Abstract
This study is aimed at improving a formula that enables easy, correct, and fast estimation of an Early-Stage Cost of Buildings (ESCE). This formula, enabling estimation of ESCE, was developed by the authors based on artificial neural networks and gene expression programming. A quantity survey was conducted for a hundred construction projects, and a data set was created. This data set was analysed with many Artificial Neural Networks to determine the variables that affect ESCE. An algorithm configuration was made with Gene Expression Programming, and the ESCE formula was created using this algorithm configuration. This formula estimates ESCE with satisfactory precision. The use of the proposed formula in the early-stage building cost calculations is important not only for faster and easier cost calculation but also to prevent any differences that may arise due to the individual making the calculations.
Highlights
On construction projects, investors wish to accurately estimate necessary financing so as to arrange the budgets of their investments, and to make sure that the project is profitable
A formula was proposed to estimate the EarlyStage Cost Estimation of Building Construction Projects (ESCE) in a rapid, easy and accurate manner by using the data selected according to the construction design and by employing the Artificial Neural Networks (ANN) and Gene Expression Programming (GEP) methods
A quantity survey study was conducted on one hundred construction projects tendered between 2011 and 2016 in relation to independent variables determined to have an impact on building costs, and a data set was created
Summary
Investors wish to accurately estimate necessary financing so as to arrange the budgets of their investments, and to make sure that the project is profitable In this process, cost calculation is an important step that should be taken into consideration by all parties, including project owners and contractors [1, 2]. An early-stage project cost calculation can vary from one individual to another For these reasons, it is obvious that there is a need for methods that would enable accurate estimation of ESCE, both in practice and theory. Kim et al [7] (12) conducted a study to measure performance of artificial neural networks, support vector techniques, and regression analysis methods in the early determination of construction costs.
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