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Bearing Capacity of Shallow Foundation's Prediction through Hybrid Artificial Neural Networks

The utilization of Artificial Neural Network (ANN) for bearing capacity estimation has some disadvantages such as getting trapped in local minima and slow rate of learning. Recent developments of optimization algorithms such as Particle Swarm Optimization (PSO) have made it possible to overcome ANN drawbacks and improve its efficiency. This paper presents a unified approach of ANN based on PSO algorithm to predict bearing capacity of shallow foundations in granular soils. To generate the network, numbers of 40 datasets including the recorded cases of full-scale axial compression load test on shallow foundations in granular soils were collected from literatures. Each dataset refers to a set of 6 inputs consisted of footing length and width, embedded depth of the footing, average vertical effective stress of the soil, friction angle of the soil, and ground water level as well as one output consisted of the ultimate axial bearing capacity. Several sensitivity analyses were conducted to determine the optimum parameters of PSO algorithm and the network architecture was determined following the trial and error method. The results demonstrate that the presented model predicts the bearing capacity of shallow foundations with high degree of accuracy.