Vojinovic, Z and Kecman, V (2001) Modelling empirical data to support project cost estimating: Neural networks versus traditional methods. Construction Innovation, 1(4), pp. 227-243. ISSN 1471-4175
Abstract
In this paper we are presenting our research findings on how effective neural networks are at forecasting and estimating preliminary project costs. We have shown that neural networks completely outperform traditional techniques in such tasks. In exploring nonlinear techniques almost all of the current research involves neural network techniques, especially multilayer perceptron (MLP) models and other statistical techniques and few authors have considered radial basis function neural network (RBF NN) models in their research. For this purpose we have developed RBF NN models to represent nonlinear static and dynamic processes and compared their performance with traditional methods. The traditional methods applied in this paper are multiple linear regression (MLR) and autoregressive moving average models with eXogenous input (ARMAX). The performance of these and RBF neural network and traditional models is tested on common data sets and their results are presented.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | cost estimating; neural networks; traditional methods |
| Index terms: | forecasting, modelling, estimating, cost estimating, neural network, multilayer, project cost |
| Subjects: | prediction and forecasting, financial and cost management, economics, artificial intelligence, specialized materials and systems, analytical methods |
| Topics: | Cost Management, Engineering Principles, Construction Materials, Research Practice, Digital Applications |
| Descriptive scope: | 3 PCA |
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