Comparison of Cost Estimation Methods using Hybrid Artificial Intelligence on Schematic Design Stage: RANFIS and CBR-GA

摘要

Cost estimating at schematic design stage as the basis of project evaluation, engineering design, and cost management, plays an important role in project decision under a limited definition of scope and constraints in available information and time, and the presence of uncertainties. The purpose of this study is to compare the performance of cost estimation models of two different hybrid artificial intelligence approaches: regression analysis-adaptive neuro fuzzy inference system (RANFIS) and case based reasoning-genetic algorithm (CBRGA) techniques. The models were developed based on the same 50 low-cost apartment project datasets in Indonesia. Tested on another five testing data, the models were proven to perform very well in term of accuracy. A CBR-GA model was found to be the best performer but suffered from disadvantage of needing 15 cost drivers if compared to only 4 cost drivers required by RANFIS for on-par performance.