Prediction and estimation of civil construction cost using linear regression and neural network

Abstract

Adequate construction cost estimation is a main factor for any type of construction projects. Forecasting cost of construction projects can be considered as a difficult task. In order to forecast the cost of the civil construction projects, we have used the ordinary least square regression (OLSR) model and multilayer perceptron (MLP) in our proposed model. The performance of the proposed model is analysed on the data of the 12 years of schedule rates of construction projects in Pune region of India. The experiment shows 91% to 97% of accuracy in prediction using ordinary least square regression model. Similarly, we have conducted series of experiments on multilayer perceptron model with different activation functions. It was observed that the multilayer perceptron model with 'softplus' activation function can be able to predict the project cost of the civil constructions with accuracy of 91% to 98%. Thus, it shows that the prediction of cost using multilayer perceptron model gives higher accuracy than the ordinary least square regression model.