Article2007

Dynamic Prediction of Project Success Using Artificial Intelligence

79 citations0/0 files storedJournal of Construction Engineering and Management

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Construction Project Management and Performance Resource-Constrained Project Scheduling Economic and Technological Systems Analysis

Abstract

The purpose of construction management is to successfully accomplish projects, which requires a continuous monitoring and control procedure. To dynamically predict project success, this research proposes an evolutionary project success prediction model (EPSPM). The model is developed based on a hybrid approach that fuses genetic algorithms (GAs), fuzzy logic (FL), and neural networks (NNs). In EPSPM, GAs are primarily used for optimization, FL for approximate reasoning, and NNs for input-output mapping. Furthermore, the model integrates the process of continuous assessment of project performance to dynamically select factors that influence project success. The validation results show that the proposed EPSPM, driven by a hybrid artificial intelligence technique, could be used as an intelligent decision support system, for project managers, to control projects in a real time base.

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References

1965Fuzzy sets 1996Fuzzy sets 2004Factors Affecting the Success of a Construction Project 1992Critical Success Factors for Construction Projects 2004Exploring Critical Success Factors for Partnering in Construction Projects 2004A study on project success factors in large construction projects in Vietnam 1993Checklist of Critical Success Factors for Building Projects 2004Measuring Project Success in the Construction Industry

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2012Hybrid principal component analysis and support vector machine model for predicting the cost performance of commercial building projects using pre-project planning variables 2015Artificial Intelligence Applied to Project Success: A Literature Review 2010Benchmarking success of building maintenance projects 2020An Approach Based on Bayesian Network for Improving Project Management Maturity: An Application to Reduce Cost Overrun Risks in Engineering Projects 2010Project success prediction using an evolutionary support vector machine inference model

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