Abstract
A NeuroFuzzy System (NFS) is one of the most commonly used systems in the real life problems because it has explicit and transparency which results from the fuzzy systems, with the learning and generalization capabilities from the dynamic behavior of the neural networks. It is one of the most successful systems, which introduced to decrement the fuzzy rules that constituting the underlying model. This system has a high efficiency; it gives good results in high speed. The NFS used in this study to predict the settlement of deep pile foundations. The results obtained from this system give good agreement and high precious for prediction of settlement compared with hyperbolic model and statistical regression analysis. Also, this scenario can be applied for similar or more complicated problems in the geotechnical engineering.
Highlights
The design of foundations is generally controlled by the criteria of bearing capacity and settlement; the latter is often governing
NF model can predict the values of settlement as shown in the following graphs using the programs coding in Appendix A, through applying long period of time out of the range of the learning data
NeuroFuzzy System (NFS) have been applied to many areas of geotechnical engineering and have demonstrated considerable success
Summary
The design of foundations is generally controlled by the criteria of bearing capacity and settlement; the latter is often governing. The input variables used in this study for the proposed neural network consisted of the time and load according to the field measurements. The ability of NF networks to predict settlement of deep pile foundations in deep soft soils and to assist with providing a better understanding regarding the relationships between settlement and the factors affecting on settlement is assessed. Dealing with a large size fuzzy model can pose many practical problems, such as, the increase of training time for the system’s weights and the difficulty of updating them [2,3,4]. The model used in this study deals with the NF network to overcome these difficulties of the practical problems representing the settlement behavior in the deep soft soil area
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