%0 Journal Article
%T Application of Machine Learning Technique in Predicting the Bearing Capacity of Rectangular Footing on Layered Sand under Inclined Loading
%J Journal of Soft Computing in Civil Engineering
%I Pouyan Press
%Z 2588-2872
%A Panwar, Vishal
%A Dutta, Rakesh Kumar
%D 2022
%\ 10/01/2022
%V 6
%N 4
%P 130-152
%! Application of Machine Learning Technique in Predicting the Bearing Capacity of Rectangular Footing on Layered Sand under Inclined Loading
%K Bearing Capacity
%K Layered sand
%K rectangular footing
%K Inclined load
%K Machine Learning Technique
%R 10.22115/scce.2022.343236.1445
%X The aim of the present study is to apply machine learning technique to predict the ultimate bearing capacity of the rectangular footing on layered sand under inclined loading. For this purpose, a total 5400 data based on the finite element method for the rectangular footing on layered sand under inclined loading were collected from the literature to develop the machine learning model. The input variables chosen were the thickness ratio (0.00 to 2.00) of the upper dense sand layer, embedment ratio (0 to 2), the friction angle of upper dense (410 to 460) sand and lower loose (310 to 360) sand layer and inclination (00 to 450) of the applied load with respect to vertical. The output is the ultimate bearing capacity. Further, the impact of the individual variable on the bearing capacity was also assessed by conducting sensitivity analysis. The results reveal that, the load inclination is the major variable affecting the bearing capacity at embedment ratio 0, 1 and 2. Finally, the performance of the developed machine learning model was assessed using six assessing statistical parameters. The results reveal that the developed model was performing satisfactorily for the prediction of the ultimate bearing capacity of the rectangular footing on layered sand under inclined loading.
%U https://www.jsoftcivil.com/article_158236_c36963bdb7592e6ad7ae24e3a37bba5f.pdf