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Estimating Construction Material Indices with ARIMA and Optimized NARNETs

Computers, Materials & Continua 2023年第1期74卷 113-129页

作者： Ümit Isıkdag Aycan Hepsag sureyya imre bıyıklı DeryaÖz Gebrail bekdas Zong Woo Geem Department of Informatics Mimar Sinan Fine Arts UniversityIstanbulTurkey Econometrics Department Istanbul UniversityIstanbulTurkey MIS Department Istanbul Gelisim UniversityIstanbulTurkey Computing HND Prog. Nisantasi UniversityIstanbulTurkey Department of Civil Engineering Istanbul University-CerrahpasaIstanbulTurkey College of IT Convergence Gachon UniversitySeongnam13120Korea

Construction Industry operates relying on various key economic *** of these indicators is material *** the other hand,cost is a key concern in all operations of the construction *** the uncertain conditions,reliable cost forecasts become an important source of *** cost is one of the key components of the overall cost of *** addition,cost overrun is a common problem in the construction industry,where nine out of ten construction projects face cost *** 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 have a time dependent *** order to increase the foreseeability of the costs of construction materials,this study focuses on estimation of construction material indices through time series *** different types of analysis are implemented for estimation of the future values of construction material *** first method implemented was Autoregressive Integrated Moving Average(ARIMA),which is known to be successful in estimation of time series having a linear *** second method implementedwas Non-LinearAutoregressive Neural Network(NARNET)which is known to be successful in modeling and estimating of series with non-linear *** results have shown that depending on the nature of the series,both these methods can successfully and accurately estimate the future values of the *** 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.

关键词： Construction material indices ARIMA non-linear autoregressive neural network NARNETs

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