Data management for construction processes using Fuzzy approach

Uncertainty is an entrenched characteristic of most construction projects. Most research works in simulating construction operations have focused predominantly on modeling and has neglected to study the effect of subjective variables on simulation process. Data mining is used to extract hidden knowledge from a data set, which would not be readily obtained by traditional methods. There is a significant need for a new generation of techniques and tools with the ability to automatically assist humans in analyzing the mountains of available construction data searching for useful knowledge. The presented research develops, using Fuzzy approach, a data mining engine to utilize, analyze, extract and model the hidden patterns of the project data sets to predict the work task durations. The engine depends on finding the relation between quantitative and qualitative variables, which affect the construction processes, and work task durations. It consists of five steps: (1) select the factors that affect the construction process; (2) build Fuzzy sets; (3) generate Fuzzy rules and models; (4) build Fuzzy knowledge base; and (5) validate the effectiveness of the built knowledge base to predict the work task durations. The developed engine is validated and verified using case study with sound and satisfactory results, 92 % average validity percent. The developed research/engine benefits both researchers and practitioners because it provides robust knowledge base for construction processes and a tool to predict the related task durations for construction activities.