Studying the behavior of neural models under hybrid and reinforced foundations

The emergence of computational power with time has gathered the interests of civil and geotechnical engineers in problem solving. The use of artificial intelligence (AI)-based models in many geotechnical problems helps in saving time, incorporating variations easily and understanding the interrelation between various parameters. In this study, AI-based artificial neural networks were used for hybrid foundation and reinforced foundation. These models have been compared as to how they behave differently for different foundation systems. The models were constructed from results of laboratory-level test set up for hybrid and reinforced foundation systems. For one hybrid foundation and two types of reinforcement, different neural models were made. Therefore, three different neural models specific to each foundation type have been compared to understand the behavior of neural models. It has been found that neural models also help in understanding the better performing system of foundations conforming to the experimental findings. The run time can be reduced if all adjustable parameters are initially adjusted.