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System Identification of a Soil Tunnel Based on a Hybrid Artificial Neural Network–Numerical Model Approach
Iranian Journal of Science and Technology, Transactions of Civil Engineering ( IF 1.7 ) Pub Date : 2020-04-21 , DOI: 10.1007/s40996-020-00405-w
Marzieh Jafari

System identification of structures is the primary goal of this study. Numerical simulation using methods such as finite element modelling or finite difference modelling is the practical solution to model the structure based on some accurate parameter values that are essential to identify the behaviour law of the structures. An optimisation method integrated with the numerical model (NM) can solve an inverse problem to provide a calibrated parameter set to improve the modelling. For this purpose, an application of artificial neural networks (ANNs) integrated with an NM with the title “hybrid ANN–NM” approach is proposed in this study so that the parameters and resulted deformations of a developed NM of the structure would enter to a designed ANN for training the network. The developed hybrid ANN–NM method to identify the system of a tunnel excavated in the soil is applied and reported in this paper. A set of variable material parameters with a set of displacement and strain data (in $$x$$ x and $$y$$ y directions) of critical points of the tunnel which obtained from 2D FDM in FLAC 7.00 software is provided to train the ANN. This study is divided into two methods: (1) fitting model to the displacements and strains to show the deformation behaviour of the tunnel related to the parameter changing, and (2) fitting model to the parameters related to the displacements and strains to estimate the optimum parameters for the NM. For the first method, the parameter dataset as input and the displacements and strains as the ANN’s output in one approach separately and in another approach altogether are considered to train the ANN. In the second method, the displacements and strains as input and the physical parameters of the NM as output are submitted to train the ANN. The MSE convergence of implemented ANN shows the power of ANN to model the behaviour of structure based on the token data. Also, the RMSE of residuals refers to the success of the proposed method for the fitting model. Besides, this method was able to provide the optimum parameters for the numerical model in minimum computing time regarding the implementation of the second method.

中文翻译:

基于混合人工神经网络-数值模型方法的土壤隧道系统识别

结构的系统识别是本研究的主要目标。使用有限元建模或有限差分建模等方法的数值模拟是基于一些准确的参数值对结构进行建模的实用解决方案,这些参数值对于识别结构的行为规律至关重要。与数值模型 (NM) 集成的优化方法可以解决逆问题以提供校准参数集以改进建模。为此,本研究提出了一种与 NM 集成的人工神经网络 (ANN) 的应用,名为“混合 ANN-NM”方法,以便结构的已开发 NM 的参数和结果变形将进入设计 ANN 来训练网络。本文应用并报告了开发的混合 ANN-NM 方法来识别在土壤中开挖的隧道系统。提供一组可变材料参数和一组隧道关键点的位移和应变数据(在 $$x$$ x 和 $$y$$ y 方向),这些数据是从 FLAC 7.00 软件中的 2D FDM 获得的,用于训练安。本研究分为两种方法:(1) 对位移和应变进行拟合模型,以显示与参数变化相关的隧道变形行为;(2) 对与位移和应变相关的参数进行拟合,以估计隧道的变形行为。 NM 的最佳参数。对于第一种方法,在一种方法中作为输入的参数数据集和作为 ANN 输出的位移和应变分别和在另一种方法中被认为是训练 ANN。在第二种方法中,位移和应变作为输入,NM 的物理参数作为输出提交来训练 ANN。已实现的 ANN 的 MSE 收敛显示了 ANN 基于令牌数据对结构行为进行建模的能力。此外,残差的均方根误差是指所提出的拟合模型方法的成功。此外,对于第二种方法的实现,该方法能够在最短的计算时间内为数值模型提供最佳参数。已实现的 ANN 的 MSE 收敛显示了 ANN 基于令牌数据对结构行为进行建模的能力。此外,残差的均方根误差是指所提出的拟合模型方法的成功。此外,对于第二种方法的实现,该方法能够在最短的计算时间内为数值模型提供最佳参数。已实现的 ANN 的 MSE 收敛显示了 ANN 基于令牌数据对结构行为进行建模的能力。此外,残差的均方根误差是指所提出的拟合模型方法的成功。此外,对于第二种方法的实现,该方法能够在最短的计算时间内为数值模型提供最佳参数。
更新日期:2020-04-21
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