集的数据用于建模。计算了若干统计函数，例如决定系数（R 2 ）、平均绝对百分比误差（MAPE）和均方根误差（RMSE），以检查上述方法的可接受性。对所提出模型的结果的审查表明，XGBoost-FA 在预测 E 和 UCS 方面比其他模型更可行，并且可以推广。