Forecasting project success in the construction industry using adaptive neuro-fuzzy inference system
Document Type
Journal Article
Publication Title
International Journal of Construction Management
Publisher
Taylor & Francis
School
School of Business and Law
RAS ID
64617
Abstract
Project managers often find it a challenge to successfully manage construction projects. As a result, understanding, evaluating, and achieving project success are critical for sponsors to control projects. In practice, determining key success factors and criteria to assess the performance of construction projects and forecast the success of new projects is difficult. To address these concerns, our objective is to go beyond the efficiency-oriented project success criteria by considering both efficiency- and effectiveness-oriented measures to evaluate project success. This paper contributes to existing knowledge by identifying a holistic and multidimensional set of project success factors and criteria using a two-round Delphi technique. We developed a decision support system using the Adaptive Neuro-Fuzzy Inference System (ANFIS) to forecast the success of mid- and large-sized construction projects. We gathered data from 142 project managers in Australia and New Zealand to implement the developed ANFIS. We then validated the constructed ANFIS using the K-fold cross-validation procedure and a real case study of a large construction project in Western Australia. The forecasting accuracy measures R2=0.97461, MAPE = 2.57912%, MAE = 1.88425, RMSE = 2.3610, RRMSE = 0.03149, and PI = 0.01589 suggest that the developed ANFIS is a very good predictor of project success.
DOI
10.1080/15623599.2023.2266676
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Comments
Kiani Mavi, N., Brown, K., Fulford, R., & Goh, M. (2023). Forecasting project success in the construction industry using adaptive neuro-fuzzy inference system. International Journal of Construction Management. Advance online publication. https://doi.org/10.1080/15623599.2023.2266676