|Author||Sahoo, Rupashree Ragini|
|Researcher||Sahoo, Rupashree Ragini|
|Educational Degree||Master of Technology (M.Tech.)|
|Subject Domain (in DDC)||Technology ♦ Engineering & allied operations ♦ Other branches of engineering|
|Subject Keyword||Geotechnical Engineering|
|Abstract||The bearing capacity and settlement study of shallow footings is a subject which needs consideration for design of a foundation. Most of the studies relate to the case of a vertical load applied centrally to the foundation. However, when loads are applied eccentrically to the foundation, the bearing capacity is different from centrally loaded footings. Meyerhof (1953) developed empirical procedures for estimating the ultimate bearing capacity of foundations subjected to eccentric loads. Based on the review of the existing literature on the bearing capacity of shallow foundations, it shows that limited attention has been paid to estimate the ultimate Bearing capacity of eccentrically loaded square foundation with depth of embedment Df. Hence the present work attempts to investigate the bearing capacity of eccentrically loaded square embedded footing. Square footings of size 10cm x 10cm are used for load-test in the laboratory. Embedment ratio Df /B is varied from zero to one and the eccentricity ratio e/B varying from zero to 0.15 with sand of relative density (Dr) equal to 69%. Ultimate bearing capacity has been found out for central as well as eccentric loading condition. An empirical equation has been developed for the reduction factor in predicting the bearing capacity of eccentrically loaded square embedded foundation. The results of the previous investigators are also analysed and compared with the present experiment. An Artificial Neural Network model is developed to estimate reduction factor (RFs) for settlement. Based on the laboratory model test results taken from Patra et al. (2013) a mathematical equation have been developed by ANN to determine the settlement of eccentrically loaded embedded strip footings. Also the model equation for reduction factor obtained from ANN analysis have been compared with empirical equation proposed by Patra et al. (2013). The predictability of ANN model is found to be better than empirical one.|
|Education Level||UG and PG|
|Learning Resource Type||Thesis|
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