Machine learning approaches for prediction of the bearing capacity of ring foundations on rock masses

Kumar, Divesh Ranjan; Samui, Pijush; Wipulanusat, Warit; Keawsawasvong, Suraparb; Sangjinda, Kongtawan; Jitchaijaroen, Wittaya

doi:10.1007/s12145-023-01152-y

Cited by 7 publications

(1 citation statement)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The significant benefit of the FELA is that it is no need to assume any failure mechanism prior to the analysis (Sloan 2013). The FELA solution has attracted the attention of many geotechnical researchers in recent years (e.g., Ke et al 2020;Shafiee and Eskandarinejad 2022;Lai et al 2023;Kumar et al 2023). The mesh features involved 10000 elements with 1000 start elements and five adaptive iterations.…”

Section: Problem Definition and Fela Modelingmentioning

confidence: 99%

Stability of Subsea Tunnels Using Finite Element Limit Analysis and Adaptive Neuro-Fuzzy Inference System

Shafiee,

Oulapour,

Abdlkadhim

2024

Preprint

View full text Add to dashboard Cite

Construction of subsea tunnels has grown significantly in recent decades around the world. Hence, ensuring the stability of these tunnels is very important. However, investigating the stability of subsea tunnels has not received enough attention. In the present study, the finite element limit analysis (FELA) was used to determine the internal pressure needed to maintain the stability of circular subsea tunnels embedded in a Tresca material by considering various values for the influential parameters. It was observed that the required internal pressure, in a dimensionless form, increased by increasing soil cover and water depth. The failure mechanism was also investigated. It was observed that the mechanism did not cover the tunnel’s roof for lower values of soil cover and soil’s undrained shear strength. An adaptive neuro-fuzzy inference system (ANFIS) was implemented to predict the required dimensionless internal pressure. It was seen that in this regard, the performance of ANFIS was far better than the multiple linear regression (MLR).

show abstract