Performance evaluation of nature-inspired algorithms for the design of bored pile foundation by artificial neural networks

The bored pile foundations are gaining popularity in construction industry because of ease in construction, low noise and vibrations. The load-carrying capacity of bored pile foundations is dependent upon soil-structure interaction. This being a three-dimensional problem is further complicated due to large variations in soil properties. Also, modeling of soil is difficult because of its nonlinear and anisotropic nature. For such cases, the artificial neural network (ANN) and nature-inspired optimization techniques have been found to be highly suitable to attain acceptable levels of accuracy. In the present study, two ANNs have been trained for determination of unit skin friction and unit end bearing capacity from soil properties. The training data for ANNs have been obtained from finite element analysis of pile foundations for 4809 different soil types. A dataset of 50 field pile loading test results is used to check the performance of the developed artificial neural networks. To enhance the accuracy of the developed ANNs, two correlation factors have been determined by applying four popular nature-inspired optimization algorithms: particle swarm optimization (PSO), fire flies, cuckoo search and bacterial foraging. In order to rank these optimization algorithms, parametric and non-parametric statistical analysis has been carried out. The results of optimization algorithms have been compared to find the most suitable solution for this multi-dimensional problem which has a large number of nonlinear equality constraints. The effectiveness and suitability of the nature-inspired algorithms for the presented problem have been demonstrated by computing correlation coefficients with field pile loading test results and then with the total execution time taken by each algorithm. The results of comparison show that PSO is the best performer for such constrained problems.