文章基本信息

标题：Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks
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作者：Vinay Chandwani ; Vinay Agrawal ; Ravindra Nagar 等
期刊名称：Advances in Artificial Neural Systems
印刷版ISSN：1687-7594
电子版ISSN：1687-7608
出版年度：2014
卷号：2014
DOI：10.1155/2014/629137
出版社：Hindawi Publishing Corporation
摘要：Artificial neural networks (ANNs) have been the preferred choice for modeling the complex and nonlinear material behavior where conventional mathematical approaches do not yield the desired accuracy and predictability. Despite their popularity as a universal function approximator and wide range of applications, no specific rules for deciding the architecture of neural networks catering to a specific modeling task have been formulated. The research paper presents a methodology for automated design of neural network architecture, replacing the conventional trial and error technique of finding the optimal neural network. The genetic algorithms (GA) stochastic search has been harnessed for evolving the optimum number of hidden layer neurons, transfer function, learning rate, and momentum coefficient for backpropagation ANN. The methodology has been applied for modeling slump of ready mix concrete based on its design mix constituents, namely, cement, fly ash, sand, coarse aggregates, admixture, and water-binder ratio. Six different statistical performance measures have been used for evaluating the performance of the trained neural networks. The study showed that, in comparison to conventional trial and error technique of deciding the neural network architecture and training parameters, the neural network architecture evolved through GA was of reduced complexity and provided better prediction performance.