Load carrying capacity assessment of thin-walled foundations: an ANFIS–PNN model optimized by genetic algorithm

A proper and reliable estimation of bearing capacity of thin-walled foundations is of importance and necessary for accurate design of these structures. This study proposes a new hybrid intelligent technique, i.e., adaptive neuro-fuzzy inference system (ANFIS)–polynomial neural network (PNN) optimized by the genetic algorithm (GA), called ANFIS–PNN–GA, for prediction of bearing capacity of the thin-walled foundations. In fact, in ANFIS–PNN–GA system, GA was used to optimize the ANFIS–PNN structure. To achieve the aim of this study, a series of data samples were collected from literature. After establishing the database, many ANFIS–PNN–GA models were constructed and proposed to estimate the bearing capacity of the aforementioned foundations. To show capability of this advance hybrid model, two pre-developed models i.e., ANFIS and PNN were also built to predict bearing capacity. The performance prediction of the proposed models were evaluated through the use of several performance indices, e.g., correlation coefficient (R) and mean square error (MSE). The R values of (0.9825, 0.9071, and 0.9928) and (0.8630, 0.7595 and 0.9241) were obtained for training and testing data of the ANFIS, PNN and ANFIS–PNN–GA, models, respectively. Accordingly, because of the role of GA as a practical optimization algorithm in improving the efficiency of both PNN and ANFIS models, results obtained by the ANFIS–PNN–GA model are more accurate compared to other implemented methods. The proposed advance hybrid model can be introduced as a new and applicable technique for solving problems in field of geotechnics and civil engineering.