Journal of Construction Engineering and Management · 2005 · 118 citations · 8 references
ArchitectureConstruction ManagementConstruction EngineeringConstruction ProductivityConstruction Project ManagementExpert ModelingExpert SystemsKnowledge-based Decision SupportProductivity AnalysisProductivity MeasurementFuzzy Expert SystemsFuzzy Inference SystemsFuzzy Rule-based SystemsKnowledge-based Expert SystemsLabor ProductivityLimited Data SetsMembership Functions
The study demonstrates how fuzzy expert systems can predict labor productivity for rigging and welding pipe activities in industrial construction. The authors identified productivity factors, designed fuzzy membership functions and expert rules, and validated the resulting models with data from a real construction project. The models achieved high linguistic prediction accuracy, showing that fuzzy expert systems are useful for both research and practical construction productivity forecasting.
The objective of this technical note is to illustrate the application of fuzzy expert systems to the modeling of a practical problem—that of predicting the labor productivity of two common industrial construction activities: rigging pipe and welding pipe. This note illustrates how to develop and test such a model, given the realistic constraints of subjective assessments, multiple contributing factors, and limitations on data sets. The factors that affect the productivity of each activity are identified, and fuzzy membership functions and expert rules are developed. The models are validated using data collected from an actual construction project. The resulting models are found to have high linguistic prediction accuracies. This note is of relevance to researchers by demonstrating how a fuzzy expert system can be developed and tested. It is of relevance to industry practitioners by illustrating how fuzzy logic and expert systems modeling can be exploited to help them solve real world problems.
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