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Prediction of combined static and cyclic load-induced settlement of shallow strip footing on granular soil using artificial neural network
The present study focuses on the development of an Artificial Neural Network (ANN) model equation to estimate the settlement of a shallow strip footing resting on granular soils due to combination of static and cyclic load. The model is developed using 324 number of datasets obtained from finite element analysis carried out with the help of Opensees. The input parameters are relative density (Dr %) of soil, depth of embedment (Df / B) of footing, intensity of static load depending on the factor of safety (FS), intensity of cyclic load (qd(max) / qu (%)) and frequency (f) of applied cyclic load to estimate non-dimensional settlement, s/su (%) of footing as output. Importance of input parameters are studied using Pearson’s correlation and Spearman’s rank correlation as well as sensitivity analysis based on Variable Perturbation method and Weight methods. The effect of input parameters on output is studied by using Neural Interpretation Diagram (NID).
Prediction of combined static and cyclic load-induced settlement of shallow strip footing on granular soil using artificial neural network
The present study focuses on the development of an Artificial Neural Network (ANN) model equation to estimate the settlement of a shallow strip footing resting on granular soils due to combination of static and cyclic load. The model is developed using 324 number of datasets obtained from finite element analysis carried out with the help of Opensees. The input parameters are relative density (Dr %) of soil, depth of embedment (Df / B) of footing, intensity of static load depending on the factor of safety (FS), intensity of cyclic load (qd(max) / qu (%)) and frequency (f) of applied cyclic load to estimate non-dimensional settlement, s/su (%) of footing as output. Importance of input parameters are studied using Pearson’s correlation and Spearman’s rank correlation as well as sensitivity analysis based on Variable Perturbation method and Weight methods. The effect of input parameters on output is studied by using Neural Interpretation Diagram (NID).
Prediction of combined static and cyclic load-induced settlement of shallow strip footing on granular soil using artificial neural network
Sasmal, Suvendu Kumar (author) / Behera, Rabi Narayan (author)
International Journal of Geotechnical Engineering ; 15 ; 834-844
2021-08-09
11 pages
Article (Journal)
Electronic Resource
Unknown
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