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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  • M.Y. Cheng, H.C. Tsai and E. Sudjono, “Conceptual Cost Estimates using Evolutionary Fuzzy Hybrid Neural Network for Projects in Construction Industry”, International Journal on Expert Systems with Application, Vol. 37, No. 6, pp. 4224-4231, 2010.

  • R. Yadav, M. Vyas, V. Vyas and S. Agrawal, “Cost Estimation Model for Residential Building using Artificial Neural Network”, International Journal of Engineering Research and Technology, Vol. 5, No. 2, pp. 312-314, 2016.

  • Huawang Shi and Wanqing Li, “The Integrated Methodology of Rough Set Theory and Artificial Neural-Network for Construction Project Cost Prediction”, Proceedings of 2nd International Symposium on Intelligent Information Technology Application, pp. 16-21, 2008.

  • V.T. Luu and S.Y. Kim, “Neural Network Model for Construction Cost Prediction of Apartment Projects in Vietnam”, Korean Journal of Construction Engineering and Management, Vol. 10, No. 3, pp. 139-147, 2009.

  • G.H. Kim, S.H. An and K.I. Kang, “Comparison of Construction Cost Estimating Models based on Regression Analysis, Neural Networks, and Case-based Reasoning”, Journal on Building and Environment, Vol. 39, No. 10, pp. 1235-1242, 2004.

  • G.H. Kim, J.M. Shin, S. Kim and Y. Shin, “Comparison of School Building Construction Costs Estimation Methods Using Regression Analysis, Neural Network, and Support Vector Machine”, Journal of Building Construction and Planning Research, Vol. 7, No. 1, pp. 1-7, 2013.

  • H.M. Gunaydın and S.Z. Dogan, “A Neural Network Approach for Early Cost Estimation of Structural Systems of Buildings”, International Journal of Project Management, Vol. 8, No. 22, pp. 595-602, 2004.

  • M. Sana, I. Arazi, M. Faris Khamidi and Z. Saiful Bin, “Development of Construction Labor Productivity Estimation Model using Artificial Neural Network”, Proceedings of IEEE National Postgraduate Conference, pp. 1-5, 2011.

  • I. Mahamid and A. Bruland, “Preliminary Cost Estimating models for Road Construction Activities”, Proceedings of International Conference on Critical Infrastructure Development, pp. 1-13, 2010.

  • X. Qiu, L. Zhang, Y. Ren, P.N. Suganthan and G. Amaratunga, “Ensemble Deep Learning for Regression and Time Series Forecasting”, Proceedings of IEEE Symposium on Computational Intelligence in Ensemble Learning, pp. 1-7, 2014.

  • L. Deng and D. Yu, “Deep Learning: Methods and Applications”, Foundations and Trends in Signal Processing, Vol. 7, No. 3-4, pp. 197-387, 2014.

  • P.G. Madhavan, “Recurrent Neural Network for Time Series Prediction”, Proceedings of IEEE 15th annual International Conference of Engineering in Medicine and Biology Society, pp. 250-259, 1993.

  • A. Krizhevsky, I. Sutskever and G.E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks”, Proceedings of International Conference on Advances in Neural Information Processing Systems, pp. 1-9, 2012.

  • M. Dalto, J. Matusko and M. Vasak “Deep Neural Networks for Time Series Prediction with Applications in Ultra-Short-Term Wind Forecasting”, Proceedings of IEEE International Conference on Industrial Technology, pp. 1-10, 2015.

  • S.A. Mojarad, S.S. Dlay, W.L.Woo and G.V. Sherbet, “Breast Cancer Prediction and Cross Validation using Multilayer Perceptron Neural Networks”, Proceedings of 7th International Symposium on Communication Systems Networks and Digital Signal Processing, pp. 1-5, 2010.

  • J.S. Sonawane and D.R. Patil, “Prediction of Heart Disease using Multilayer Perceptron Neural Network”, Proceedings of IEEE International Conference on Information Communication and Embedded Systems, pp. 1-6, 2014.

  • M. Durairaj and V. Revathi, “Prediction of Heart Disease using Back Propagation MLP Algorithm”, International Journal of Scientific and Technology Research, Vol. 4, No. 8, pp. 235-239, 2015.

  • D.A. Clevert, T. Unterthiner and S. Hochreiter, “Fast and Accurate Deep Network”, Proceedings of International Conference on Learning Representations, pp. 1-14, 2016.

  • John A. Bullinaria, “Learning in Multi-Layer Perceptrons-Back-Propagation”, Available at: http://www.cs.bham.ac.uk/~jxb/NN/l7.pdf.

  • Analytics Vidhya, “Types of Regression”, Available at: https://www.analyticsvidhya.com/blog/2015/08/comprehensiveguide-regression/. Accessed on 2017.

  • Hegazy T, P. Fazio and O. Moselhi, “Developing Practical Neural Network Applications using Back-Propagation”, Computer Aided Civil and Infrastructure Engineering, Vol. 9, No. 2, pp. 145-159, 1994.

  • P. Gaurang, G. Amit, Y.P. Kosta and P. Devyani, “Behaviour Analysis of Multilayer Perceptrons with Multiple Hidden Neurons and Hidden Layers”, International Journal of Computer Theory and Engineering, Vol. 3, No. 2, pp. 332-337, 2011.


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  • Construction Cost Prediction Using Neural Networks

Abstract Views: 279  |  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