Abstract In this study, a new technique for predicting roadways stability in tunneling and underground space was proposed based on a combination of particle swarm optimization (PSO) algorithm and artificial neural network (ANN), called ANN-PSO model. The dataset from five tunneling and underground mines in the 2006-2019 period was recorded monthly and used for this aim with 145 observations. Accordingly, the stability of roadways in tunneling and underground space was evaluated based on the geomechanical parameters. The uniaxial compressive strength, internal friction angle, rock mass rating, tensile strength, cohesion, density, Young's modulus, shear strength, and slake durability were used as the influence parameters for evaluating and predicting roadway stability. Five other intelligent methods were also developed and compared with the proposed ANN-PSO model in order to have a comprehensive assessment, including support vector machine (SVM), hybrid neural fuzzy inference system (HYFIS), multiple linear regression (MLR), classification and regression tree (CART), and conditional inference tree (CIT). Three model assessment indices, such as MAE, RMSE, and R2 were used to simulate the accuracy of the roadway stability predictive models. Besides, ranking and color intensity techniques were also applied for further assessment. The results showed that the stability of the roadway could be accurately assessed by the proposed ANN-PSO model with an RMSE of 9.708, R2 of 0.972, and MAE of 7.161. They also revealed that the proposed ANN-PSO model yielded the most outperformed over the other models. The sensitivity analysis resulting also indicated that the uniaxial compressive strength, shear strength, quench durability index, density, and rock mass rating were the most important parameters for predicting roadway stability. They should be used in predicting the stability of roadways in tunneling and underground space.

中文翻译：

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使用基于人工神经网络的粒子群优化评估和预测隧道和地下空间道路的稳定性

摘要 在这项研究中，基于粒子群优化（PSO）算法和人工神经网络（ANN）的组合，提出了一种预测隧道和地下空间巷道稳定性的新技术，称为ANN-PSO模型。2006 年至 2019 年期间五个隧道和地下矿山的数据集每月记录一次，并用于此目的，并进行了 145 次观测。因此，基于地质力学参数评估隧道和地下空间巷道的稳定性。单轴抗压强度、内摩擦角、岩体额定值、抗拉强度、内聚力、密度、杨氏模量、剪切强度和耐老化性被用作评价和预测巷道稳定性的影响参数。还开发了其他五种智能方法并与所提出的 ANN-PSO 模型进行了比较，以进行综合评估，包括支持向量机 (SVM)、混合神经模糊推理系统 (HYFIS)、多元线性回归 (MLR)、分类和回归树 (CART) 和条件推理树 (CIT)。采用MAE、RMSE、R2三个模型评价指标模拟道路稳定性预测模型的准确性。此外，排名和颜色强度技术也被用于进一步评估。结果表明，提出的 ANN-PSO 模型可以准确评估道路的稳定性，RMSE 为 9.708，R2 为 0.972，MAE 为 7.161。他们还透露，所提出的 ANN-PSO 模型的表现优于其他模型。敏感性分析结果还表明，单轴抗压强度、剪切强度、淬火耐久性指数、密度和岩体等级是预测巷道稳定性的最重要参数。它们应该用于预测隧道和地下空间中巷道的稳定性。