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.

The use of Cocos nucifera (coconut shell) ash as a supplementary cementitious material has shown potential in enhancing the compressive strength of concrete. However, the optimal calcination temperature and duration for producing effective ash remain uncertain. This study employs a Central Composite Design (CCD) to investigate the effect of calcination on concrete strength. Nine combinations of temperatures 550°. to 800°C) and durations (1 to 3 hours) were tested, producing 13 samples, which were cured for 28 days before compressive strength testing. X-ray Fluorescence (XRF) analysis identified 15 elements, with iron significantly influencing strength. The highest compressive strength (24.9 MPa) was achieved at 675°C for 2 hours, where iron content reached 16.63 %. A full quadratic regression model was developed, with an R2 of 79.47 %, and backward elimination refined the model to a predicted R2 of 67.32 %. Sensitivity analysis revealed temperature as the most significant factor, with a sensitivity value of 14.53 compared to 1.48 for duration. Optimization indicated the ideal calcination temperature to be 672.81° C. This study supports sustainable development goals by advancing innovative materials for infrastructure and by promoting the use of agricultural waste, reducing the environmental footprint of concrete production.

This study aimed to develop models for predicting trip frequency in San Jose City, Province of Nueva Ecija, Philippines incorporating socio-demographic factors (SDF) and attitudinal factors (AF) through the use of artificial neural network (ANN). Socio-demographic factors in the model include age, sex, civil status (CS), number of children (NOC), barangay, number of household members (NHM), educational attainment (EA), employment status (ES), household income (HI), number of driver license holder (DLH), number of personal vehicles owned (PVO), and number of vehicles owned by the household (VOH) while the attitudinal factors in the model include car dependency (CD), convenience, speed, privacy and safety (PS), health and environment (HE), cost, and comfort. The collected data were processed to develop ANN model in different pandemic stages with 19-19-1 (input-hidden-output) network structure used for these models. The sensitivity analysis (SA) results indicate that in the pre-pandemic period, employment status is the most influential parameter (MIP) to the trip frequency in the study area, while the educational attainment is the MIP during the pandemic period and in the post-pandemic period. The findings of the study signify the effectiveness of ANN in forecasting trip frequency as evident to the low mean absolute percentage error (MAPE) values obtained for the three models. The results can be used by policymakers in making informed strategies in further improving the travel experience of the population in the study area.

The inclusion of Artificial Intelligence (AI) and Machine Learning (ML) into cybersecurity education offers a significant opportunity to customize learning, improve engagement, and connect theoretical concepts with practical applications. This study presents the Dynamic Integration and Optimization of NetCyber Activities (DIONA) system which is an AI-enhanced educational tool developed from the NetFusion Learning Academy (NLA) to tackle ongoing issues in conventional cybersecurity education, including restricted adaptability, absence of real-time feedback, and inadequate practical skill application. The study employs a systematic methodology based on four distinct objectives: (1) to assess the efficacy of NLA in improving student learning across five critical domains— knowledge retention, practical skills, engagement, conceptual understanding, and problem-solving; (2) to examine student and faculty perceptions of its educational value; (3) to develop the ACTIVE AI Framework for AI-driven pedagogy; and (4) to create and validate DIONA as an AI/ML-based experiential learning platform. Statistical and thematic analyses indicated that although NLA effectively enhances knowledge and engagement, deficiencies persist in practical skills and problem-solving, necessitating the incorporation of AI-powered tools. The ACTIVE AI Framework and DIONA system offer customized learning trajectories, AI-generated feedback, and immersive simulations that correspond with authentic cybersecurity challenges. Results endorse the significance of intelligent learning analytics and specialized AI systems in transforming technical education and equipping students for changing digital environments.