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 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.

The factor evaluation for sustainability implementation in post-disaster reconstruction projects was conducted using artificial neural network (ANN)-based sensitivity analysis. The current study involves the utilization of Levenberg-Marquardt algorithm (LMA) for the ANN model development. The findings demonstrated that the governing network structure is 10-3-1 utilizing input parameters (IP) including waste management (Sl), environmental impact management (S2), support of management (S3), construction cost (S4), public health and safety (S5), user security (S6), noise pollution (S7), energy consumption (S8), public services (S9), and recycling (S10). The results revealed that the governing model (GM) has a performance of correlation (R) = 0.99813 and Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) of 0.00110 and 0.4240/0, respectively. Moreover, a sensitivity analysis (SA) using Garson's algorithm (GA) reveals a trend of the relative value (RV) was observed to be construction cost > public services > support of management > user security > environmental impact management> noise pollution> public health and safety> energy consumption > recycling > waste management wherein the construction cost is the most influential parameter to the sustainability rating in post-disaster reconstruction projects (PDRP). The study shows how well ANN can detect the crucial elements that determine a project's sustainability rating in the post-disaster reconstruction projects.

Corrosion stands as the primary cause behind the diminished service life of reinforced concrete structures, particularly in environments such as ports, harbors, bridges, and other offshore and near-shore locations where chloride-induced corrosion poses a significant threat. This study investigates the efficacy of three key parameters polypropylene fiber ratio (FR), concrete cover (CC), and bar diameter (∅)—in minimizing corrosion (CL) in reinforced concrete structures. Central Composite Design (CCD) of Response Surface Methodology (RSM) is employed to determine the optimal conditions for these parameters. The number of samples for Impressed Current (IC) testing is determined through this methodology. Initial analysis utilizing the full quadratic model yields a Predicted value R2 of 60.77%. However, employing backward elimination enhances the predictive capability, resulting in an improved R2 value of 87.53%. Sensitivity analysis utilizing the coded units of the RSM model reveals that the polypropylene fiber ratio exerts the most significant impact on corrosion levels, with a sensitivity value of -4.932. Consequently, optimization efforts are focused on this parameter, leading to the identification of an optimized value of 1.17% for FR, which results in minimal corrosion. This research underscores the effectiveness of employing RSM techniques in optimizing corrosion mitigation strategies in reinforced concrete structures, with FR emerging as a critical determinant in achieving corrosion resistance.