Analysis of Factors affecting Project Team Success in Post-Disaster Reconstruction Projects using Neural Network-based Feature Evaluation Technique
Proceedings of the 2025 9th International Conference on Advances in Artificial Intelligence, (2026), pp. 245-251
Junjun H. Moreno
a
,
Dante Laroza Silva
b
,
Kevin Lawrence M. De Jesus
a
,
Renato Borja Jr.
c
,
Donna Ville Leopoldo Gante
a
,
Meriam Leopoldo
d
,
Bon Ryan P. Aniban
a
,
Crispin Lictaoa
e
,
Ralph Alwin De Jesus
c
,
Jordan Velasco
f
a Department of Civil Engineering, FEU Institute of Technology, Manila, Metro Manila, Philippines
b School of Civil Environmental, and Geological Engineering, Mapua University, Manila, Metro Manila, Philippines
c School of Graduate Studies, Mapua University, Manila, Metro Manila, Philippines
d College of Engineering and Architecture, Mapua Malayan Colleges Mindanao, Davao City, Davao del Sur, Philippines
e Civil Engineering Department, Adamson University, Manila, Metro Manila, Philippines
f College of Engineering, Pamantasan ng Lungsod ng Valenzuela, Valenzuela City, Metro Manila, Philippines
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.