Service Life Assessment of Reinforced Concrete Structures in Coastal Regions of India Considering Global Warming and Deterioration Mechanisms Applying an ANN-Based Model

Corrosion in reinforced concrete (RC) structures poses a significant durability challenge, particularly in marine environments where climate change, driven by global warming, exacerbates deterioration by increasing climatic factors such as temperature and relative humidity. This study presents a comprehensive and reliable advanced service life prediction model that incorporates these climatic factors to predict the lifespan of RC structures in coastal India, particularly those subjected to corrosion. Parametric studies in this research evaluated the effects of environmental exposure (submerged, tidal/splash, atmospheric), global warming scenarios (without climate change, average temperature, expected climate change, and pessimistic climate change), and structural parameters (water-to-cement ratio, bar diameter, and cover thickness) on the degradation of RC structures over time. The model’s accuracy is compared with predictions from an artificial neural network (ANN) model. The efficacy of the ANN model is assessed through statistical measures such as sensitivity and specificity, and its performance is analyzed across these structural parameters. By applying soft computing techniques, this study effectively predicts the service life of RC structures. The predicted outputs are validated against expected results, confirming the model’s ability to accurately forecast the longevity of RC structures, taking into account the impact of global warming scenarios. The practical implementation of this study will support designers and decision-makers in making more informed and sustainable decisions regarding coastal structures, particularly in the context of climate change scenarios.