Learning method for knowledge retention in CBR cost models

Abstract

The case-based reasoning methodology fundamentally relies on historical cases to solve new problems. Supplementing insufficient data by the reproduction of appropriate values can mitigate the potential negative effects on the solutions resulting from sudden changes. However, CBR researchers have rarely examined this issue. To address this challenge, this research proposes a learning method for knowledge retention based on CBR by applying a data-mining approach to manage missing dataset values. A case study on a 164-apartment project was conducted to compare the estimation accuracy of the suggested learning method to that of past research with the same experiment conditions. The learning method with the CBR model achieved higher accuracy of the overall cost estimation and higher stability compared with the previous model. This research shows how cases can be generated and retained as learned cases to overcome the difficulties of continuous updates in a wide range of construction projects, as well as why the case bases need to be continuously updated. The research outcomes could support work related to cost estimation for decision makers ranging from beginners to experts in both academia and industry.