Neural Network Approach for Ranking of Critical Factors in Project Control Mechanism for Mid-Rise Residential Building Construction in Metro Manila
2025 8th International Conference on Big Data and Artificial Intelligence (BDAI), (2026), pp. 58-64
Virgilio R. Villaescusa
a
,
Dante L. Silva
a
,
Kevin Lawrence M. De Jesus
b
,
Francis Anthony G. Llacuna
c
,
Donna Ville L. Gante
b
,
Broderick V. Flores
d
,
Meriam P. Leopoldo
e
,
Sheina R. Pallega
f
a School of Graduate Studies, Mapúa University, Manila, Philippines
b Department of Civil Engineering, FEU Institute of Technology, Manila, Philippines
c Mathematics Department, Mapúa University, Manila, Philippines
d School of Civil, Environmental, and Geological Engineering, Mapúa University, Manila, Philippines
e College of Engineering and Architecture, Mapúa Malayan Colleges Mindanao, Davao, Philippines
f College of Engineering, National University, Philippines, Manila, Philippines
Abstract: The construction of mid-rise residential buildings in Metro Manila faces constant challenges related to project control inefficiencies, leading to delays, budget overruns, and quality concerns. This study aims to rank the factors critical to project control mechanisms (PCM), providing insights into the key drivers of project success in mid-rise residential construction projects. An Artificial Neural Network (ANN) model was developed to validate these rankings, utilizing the LevenbergMarquardt training algorithm and tansig activation function. The model achieved exceptional predictive accuracy, with an overall R of 0.99445, along with a low MSE (0.007515) and MAPE (1.6808%). Using the connection weights from the model, the analysis revealed that stakeholders influence, technology integration, and contractor performance are the top three most critical factors, highlighting the importance of collaborative decision-making, digital transformation, and contractor accountability. Resource allocation, quality standards, and schedule delays ranked mid-tier, while budget management, scope definition, and labor productivity were perceived as less critical in comparison. The findings provide a data-driven basis for improving project control strategies, offering valuable insights for construction managers, policymakers, and urban developers to enhance efficiency, minimize risks, and optimize decision-making in Metro Manila’s mid-rise construction sector.