In the current paper, the uniaxial compressive strength (UCS) and Young modulus (E) of rocks were predicted using a hybridized intelligence method. The model was developed using an optimum multi-objective generalized feedforward neural network (GFFN) incorporated with an imperialist competitive metaheuristic algorithm (ICA) and managed using 208 datasets of different physical and mechanical quarries from almost all over of Iran. Rock class, density, porosity, P-wave velocity, point load index and water absorption were datacenter components. The predictability and accuracy performance of the hybrid ICA-GFFN model were discussed using different error criteria and confusion matrixes. The observed 5.4% and at least 32% improvement in hybrid ICA-GFFN than GFFN and multivariate regression (MVR) demonstrated feasible and accurate enough tools that can effectively be applied for multi-objective prediction purposes. The influence of inputs on predicted outputs was also identified using two different sensitivity analyses.

本文采用混合智能方法对岩石的单轴抗压强度（UCS）和杨氏模量（E）进行了预测。该模型是使用结合帝国主义竞争元启发式算法（ICA）的最优多目标广义前馈神经网络（GFFN）开发的，并使用了来自伊朗几乎整个地区的208种不同物理和机械采石场的数据集进行了管理。岩石类别，密度，孔隙率，P波速度，点荷载指数和吸水率是数据中心的组成部分。混合ICA - GFFN的可预测性和准确性。使用不同的误差标准和混淆矩阵讨论了模型。观察到的混合ICA - GFFN比GFFN和多元回归（MVR）分别提高了5.4％和至少32％，这证明了可行且准确的工具可以有效地用于多目标预测。还使用两种不同的敏感性分析来确定输入对预测输出的影响。