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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10505 - Geology

Projekt

<a href="/cs/project/GC21-35764J" target="_blank" >GC21-35764J: Experimentální a numerické studium sdruženého termo-hydro-mechanického chování jílu s důrazem na cyklické zatěžování</a><br>

Návaznosti

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

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Case Studies in Construction Materials

ISSN

2214-5095

e-ISSN

2214-5095

Svazek periodika

20

Číslo periodika v rámci svazku

July

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

17

Strana od-do

e02800

Kód UT WoS článku

001147759200001

EID výsledku v databázi Scopus

2-s2.0-85181000659