Please use this identifier to cite or link to this item:
|Title:||Neurofuzzy networks applied to settlement of shallow foundations on granular soils|
|Citation:||Applications of Statistics and Probability in Civil Engineering: Proceedings of the 9th International Conference on Applications of Statistics and Probability in Civil Engineering, San Francisco, California, USA, July 6-9, 2003, vol. 2 / A. Der Kiureghian, S. Madanat and J. M. Pestana (eds.): pp.1379-1383|
|Conference Name:||Applications of Statistics and Probability in Civil Engineering (2003 : San Francisco, California)|
|M.A. Shahin, M.B. Jaksa & H.R. Maier|
|Abstract:||In recent years, artificial neural networks (ANNs) have been applied successfully to many aspects of geotechnical engineering and its related fields. In the majority of these applications, multi-layer perceptrons (MLPs) that are trained with the back-propagation algorithm are used. This can be attributed to the fact that MLPs trained with back-propagation have a high capability of data mapping. However, one shortcoming of this type of ANN is that knowledge acquired during training is stored in the connection weights in a complex manner that is often difficult to interpret. Consequently, the rules governing the relationships between the network input/output variables are difficult to quantify, and thus ANNs are often criticized for being black boxes. One way to overcome this problem is to use neurofuzzy networks. Neurofuzzy networks combine the explicit knowledge representation of fuzzy systems with the potential power of ANN learning algorithms. Neurofuzzy networks can be trained by processing data samples to perform input/output mappings, similar to the way MLPs do, with the additional benefit of being able to translate the acquired knowledge into a set of transparent fuzzy rules that clearly describe the model input/output relationships. A review of the literature indicates that neurofuzzy networks are new tools in the field of geotechnical engineering. In this paper, the feasibility of using neurofuzzy networks in the field of geotechnical engineering is investigated for a case study of predicting settlement of shallow foundations on granular soils. The type of neurofuzzy network that is used in this work is a B-spline network, which uses the adaptive spline modeling of observation data (ASMOD) algorithm to automatically optimize the model structure and the number of model inputs. The results indicate that neurofuzzy networks are a useful technique that are: (i) capable of accurately predicting the settlement of shallow foundations on granular soils, and (ii) able to provide a transparent understanding of the relationship between settlement and the factors affecting it.|
|Keywords:||neurofuzzy networks; settlement; shallow foundations; prediction|
|Description:||© 2003 Millpress|
|Appears in Collections:||Civil and Environmental Engineering publications|
Environment Institute publications
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.