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Title: Bearing Capacity Prediction of Inclined Loaded Strip Footing on Reinforced Sand by Ann
Authors: Sahu, Roma
Patra, C R
Sivakugan, N
Das, B M
Keywords: Inclined load
Neural network
Ultimate bearing capacity
Reduction factor
Issue Date: Jul-2017
Citation: GeoMEast 2017 International Congress & Exhibition, Sharm El-Sheikh, Egypt, 15–19 July 2017
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 an inclined loaded foundation from the ultimate bearing capacity of a vertically loaded foundation. 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 compared with the laboratory model test results and these results are in good agreement.
Description: Copyright belongs to proceedings publisher
Appears in Collections:Conference Papers

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