Estimate at Completion for construction projects using Evolutionary Support Vector Machine Inference Model

Abstract

Construction projects are influenced by a range of factors that impact upon final project cost. Estimate at Completion (EAC) is an important approach used to estimate final project cost, which takes into consideration probable project performance and risks. EAC helps project managers identify potential but still unknown problems and adopt response strategies. This study constructed an evolutionary EAC model to generate project cost estimates that proved significantly more reliable than estimates achievable using currently prevailing formulae. The developed learning model fused two artificial intelligence approaches, namely the fast messy genetic algorithm (fmGA) and Support Vector Machine (SVM), to create an Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM was then applied to estimate final project costs for historical cases. Finally, using the EAC estimate, project cost influence indices, and project cost diagrams, the discrepancy between estimate and practical values was examined to determine potential problems in order to help project managers better control project costs. The learning results were validated in real applications that showed good performance for training models. Providing project managers reliable EAC trend estimates is helpful for their effective control of project costs and taking appropriate peremptory measures to handle potential problems.