2021
DOI: 10.14295/transportes.v29i2.2401
|View full text |Cite
|
Sign up to set email alerts
|

A prediction model of the coefficient of friction for runway using artificial neural network

Abstract: As condições superficiais de uma pista de pouso e decolagem (PPD) são fundamentais para a garantia da segurança das operações das aeronaves que a utilizam. Nesse sentido, operadores de aeródromos devem manter atenção especial ao coeficiente de atrito e à macrotextura, para que possam promover uma PPD segura, planejar estratégias de manutenção e reabilitação em momentos oportunos, à medida que esses parâmetros se deterioram. Dessa forma, com o intuito de auxiliar operadores de aeródromo e a agência reguladora n… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2023
2023
2023
2023

Publication Types

Select...
1

Relationship

1
0

Authors

Journals

citations
Cited by 1 publication
(2 citation statements)
references
References 17 publications
0
2
0
Order By: Relevance
“…When approaching the use of ANN in Transportation Engineering, several authors used this technique to predict parameters related to the condition of tire-pavement adherence, as well as to indicate the need or not for maintenance on the highways and runways (Flintsch et al, 1996;Fwa et al, 1997;Bosurgi and Trifirò, 2005;Thube, 2012;Domitrović et al, 2018;Najafi et al, 2019;Hossain et al, 2019;Ribeiro et al, 2018;Yao et al, 2019;Quariguasi et al, 2021).…”
Section: Artificial Neural Networkmentioning
confidence: 99%
See 1 more Smart Citation
“…When approaching the use of ANN in Transportation Engineering, several authors used this technique to predict parameters related to the condition of tire-pavement adherence, as well as to indicate the need or not for maintenance on the highways and runways (Flintsch et al, 1996;Fwa et al, 1997;Bosurgi and Trifirò, 2005;Thube, 2012;Domitrović et al, 2018;Najafi et al, 2019;Hossain et al, 2019;Ribeiro et al, 2018;Yao et al, 2019;Quariguasi et al, 2021).…”
Section: Artificial Neural Networkmentioning
confidence: 99%
“…Variables with precedent in past models applied to highways and airports were used, namely: periodic maintenance (rubber removal), coating age, ambient temperature, relative air humidity and traffic (Fwa et al, 1997;Anupam et al, 2013;Santos et al, 2014;Oliveira, 2017;Susanna et al, 2017;Yao et al, 2019;Quariguasi et al, 2021). In addition, it is noteworthy that, in this work, the existence or not of grooving in the RWY was considered, and that data from the friction coefficient measured by different equipment were used.…”
Section: Transportes | Issn: 2237-1346mentioning
confidence: 99%