Prediction of California bearing ratio and modified proctor parameters using deep neural networks and multiple linear regression: A case study of granular soils

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F24%3A10480389" target="_blank" >RIV/00216208:11310/24:10480389 - isvavai.cz</a>

Result on the web

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=dTXb14krTm" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=dTXb14krTm</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cscm.2023.e02800" target="_blank" >10.1016/j.cscm.2023.e02800</a>

Result language

angličtina

Original language name

Prediction of California bearing ratio and modified proctor parameters using deep neural networks and multiple linear regression: A case study of granular soils

Original language description

The California Bearing Ratio (CBR) and modified proctor parameters belong to the soil geotechnical properties used to assess soil behavior. Direct measurement of these properties can be quite time-consuming in large-scale applications or when immediate results are required. Therefore, significant research efforts have been made in the literature to develop indirect methods for their estimation. However, some gaps in the state-of-the-art can be highlighted in these topics, such as the deficiency in computational models to calculate the maximum dry unit weight (γd(max)), optimum moisture content (ω(opt)) and CBR, and the lack of methods that consider their intrinsic influence on each other. Hence, in this investigation, mathematical and computational models were created to obtain the above-mentioned variables from the soil grain size distribution. The mathematical model was based on Multiple Linear Regression (MLR) correlations. Meanwhile, the computational model was constructed from a custom-made Deep Neural Networks (DNNs) architecture. Subsequently, the accuracy of these models was validated with an experimental case study. The results demonstrated that the proposed methods in this study are more precise than previous approaches in the literature. Accordingly, the main contribution of this manuscript to the industry is the formation of models with high exactness to predict the γd(max), ω(opt) and CBR of granular soils.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10505 - Geology

Project

<a href="/en/project/GC21-35764J" target="_blank" >GC21-35764J: Experimental and numerical investigation of coupled thermo-hydro-mechanical behaviour of clay with focus to cyclic processes</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Case Studies in Construction Materials

ISSN

2214-5095

e-ISSN

2214-5095

Volume of the periodical

20

Issue of the periodical within the volume

July

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

17

Pages from-to

e02800

UT code for WoS article

001147759200001

EID of the result in the Scopus database

2-s2.0-85181000659