Estimate-at-completion (EAC) prediction using Archimedes optimization with adaptive fuzzy and neural networks

Abstract

Construction companies estimate project costs at the beginning of the project; however, many factors impact the final project cost. Estimate at Completion (EAC) is a critical approach for estimating the final cost based on actual project performance. This paper aims to improve EAC predictions by integrating Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Artificial Neural Network (ANN) with Archimedes Optimization Algorithm (AOA). The integration of the input optimization algorithm aims to optimize the input features and explore the factors that significantly affect EAC. Using 306 data points from 13 construction projects in Taiwan between 2000 and 2007, this paper developed hybrid models and found a significant improvement in EAC estimation compared to ANN and ANFIS.