Prediction of Multi-layered Pavement Moduli Based on Falling Weight Deflectometer Test Using Soft Computing Approaches

The application of supervised machine learning algorithms to provide solutions for various civil engineering problems is an emerging trend. This paper presents the utilization of artificial neural network (ANN) and random forest regression (RFR) for the prediction of the elastic moduli of multi-laye...

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Vydané v:	Transportation infrastructure geotechnology Ročník 11; číslo 4; s. 2348 - 2381
Hlavní autori:	Phulsawat, Barami, Senjuntichai, Angsumalin, Senjuntichai, Teerapong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.08.2024 Springer Nature B.V
Predmet:	Accuracy Algorithms Artificial neural networks Building Materials Civil engineering Datasets Deflectometers Elastic analysis Engineering Flexible pavements Foundations Geoengineering Geotechnical Engineering & Applied Earth Sciences Hydraulics Impact analysis Impact prediction Machine learning Matrix methods Modulus of elasticity Multilayers Neural networks Parameter sensitivity Performance evaluation Performance prediction Predictions Roads & highways Sensitivity analysis Soft computing Stiffness matrix Supervised learning Synthetic data Technical Paper Weight Pavement performance prediction Falling weight deflectometer Sensitivity analysis Multi-layered medium Artificial neural networks Random forest regression
ISSN:	2196-7202, 2196-7210
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