Parallel approach of a Galerkin-based methodology for predicting the compressive strength of the lightweight aggregate concrete

Abstract

A methodology based on the Galerkin formulation of the finite element method has been analyzed for predicting the compressive strength of the lightweight aggregate concrete using ultrasonic pulse velocity. Due to both the memory requirements and the computational cost of this technique, its parallelization becomes necessary for solving this problem. For this purpose a mixed MPI/OpenMP parallel algorithm has been designed and different approaches and data distributions analyzed. On the other hand, this Galerkin methodology has been compared with multiple linear regression models, regression trees and artificial neural networks. Based on different measures of goodness of fit, the effectiveness of the Galerkin methodology, compared with these statistical techniques for data mining, is shown.

Use of Artificial Neural Network in Design of Fly Ash Blended Cement Concrete Mixes

“…Weighting functions are provided at the intersection of nodes depending on the previous learning process. Hidden layers are layers which are intermediate between the input and the output layers [2][3][4][5]. Training of ANN is the first step in which it takes some given input data into consideration and relates it to its output data which also is given to it.…”

Numerical Non-Linear Modelling Algorithm Using Radial Kernels on Local Mesh Support

“…In real cases, the effects of non-linearity dominate the dynamics of the system and far from the equilibrium points linear relations as in (8) are not useful. In the cases when the relation is not linear but known:…”

“…In (8) and (9), determining the values of the parameters of the model can be done by minimizing an error function defined over the parameters. One common method is to consider the mean square error (MSE) of the estimated values on the experimental points.…”

“…One approach more on the line of the original FEM can be seen in [5] and applied by an example in [6,7]. Although different modifications of the initial algorithm have been developed, see [8] in order to improve the computing speeds for the regression model, the basic methodology can be resumed as follows.…”