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COMPARISON OF GENE EXPRESSION PROGRAMMING AND ARTIFICIAL NEURAL NETWORK TECHNIQUES FOR ESTIMATING BUILDING COST

Aim: Construction project, cost calculation based on bill of materials is generally accepted as a classical method in terms of giving definite result. However, because the calculation of each work item is required, there is a need for a long calculation time. In this study, is worked on the availability of practical, fast and realistic results using artificial intelligence techniques with a few determined variables without performing detailed quantification works in the public institutions budget planning or in the tenderer’s project cost estimate calculations; finally, the obtained results are compared. Method: For this purpose, it is aimed to estimate the project cost by using Gene Expression Programming (GEP) technique and Artificial Neural Network (ANN) techniques by using variables such as the number of floors, duration, building type and total construction area of 75 education and health building projects carried out between 2011 and 2016 (60 of which are training,15 are test data). Results: According to the test data obtained at the end of the study, the project cost determination coefficient (R2) was 0.970 with the Gene Expression Programming technique and 0.967 with the artificial neural network technique. Conclusion: The study shows that Gene Expression Programming and Artificial Neural Networks techniques can be used equally in building cost calculations, and both methods give acceptable values close to reality.