Improving estimate at completion (EAC) cost of construction projects using adaptive neuro-fuzzy inference system (ANFIS)

Earned value management (EVM) is a well-known technique for measuring project performance and progress. Owing to the EVM's attitude simultaneously combine cost time performance, can be forecasted accurately, this plays vital role in future of projects. In current study, authors employed an adaptive neuro-fuzzy inference system (ANFIS) as powerful prediction tool forecast completion projects considering percentage risk qualitative variables comparing it with other types neural networks. Because network structure usually tuned based on obtained results, optimization procedure applied using conventional method estimating cost-caused breakdown. The results showed that ANFIS had suitable (MSE = 0.0003), sensitivity analysis, earned recognized most sensitive factor project. This study improves general estimate formula by uncertain conditions.