Assessment of evolutionary algorithms in predicting non-deposition sediment transport

In this article, the densimetric Froude number of the flow is estimated using the parameters of volumetric sediment concentration (C_V), the relative depth of flow (d/R), dimensionless particle number (D_gr) and the overall sediment friction factor (λ_s). The particle swarm optimization (PSO) and imperialist competitive algorithms (ICA) were used to estimate the densimetric Froude number. To study the effects of sediment transport parameters on the densimetric Froude number, six different models are presented. The PSO algorithm with root mean square error (RMSE) = 0.014 and mean absolute percentage error (MAPE) = 5.1% present the results with a relatively good accuracy. The accuracy of the results presented for the selected model by the ICA algorithm is also in the form of RMSE = 0.007 and MAPE = 5.6%. Although both algorithms return good results in estimating the densimetric Froude number for the selected model, it should be mentioned that for all the six presented models ICA returns better results than PSO.     

微粒群优化算法在Theis公式参数识别中的应用

采用一种微粒群优化算法来识别承压完整井非稳定地下水运动Theis公式中的水文地质参数。微粒群算法是一种新型的群体智能算法,它将每个个体看作在多维搜索空间中的一个没有重量和体积的微粒,并在搜索空间中以一定的速度飞行,该飞行速度由个体的飞行经验和群体的飞行经验进行动态调整。然后根据个体适应值大小运算,根据适应度函数对微粒的速度和位置进行进化,最终得到足够好的适应度值。本文采用微粒群算法可根据抽水试验资料快速反演Theis公式近似解析解中的水文地质参数。实例计算结果表明该微粒群算法计算速度快,在水文地质逆问题求解中值得推广应用。     

An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass

Field penetration index (FPI) is one of the representative key parameters to examine the tunnel boring machine (TBM) performance. Lack of accurate FPI prediction can be responsible for numerous disastrous incidents associated with rock mechanics and engineering. This study aims to predict TBM performance (i.e. FPI) by an efficient and improved adaptive neuro-fuzzy inference system (ANFIS) model. This was done using an evolutionary algorithm, i.e. artificial bee colony (ABC) algorithm mixed with the ANFIS model. The role of ABC algorithm in this system is to find the optimum membership functions (MFs) of ANFIS model to achieve a higher degree of accuracy. The procedure and modeling were conducted on a tunnelling database comprising of more than 150 data samples where brittleness index (BI), fracture spacing, α angle between the plane of weakness and the TBM driven direction, and field single cutter load were assigned as model inputs to approximate FPI values. According to the results obtained by performance indices, the proposed ANFIS_ABC model was able to receive the highest accuracy level in predicting FPI values compared with ANFIS model. In terms of coefficient of determination (R²), the values of 0.951 and 0.901 were obtained for training and testing stages of the proposed ANFIS_ABC model, respectively, which confirm its power and capability in solving TBM performance problem. The proposed model can be used in the other areas of rock mechanics and underground space technologies with similar conditions.