Implementation of deep neural networks and statistical methods to predict the resilient modulus of soils

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F23%3A10476937" target="_blank" >RIV/00216208:11310/23:10476937 - isvavai.cz</a>

Result on the web

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

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/10298436.2023.2257852" target="_blank" >10.1080/10298436.2023.2257852</a>

Result language

angličtina

Original language name

Implementation of deep neural networks and statistical methods to predict the resilient modulus of soils

Original language description

The Resilient Modulus (Mr) is perhaps the most relevant and widely used parameter to characterise the soil behaviour under repetitive loading for pavement applications. Accordingly, it is a crucial parameter controlling the mechanistic-empirical pavement design. Nonetheless, determining the Mr by laboratory tests is not always possible due to the high consumption of time and financial resources. Thus, developing new indirect approaches for estimating the MR is necessary. Precisely, this article investigates the application of Deep Neural Networks (DNNs) and statistical methods to predict the Mr of soils. For that purpose, the Long-Term Pavement Performance (LTPP) database was implemented. It includes 64 701 datasets resulting from coarse-grained and fine-grained soil samples considering a wide range of grain size distribution and subjected to different stress levels. The input parameters were the bulk stress, octahedral shear stress, and the percentage of soil particles passing through the different sieves (3&quot;, 2&quot;, 3/2&quot;, 1&quot;, 3/4&quot;, 1/2&quot;, 3/8&quot;, No. 4, No. 10, No. 40, No. 80, and No. 200) and the output was the Mr. The results suggest that while conventional mathematical models are unable to predict the influence of the grain size distribution and stress level on the Mr, the proposed DNNs were able to reproduce very accurate predictions. Notably, the proposed computational models have been uploaded to a GitHub repository and have become a valuable tool for forecasting the Mr when experimental measurements are not feasible.

Czech name

—

Czech description

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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

2023

Confidentiality

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

Name of the periodical

International Journal of Pavement Engineering

ISSN

1029-8436

e-ISSN

1477-268X

Volume of the periodical

24

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

19

Pages from-to

2257852

UT code for WoS article

001068220900001

EID of the result in the Scopus database

2-s2.0-85171858404