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A platform for research: civil engineering, architecture and urbanism
Modelling the capacity of micropiled-raft foundation rested on soft clayey soil using an artificial neural network approach
Micropiled-raft foundation shows its ability to solve particular type of foundation problem i.e. building of medium height and weight structures over soft deep-seated clayey soil deposit. Potential of artificial neural network method was used in the study for the assessment of capacity of micropiled-raft foundation under a given settlement in soft clayey soil considering the complex non-linear load-settlement behaviour. Experiments were conducted on micropiled-raft foundation constructed with different variables within a soft clayey soil bed in the test pit in field and a database was prepared from load-settlement graphs those were utilized to develop different ANN models. It was observed from the study that Bayesian Regularization algorithm with 90–10% validation model performs the best. Sensitivity analysis was performed to determine the relative significance of different input variables and a neural interpretation diagram was prepared. An empirical equation was proposed with best fit ANN model and an example illustrated.
Modelling the capacity of micropiled-raft foundation rested on soft clayey soil using an artificial neural network approach
Micropiled-raft foundation shows its ability to solve particular type of foundation problem i.e. building of medium height and weight structures over soft deep-seated clayey soil deposit. Potential of artificial neural network method was used in the study for the assessment of capacity of micropiled-raft foundation under a given settlement in soft clayey soil considering the complex non-linear load-settlement behaviour. Experiments were conducted on micropiled-raft foundation constructed with different variables within a soft clayey soil bed in the test pit in field and a database was prepared from load-settlement graphs those were utilized to develop different ANN models. It was observed from the study that Bayesian Regularization algorithm with 90–10% validation model performs the best. Sensitivity analysis was performed to determine the relative significance of different input variables and a neural interpretation diagram was prepared. An empirical equation was proposed with best fit ANN model and an example illustrated.
Modelling the capacity of micropiled-raft foundation rested on soft clayey soil using an artificial neural network approach
Borthakur, Nirmali (author) / Das, Manita (author)
International Journal of Geotechnical Engineering ; 16 ; 558-573
2022-05-28
16 pages
Article (Journal)
Electronic Resource
Unknown
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