Artificial Neural Networks for Predicting Asphalt Fatigue Life: Investigating Material and Loading Parameters with a Comprehensive Dataset and Addressing Model Intricacies

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F25%3A00384945" target="_blank" >RIV/68407700:21110/25:00384945 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-94600-4_5" target="_blank" >https://doi.org/10.1007/978-3-031-94600-4_5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-94600-4_5" target="_blank" >10.1007/978-3-031-94600-4_5</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Artificial Neural Networks for Predicting Asphalt Fatigue Life: Investigating Material and Loading Parameters with a Comprehensive Dataset and Addressing Model Intricacies

Popis výsledku v původním jazyce

This study employs artificial neural networks (ANNs) to pre-dict the fatigue life of asphalt concrete (AC), crucial for road main-tenance and longevity. Leveraging a dataset from extensive laboratory tests, we optimized ANN models to address the variability in AC fatigue data. Our approach involved fine-tuning hyperparameters and adapt-ing network architectures to best utilize a dataset of 152 samples. The models were trained with both linear and logarithmic loss functions. Results showed that modified bituminous binders significantly improve fatigue life predictions, with comprehensive input parameters being vital for accurate modeling. Although models achieved moderate overall R^2R2 scores of about 0.4, they highlighted the significant impact of binder type and content on prediction outcomes. The research demonstrates the potential of machine learning to enhance pavement engineering by pro-viding deeper insights into AC fatigue life. Optimized models and codes are available in an open repository, encouraging further exploration and application.

Název v anglickém jazyce

Artificial Neural Networks for Predicting Asphalt Fatigue Life: Investigating Material and Loading Parameters with a Comprehensive Dataset and Addressing Model Intricacies

Popis výsledku anglicky

This study employs artificial neural networks (ANNs) to pre-dict the fatigue life of asphalt concrete (AC), crucial for road main-tenance and longevity. Leveraging a dataset from extensive laboratory tests, we optimized ANN models to address the variability in AC fatigue data. Our approach involved fine-tuning hyperparameters and adapt-ing network architectures to best utilize a dataset of 152 samples. The models were trained with both linear and logarithmic loss functions. Results showed that modified bituminous binders significantly improve fatigue life predictions, with comprehensive input parameters being vital for accurate modeling. Although models achieved moderate overall R^2R2 scores of about 0.4, they highlighted the significant impact of binder type and content on prediction outcomes. The research demonstrates the potential of machine learning to enhance pavement engineering by pro-viding deeper insights into AC fatigue life. Optimized models and codes are available in an open repository, encouraging further exploration and application.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20501 - Materials engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

Futuristic Computational Systems and Advanced Engineering for the Society, Proceedings of the 6th International Conference on Artificial Intelligence and Applied Mathematics in Engineering ICAIAME 2024, Volume 2

ISBN

9783031946004

ISSN

2731-5010

e-ISSN

—

Počet stran výsledku

16

Strana od-do

62-77

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Basel

Místo konání akce

Warsaw

Datum konání akce

26. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—