A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Reliability Analysis of Pile Foundation Using ELM and MARS
Abstract In recent time, there have been significant advances in the reliability based design to take care of variability and uncertainty in the pile design. The principal objective of the study is to examine the applicability of Extreme Learning Machine (ELM) and Multivariate Adaptive Regression Spline (MARS) models for predicting the bearing capacity of a pile embedded in cohesionless soil and comparing their respective performances. The models are developed using numerical analysis and conventional equations. A comparative study is made between reliability indices obtained by First Order Second Moment Method (FOSM) and MARS and ELM based FOSM. The performance was evaluated using various performance parameters.
Reliability Analysis of Pile Foundation Using ELM and MARS
Abstract In recent time, there have been significant advances in the reliability based design to take care of variability and uncertainty in the pile design. The principal objective of the study is to examine the applicability of Extreme Learning Machine (ELM) and Multivariate Adaptive Regression Spline (MARS) models for predicting the bearing capacity of a pile embedded in cohesionless soil and comparing their respective performances. The models are developed using numerical analysis and conventional equations. A comparative study is made between reliability indices obtained by First Order Second Moment Method (FOSM) and MARS and ELM based FOSM. The performance was evaluated using various performance parameters.
Reliability Analysis of Pile Foundation Using ELM and MARS
Kumar, Manish (author) / Samui, Pijush (author)
2018
Article (Journal)
English
Reliability Analysis of Pile Foundation Using ELM and MARS
| Online Contents | 2018
Reliability Analysis of Pile Foundation Using GMDH, GP and MARS
| TIBKAT | 2022
Reliability Analysis of Pile Foundation Using GMDH, GP and MARS
| Springer Verlag | 2021