A VaFALCON neuro-fuzzy system for mining of incomplete construction databases

This paper tackles problems encountered in mining of incomplete data for knowledge discovery of construction databases. As historical construction data are expensive and time-consuming to collect, any waste of incomplete data means not only loss of knowledge but also increase of costs for knowledge discovery of construction engineering. Unfortunately, incompleteness is omnipresent in the existing construction databases. This paper proposes a VaFALCON (Variable-Attribute Fuzzy Adaptive Logic Control Network) neuro-fuzzy system that is based on the architecture of the original FALCON and equipped with capabilities for mining incomplete data. Three real world examples are selected to test the proposed VaFALCON. The testing results show that the proposed VaFALCON is able to improve the system accuracy up to 84.5% and recover accuracy at least 81% even under the severe data incompleteness case, where all datasets of the database are incomplete.