Quantity Estimating of Building with Logarithm-Neuron Networks

Abstract

Cost estimating is a computational process that attempts to predict the final cost of a future project even though not all of the parameters are known when the cost estimate is prepared. Artificial neural networks are a good tool to model nonlinear systems, but the learning speed of a network is often unacceptably slow and the generalization capability is often unsatisfactorily low in solving highly nonlinear function mapping problems. In this paper, a novel neural network architecture, the logarithm-neuron network (LNN), is proposed and examined for its efficiency and accuracy in quantity estimating of steel and RC buildings. The architecture of the LNN is the same as that of the standard back-propagation neural network (BPN), but logarithm neurons are added to the input layer and output layer of the network. The results indicate that the logarithm neurons in the network provide an enhanced network architecture to improve significantly the performance of these networks in quantity estimating for buildings.