A neural network model for the uplift capacity of suction caissons

Suction caissons are frequently used for the anchorage of large compliant o ffshore structures. The uplift capacity of the suction caissons is a critic al issue in these applications, and reliable methods: of predicting the cap acity are required in order to produce effective designs. In this paper a b ack-propagation neural network model is developed to predict the uplift cap acity of suction foundations. A database containing the results from a numb er of model and centrifuge tests is used. The results of this study indicat e that the neural network model serves as a reliable and simple predictive tool for the uplift capacity of suction caissons. As more data becomes avai lable, the model itself can be improved to make more accurate capacity pred iction for a wider range of load and site conditions, The neural network pr edictions are also compared with finite element based predictions. (C) 2001 Elsevier Science Ltd. All rights reserved.