Prediction of undrained shear strength based on KMR model

The accurate prediction of undrained shear strength is of significant importance in areas such as slope stability, earthquake resistance, and pile foundation design. Therefore, enhancing the accuracy of undrained shear strength prediction is crucial. The research results indicate that employing the KMR model can improve the computational accuracy by 4.4% to 18.9%. Furthermore, compared to conventional empirical formulas, the KMR model evidently processes data more rapidly and can predict relevant parameters more accurately. This method provides a new research idea to some extent for addressing the issue of low prediction accuracy of traditional machine learning models for geotechnical parameters.

Authors

Publication Details

Journal
Proceedings of the Romanian Academy Series A Mathematics Physics Technical Sciences Information Science
Published
2024-11-29
DOI
https://doi.org/10.59277/pra-ser.a.25.4.08
Primary Topic
Educational Technology and Assessment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Prediction of undrained shear strength based on KMR model

Bin Luo, Pei Li, Mingyue Chen, Chenghang Zhang
Proceedings of the Romanian Academy Series A Mathematics Physics Technical Sciences Information Science
Educational Technology and Assessment
article

Prediction of undrained shear strength based on KMR model

Bin Luo, Pei Li, Mingyue Chen, Chenghang Zhang
article en

Abstract

The accurate prediction of undrained shear strength is of significant importance in areas such as slope stability, earthquake resistance, and pile foundation design. Therefore, enhancing the accuracy of undrained shear strength prediction is crucial. The research results indicate that employing the KMR model can improve the computational accuracy by 4.4% to 18.9%. Furthermore, compared to conventional empirical formulas, the KMR model evidently processes data more rapidly and can predict relevant parameters more accurately. This method provides a new research idea to some extent for addressing the issue of low prediction accuracy of traditional machine learning models for geotechnical parameters.

Proceedings of the Romanian Academy Series A Mathematics Physics Technical Sciences Information ScienceVol. 25(4)
Sustainable cities and communities
Openalex Percentile: Top 66%
Educational Technology and Assessment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.