Analysis of Factors affecting Project Team Success in Post-Disaster Reconstruction Projects using Neural Network-based Feature Evaluation Technique

Abstract

Post-disaster reconstruction projects (PDRP) are integral to ensure that a community will recover and return to normal after a major disaster. The project team success (PTS) in PDRPs is essential to ensure that post-construction efforts will be effective and attain its objective of recovery in the community. An Artificial Neural Network (ANN) model was established considering several factors including post-disaster reconstruction project including project manager’s leadership style (PMLS), multi-disciplinary project competence (MDPC), project manager’s experience and competence (PMEC), high degree of trust within the project management team (HDTPMT), implementing an effective decision (IAED), effective project control (EPC), competent project manager (CPM), project risk and liability management (PRLM), motivated and well-integrated team (MWIT), and team composition (TC). The governing ANN model has a topology of 10-3-1 network structure and showed good performance with correlation plot (R) of 0.99850, MSE and MAPE of 0.00135 and 0.40559, respectively. The relative importance (RI) of the input parameters (IP) was also determined utilizing the connection weights (CWs) via Garson’s algorithm (GA). The findings showed that the MWIT factor is the most influential factor (MIF) to project team success in PDRPs. The results in this study could be utilized to focus on improving areas to guarantee the success of PDRPs.