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Title: Prediction of Ultimate Bearing Capacity of Eccentrically Loaded Rectangular Foundations using ANN
Authors: Sethy, Barada Prasad
Patra, C R
Sivakugan, N
Das, B M
Keywords: Eccentric load
Rectangular foundation
Depth of embedment
Neural network
Reduction factor
Issue Date: Jul-2017
Citation: GeoMEast 2017 International Congress & Exhibition, Sharm El-Sheikh, Egypt, 15–19 July 2017
Abstract: Extensive laboratory model tests were conducted on a rectangular embedded foundation resting over homogeneous sand bed and subjected to an eccentric load to determine the ultimate bearing capacity. The depth of embedment varies from 0 to 1B with an increment of 0.5B; where B is the width of foundation and the eccentricity ratio (e/B) varies from 0 to 0.15 with an increment of 0.05. Based on the laboratory model test results, a neural network model is developed to estimate the reduction factor (RF). The reduction factor can be used to estimate the ultimate bearing capacity of an eccentrically loaded foundation from the ultimate bearing capacity of a centrally loaded foundation. A thorough sensitivity analysis was carried out to determine the important parameters affecting the reduction factor. Importance was given on the construction of neural interpretation diagram, and based on this diagram, whether direct or inverse relationships exist between the input and output parameters was determined. The results from artificial neural network (ANN) were compared with the laboratory model test results and these results are well matched
Description: Copyright belongs to proceedings publisher
Appears in Collections:Conference Papers

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