| Title |
Comparing Accuracy of Prediction Cost Estimation Using Case-Based Reasoning and Neural Networks |
| Authors |
Kim Gwang-Hee ; Kim Sang-Yong ; Kang Kyung-In |
| Keywords |
Cost Estimation ; Case-Based Reasoning ; Artificial Neural Networks ; Regression Analysis |
| Abstract |
Prediction of the cost estimation of apartment houses is an important task in the management of construction projects. This study predicts the cost of apartment houses using case-based reasoning (CBR) and artificial neural networks(ANN) techniques. CBR has been recently favored because it seems to resemble more closely the psychological process human follow when trying to apply their knowledge to the solution of problems. ANN has proved themselves to be very useful in various modeling applications, because it can represent complex mapping functions and discover the representations using powerful learning algorithm. This study is conducted by using the same 540 cases which are obtained in Korea. 30 cases among the data are used for testing. Testing error rates of 3.68% in the CBR and 6.66% in the ANN were obtained. Results showed that CBR can produce slightly more accurate results and achieve higher computational efficiency than ANN. If the use of CBR and ANN is understood better, as a result, cost estimation can be predicted with reasonability and reliability, all parties involved in the construction process could save considerable money. |