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Response of rectangular footing resting on reinforced silty sand treated with lime using experimental and computational approach
The usage of geosynthetics can be employed widely in the field and geotechnical engineering. The ground improvement approaches using geosynthetics as reinforcing materials are widely used to overcome the limitations of other methods. Considering these aspects, a sequence of model load versus settlement experiments with variance in the position of geotextile and percentage of lime was executed to observe the toughness as well as settlement properties of the silty sand with single layer of geotextile. There are several aspects of this paper like the computational techniques such as artificial neural network (ANN) and extreme learning machine (ELM). The implementation of computational models gives an advanced resolution for forecasting the load sustaining capability of the rectangular footing in a simple and economic way. These computational models could be judged by employing several well-approved statistical indices. It could also be verified through the experimental results achieved from the studies. The upshot shows that the developed ELM model has a remarkable potential to evaluate the ultimate bearing capacity (UBC) of the rectangular footing. It can be set forth as a predictive tool for the initial designing process.
Response of rectangular footing resting on reinforced silty sand treated with lime using experimental and computational approach
The usage of geosynthetics can be employed widely in the field and geotechnical engineering. The ground improvement approaches using geosynthetics as reinforcing materials are widely used to overcome the limitations of other methods. Considering these aspects, a sequence of model load versus settlement experiments with variance in the position of geotextile and percentage of lime was executed to observe the toughness as well as settlement properties of the silty sand with single layer of geotextile. There are several aspects of this paper like the computational techniques such as artificial neural network (ANN) and extreme learning machine (ELM). The implementation of computational models gives an advanced resolution for forecasting the load sustaining capability of the rectangular footing in a simple and economic way. These computational models could be judged by employing several well-approved statistical indices. It could also be verified through the experimental results achieved from the studies. The upshot shows that the developed ELM model has a remarkable potential to evaluate the ultimate bearing capacity (UBC) of the rectangular footing. It can be set forth as a predictive tool for the initial designing process.
Response of rectangular footing resting on reinforced silty sand treated with lime using experimental and computational approach
Yousuf, Syed Md (author) / Khan, M.A. (author) / Ibrahim, S.M. (author) / Sharma, Anil Kumar (author) / Ahmad, Furquan (author)
Geomechanics and Geoengineering ; 19 ; 139-161
2024-03-03
23 pages
Article (Journal)
Electronic Resource
English
Silty sand , lime , geotextile , rectangular footing , ANN , ELM