Chloride-induced corrosion (CIC) is a primary reason of deterioration in reinforced concrete (RC), particularly in marine structures which causes cracking, degradation, and decreased service life. Advances in the 4th Industrial Revolution have enabled utilization of machine learning techniques in different fields of civil engineering. This study develops an Artificial Neural Network (ANN) enhanced by Particle Swarm Optimization (PSO) to predict rebar corrosion in polypropylene fiber reinforced concrete (PFRC). Accelerated corrosion tests were performed using the impressed current method on samples with varying polypropylene fiber content, concrete cover (CC), and bar diameter (BD). Experimental results showed that the 3-7-1 network structure (NS) (3 input neurons (IN), 7 hidden neurons (HN), 1 output neuron (ON)) achieved the highest accuracy with correlation coefficient (R) of 0.98969, mean squared error (MSE) of 0.18846, and mean absolute percentage error (MAPE) of 7.832 %. Employing the generated connection weights (CW) from the governing model (GM), through Olden's connection weights approach, observed that the concrete cover had the most significant influence on corrosion (-43.231%), followed by bar diameter (33.717%) and fiber content (-23.052%). It highlights that increasing concrete cover and fiber content significantly reduces corrosion in PFRC, which may be used by civil engineering professionals as it offers insights for enhancing the durability of reinforced concrete structures. This approach supports SDG 9 (Sustainable Development Goal 9: Industry, Innovation, and Infrastructure) by promoting resilient, innovative construction methods and contributes to SDG 11 (Sustainable Development Goal 11: Sustainable Cities and Communities) by enhancing the longevity and sustainability of urban infrastructure.

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.