Regularization neural network for construction cost estimation

Estimation of the cost of a construction project is an important task in the management of construction projects. The quality of construction management depends on accurate estimation of the construction cost. Highway construction costs are very noisy and the noise is the result of many unpredictable factors. In this paper, a regularization neural network is formulated and a neural network architecture is presented for estimation of the cost of construction projects. The model is applied to estimate the cost of reinforced-concrete pavements as an example. The new computational model is based on a solid mathematical foundation making the cost estimation consistently more reliable and predictable. Further, the result of estimation from the regularization neural network depends only on the training examples. It does not depend on the architecture of the neural network, the learning parameters, and the number of iterations required for training the system. Moreover, the problem of noise in the data is taken into account in a rational manner.