Bituminous Mixtures Experimental Data Modeling Using a Hyperparameters-Optimized Machine Learning Approach
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F21%3A00353226" target="_blank" >RIV/68407700:21110/21:00353226 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.3390/app112411710" target="_blank" >https://doi.org/10.3390/app112411710</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/app112411710" target="_blank" >10.3390/app112411710</a>
Alternative languages
Result language
angličtina
Original language name
Bituminous Mixtures Experimental Data Modeling Using a Hyperparameters-Optimized Machine Learning Approach
Original language description
This study introduces a machine learning approach based on Artificial Neural Networks (ANNs) for the prediction of Marshall test results, stiffness modulus and air voids data of different bituminous mixtures for road pavements. A novel approach for an objective and semi-automatic identification of the optimal ANN’s structure, defined by the so-called hyperparameters, has been introduced and discussed. Mechanical and volumetric data were obtained by conducting laboratory tests on 320 Marshall specimens, and the results were used to train the neural network. The k-fold Cross Validation method has been used for partitioning the available data set, to obtain an unbiased evaluation of the model predictive error. The ANN’s hyperparameters have been optimized using the Bayesian optimization, that overcame efficiently the more costly trial-and-error procedure and automated the hyperparameters tuning. The proposed ANN model is characterized by a Pearson coefficient value of 0.868.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20101 - Civil engineering
Result continuities
Project
<a href="/en/project/TE01020168" target="_blank" >TE01020168: Centre for Effective and Sustainable Transport Infrastructure (CESTI)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Applied Sciences
ISSN
2076-3417
e-ISSN
2076-3417
Volume of the periodical
11
Issue of the periodical within the volume
24
Country of publishing house
CH - SWITZERLAND
Number of pages
20
Pages from-to
1-20
UT code for WoS article
000735390400001
EID of the result in the Scopus database
2-s2.0-85120988268