PREDESIGN COST ESTIMATION OF URBAN RAILWAY PROJECTS WITH PARAMETRIC MODELING

Rifat Sönmez · Bahadir Ontepeli

47 citations0/0 files storedJournal of Civil Engineering and Management

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Construction Project Management and Performance Railway Engineering and Dynamics Capital Investment and Risk Analysis

Abstract

This paper presents a parametric modeling method for predesign cost estimation of urban railway systems. Data of 13 light rail and metro projects located in Turkey were compiled for quantification of the impacts of parameters on the project costs. Parametric models have been developed using regression analysis and neural networks techniques. Ten linear regression models were used for determination of the parameters significantly impacting cost of urban railway projects. Two neural networks were considered as an alternative to regression models, particularly for the identification of the non‐linear relations. Predictive behaviour and performance of the models were compared to determine a model that presents adequate relations and has a reasonable accuracy. The proposed method provides a powerful approach for determination of a satisfactory parametric cost model during early project stages by incorporating a coordinated use of regression analysis and neural network techniques. Santrauka Pateiktas miesto geležinkelių sistemų priešprojektinių išlaidų skaičiavimo metodas, pagrįstas parametriniu modeliavimu. Parametrų įtaka projekto išlaidoms nustatyta išnagrinėjus 13 nedidelių geležinkelių ir metro projektų Turkijoje. Parametriniai modeliai sudaryti taikant regresinę analizę bei dirbtinio intelekto tinklus. Parametrų įtakos miestų geležinkelių projektavimo išlaidoms reikšmingumui nustatyti sudaryta 10 tiesinės regresijos modelių. Kaip alternatyva regresijos modeliams sudaryti 2 neuroniniai tinklai, ypač nagrinėjant netiesines priklausomybes. Pasiūlytais modeliais gauti rezultatai palyginti tarpusavyje, siekiant nustatyti adekvačias priklausomybes ir užtikrinti reikiamą tikslumą. Pasiūlytas metodas leidžia sukurti parametrinį projekto išlaidų modelį ankstyvojoje priešprojektinėje stadijoje. Tai pasiekta suderintai panaudojus regresinę analizę ir dirbtinio intelekto tinklus.

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References

1998Neural Network Model for Parametric Cost Estimation of Highway Projects 2004A neural network approach for early cost estimation of structural systems of buildings 2004Conceptual cost estimation of building projects with regression analysis and neural networks 2008Parametric Range Estimating of Building Costs Using Regression Models and Bootstrap 1974Predesign Cost-Estimation Function for Buildings

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