ICTACT Journal on Soft Computing

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Construction Cost Prediction Using Neural Networks


Affiliations
1 Department of Computer Science and Engineering, Rajarambapu Institute of Technology, India
2 Department of Information Technology, Rajarambapu Institute of Technology, India
     

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Construction cost prediction is important for construction firms to compete and grow in the industry. Accurate construction cost prediction in the early stage of project is important for project feasibility studies and successful completion. There are many factors that affect the cost prediction. This paper presents construction cost prediction as multiple regression model with cost of six materials as independent variables. The objective of this paper is to develop neural networks and multilayer perceptron based model for construction cost prediction. Different models of NN and MLP are developed with varying hidden layer size and hidden nodes. Four artificial neural network models and twelve multilayer perceptron models are compared. MLP and NN give better results than statistical regression method. As compared to NN, MLP works better on training dataset but fails on testing dataset. Five activation functions are tested to identify suitable function for the problem. ‘elu' transfer function gives better results than other transfer function.

Keywords

Construction Cost Prediction, Neural Network, Multilayer Perceptron.
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  • Construction Cost Prediction Using Neural Networks

Abstract Views: 363  |  PDF Views: 3

Authors

Smita K. Magdum
Department of Computer Science and Engineering, Rajarambapu Institute of Technology, India
Amol C. Adamuthe
Department of Information Technology, Rajarambapu Institute of Technology, India

Abstract


Construction cost prediction is important for construction firms to compete and grow in the industry. Accurate construction cost prediction in the early stage of project is important for project feasibility studies and successful completion. There are many factors that affect the cost prediction. This paper presents construction cost prediction as multiple regression model with cost of six materials as independent variables. The objective of this paper is to develop neural networks and multilayer perceptron based model for construction cost prediction. Different models of NN and MLP are developed with varying hidden layer size and hidden nodes. Four artificial neural network models and twelve multilayer perceptron models are compared. MLP and NN give better results than statistical regression method. As compared to NN, MLP works better on training dataset but fails on testing dataset. Five activation functions are tested to identify suitable function for the problem. ‘elu' transfer function gives better results than other transfer function.

Keywords


Construction Cost Prediction, Neural Network, Multilayer Perceptron.

References