Construction cost estimation of spherical storage tanks: artificial neural networks and hybrid regression—GA algorithms

Subject Areas : Mathematical Optimization

Vida Arabzadeh ¹ , S. T . A. Niaki ² , Vahid Arabzadeh ³

1 - Department of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 - Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
3 - HVAC Technology, Department of Mechanical Engineering, School of Engineering, Aalto University, 14400, 00076, Aalto, Finland

Received: 2020-10-07 Accepted : 2020-10-07 Published : 2018-12-01

Keywords: Cost estimation  Manufacturing project , Spherical storage tanks  Neural networks  Genetic, algorithm  Regression method,

Abstract :

One of the most important processes in the early stages of construction projects is to estimate the cost involved. This process involves a wide range of uncertainties, which make it a challenging task. Because of unknown issues, using the experience of the experts or looking for similar cases are the conventional methods to deal with cost estimation. The current study presents data-driven methods for cost estimation based on the application of artificial neural network (ANN) and regression models. The learning algorithms of the ANN are the Levenberg–Marquardt and the Bayesian regulated. Moreover, regression models are hybridized with a genetic algorithm to obtain better estimates of the coefficients. The methods are applied in a real case, where the input parameters of the models are assigned based on the key issues involved in a spherical tank construction. The results reveal that while a high correlation between the estimated cost and the real cost exists; both ANNs could perform better than the hybridized regression models. In addition, the ANN with the Levenberg–Marquardt learning algorithm (LMNN) obtains a better estimation than the ANN with the Bayesian-regulated learning algorithm (BRNN). The correlation between real data and estimated values is over 90%, while the mean square error is achieved around 0.4. The proposed LMNN model can be effective to reduce uncertainty and complexity in the early stages of the construction project.