Open Access

ARTICLE

Estimating Construction Material Indices with ARIMA and Optimized NARNETs

by Ümit Işıkdağ¹, Aycan Hepsağ², Süreyya İmre Bıyıklı³, Derya Öz⁴, Gebrail Bekdaş⁵, Zong Woo Geem^6,*

1 Department of Informatics, Mimar Sinan Fine Arts University, Istanbul, Turkey
2 Econometrics Department, Istanbul University, Istanbul, Turkey
3 MIS Department, Istanbul Gelisim University, Istanbul, Turkey
4 Computing HND Prog., Nisantasi University, Istanbul, Turkey
5 Department of Civil Engineering, İstanbul University-Cerrahpaşa, İstanbul, Turkey
6 College of IT Convergence, Gachon University, Seongnam, 13120, Korea

* Corresponding Author: Zong Woo Geem. Email:

Computers, Materials & Continua 2023, 74(1), 113-129. https://doi.org/10.32604/cmc.2023.032502

Received 20 May 2022; Accepted 22 June 2022; Issue published 22 September 2022

Abstract

Construction Industry operates relying on various key economic indicators. One of these indicators is material prices. On the other hand, cost is a key concern in all operations of the construction industry. In the uncertain conditions, reliable cost forecasts become an important source of information. Material cost is one of the key components of the overall cost of construction. In addition, cost overrun is a common problem in the construction industry, where nine out of ten construction projects face cost overrun. In order to carry out a successful cost management strategy and prevent cost overruns, it is very important to find reliable methods for the estimation of construction material prices. Material prices have a time dependent nature. In order to increase the foreseeability of the costs of construction materials, this study focuses on estimation of construction material indices through time series analysis. Two different types of analysis are implemented for estimation of the future values of construction material indices. The first method implemented was Autoregressive Integrated Moving Average (ARIMA), which is known to be successful in estimation of time series having a linear nature. The second method implemented was Non-Linear Autoregressive Neural Network (NARNET) which is known to be successful in modeling and estimating of series with non-linear components. The results have shown that depending on the nature of the series, both these methods can successfully and accurately estimate the future values of the indices. In addition, we found out that Optimal NARNET architectures which provide better accuracy in estimation of the series can be identified/discovered as result of grid search on NARNET hyperparameters.

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