Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Piled-raft in recent years has been accepted as an economical and efficient form of foundation which can sustain high loads in structures such as high rise buildings. In this paper, load-settlement behaviour of the raft and piled-raft foundation has been investigated. To study the behaviour, a model with raft and piled-raft foundation system was developed and subjected to quantum of loads. The various tests were conducted by varying number of piles, diameter of piles, Length by width ratio of piles and relative densities of soil. In order to transform the results of this whole series of experiments on computing platform artificial neural networks (ANN) on Matlab was used to predict the settlements and was compared with experimental results. To develop ANN model, 687 data points were used. The process of developing neural model includes selection of various internal parameters to obtain better predictive ANN model. It was observed that ANN was able to give results much closer to experimental results and also at some instances it has behaved like an experimental set up. The sensitivity analysis done in this study gives the effect of each parameter in percentages on settlement. The number of piles in the model used contributes 26.82% for settlement.
Piled-raft in recent years has been accepted as an economical and efficient form of foundation which can sustain high loads in structures such as high rise buildings. In this paper, load-settlement behaviour of the raft and piled-raft foundation has been investigated. To study the behaviour, a model with raft and piled-raft foundation system was developed and subjected to quantum of loads. The various tests were conducted by varying number of piles, diameter of piles, Length by width ratio of piles and relative densities of soil. In order to transform the results of this whole series of experiments on computing platform artificial neural networks (ANN) on Matlab was used to predict the settlements and was compared with experimental results. To develop ANN model, 687 data points were used. The process of developing neural model includes selection of various internal parameters to obtain better predictive ANN model. It was observed that ANN was able to give results much closer to experimental results and also at some instances it has behaved like an experimental set up. The sensitivity analysis done in this study gives the effect of each parameter in percentages on settlement. The number of piles in the model used contributes 26.82% for settlement.
Predicting the Settlement in Raft and Piled-Raft Foundations using neural models
13.06.2018
oai:zenodo.org:1288017
International Journal of Advanced Structures & Geotechnical Engineering 6(4) 134-143
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC:
621
Settlement Behaviour of Piled Raft Foundations
| British Library Conference Proceedings | 1996
Settlement analysis of piled raft foundations
| British Library Conference Proceedings | 2006
Settlement of piled raft foundations on clay
| British Library Conference Proceedings | 1994
Settlement of piled foundations using equivalent raft approach
| British Library Online Contents | 2010
Piled Raft and Compensated Piled Raft Foundations for Soft Soil Sites
| British Library Conference Proceedings | 2005