Optimization of Sustainable Seismic Retrofit by Developing an Artificial Neural Network

Reinforced concrete structures often require retrofitting due to damage caused by natural disasters such as earthquakes, floods, or hurricanes; deterioration from aging; or exposure to harsh environmental conditions. Retrofitting strategies may involve adding new structural elements like shear walls, dampers, or base isolators, as well as strengthening the existing components using methods such as reinforced concrete, steel, or fiber-reinforced polymer jacketing. Selecting the most appropriate retrofit method can be complex and is influenced by various factors, including initial cost, long-term maintenance, environmental impact, and overall sustainability. This study proposes utilizing an artificial neural network (ANN) to predict sustainable and cost-effective seismic retrofit solutions. By training the ANN with a comprehensive dataset that includes jacket thickness, material specifications, reinforcement details, and key sustainability indicators (economic and environmental factors), the model was able to recommend optimized retrofit designs. These designs include ideal values for jacket thickness, concrete strength, and the configuration of reinforcement bars, aiming to minimize both costs and environmental footprint. A major focus of this research was identifying the optimal number of neurons in the hidden layers of the ANN. While the number of input and output neurons is defined by the dataset, determining the right configuration for hidden layers is critical for performance. The study found that networks with one or two hidden layers provided more reliable and efficient results compared to more complex architectures, achieving a total regression value of 0.911. These findings demonstrate that a well-tuned ANN can serve as a powerful tool for designing sustainable seismic retrofit strategies, helping engineers make smarter decisions more quickly and efficiently.