Bearing capacity prediction of inclined loaded strip footing on reinforced sand by ANN

Sahu, R., Patra, C.R., Sivakugan, N., and Das, B.M. (2017) Bearing capacity prediction of inclined loaded strip footing on reinforced sand by ANN. In: Shukla, Sanjay Kumar, and Guler, Erol, (eds.) Advances in Reinforced Soil Structures. Sustainable Civil Infrastructures . Springer, Cham, Switzerland, pp. 97-109.

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Abstract

Laboratory model tests have been conducted on a strip foundation resting over multi-layered geogrid-reinforced dense and loose sand subjected to inclined load. Based on the laboratory model test results, a neural network model is developed to estimate the reduction factor for bearing capacity. The reduction factor obtained by ANN can be used to estimate the ultimate bearing capacity of a strip foundation subjected to centric inclined load from the ultimate bearing capacity of the same foundation under centric vertical loading. A thorough sensitivity analysis was carried out to find out the important parameters affecting the reduction factor. Emphasis was given on the construction of neural interpretation diagram, based on the weights developed in the neural network model, to determine the direct or inverse effect of input parameters to the output. An ANN model equation is developed based on trained weights of the neural network model. The results from artificial neural network (ANN) were com-pared with the laboratory model test results and these results are in good agreement.

Item ID: 49677
Item Type: Book Chapter (Research - B1)
Keywords: inclined load; geogrid; sand; neural network; ultimate bearing capacity; reduction factor
Additional Information:

This paper was presented at the 1st GeoMEast International Congress and Exhibition, Sharm el Sheik, Egypt, 15-20 July 2017

ISBN: 978-3-319-63569-9
ISSN: 2366-3413
Date Deposited: 01 Aug 2017 04:32
FoR Codes: 09 ENGINEERING > 0905 Civil Engineering > 090501 Civil Geotechnical Engineering @ 100%
SEO Codes: 87 CONSTRUCTION > 8702 Construction Design > 870201 Civil Construction Design @ 100%
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