Rock mass quality assessment has a vital influence on the excavation of tunnels and caverns in rock mass. For this purpose, extensive field studies, including records of measure-while-drilling data and rock mass quality scores (RQS) from the observation reports of tunnel faces, have been conducted. In order to predict RQS, three optimized artificial neural network (ANN) models based on genetic algorithm (GA), particle swarm optimization (PSO), and imperialist competition algorithm (ICA) were developed. Six parameters of measure-while-drilling (MWD) data and their corresponding RQS constituted 1270 datasets, which were set as input and output of ANN, respectively. The traditional multiple linear regression (MLR), multiple nonlinear regression (MNR) statistical model, and ANN model were developed as comparative models. Comparison results reveal that PSO-ANN and ICA-ANN models are capable of predicting RQS with higher reliability than the MLR, MNR, ANN, and GA-ANN models. Results indicate that PSO-ANN and ICA-ANN models can be used to predict RQS; however, the PSO-ANN model has better performance.

岩体质量评估对岩体中隧道和洞室的开挖具有至关重要的影响。为此，已经进行了广泛的现场研究，包括随钻测量数据记录和来自隧道工作面观测报告的岩体质量得分（RQS）记录。为了预测RQS，开发了基于遗传算法（GA），粒子群优化（PSO）和帝国主义竞争算法（ICA）的三种优化的人工神经网络（ANN）模型。随钻测量（MWD）数据的六个参数及其相应的RQS构成了1270个数据集，分别设置为ANN的输入和输出。开发了传统的多元线性回归（MLR），多元非线性回归（MNR）统计模型和ANN模型作为比较模型。比较结果表明，与MLR，MNR，ANN和GA-ANN模型相比，PSO-ANN和ICA-ANN模型能够以更高的可靠性预测RQS。结果表明，PSO-ANN和ICA-ANN模型可用于预测RQS。但是，PSO-ANN模型具有更好的性能。