Universidad Nacional Autónoma de Tayacaja Daniel Hernández Morillo - UNAT

Technological tools for failure assessment in the flexible pavement surface, a systematic review
PDF (Español (España))
HTML (Español (España))

Keywords

Technological tools
evaluation
failures
pavements

How to Cite

Cárdenas Resines , C. L., Carrillo Sinche, J. L., Izarra Vargas, A. D., Murga Tirado, C. E., & Vásquez Salazar, A. G. (2023). Technological tools for failure assessment in the flexible pavement surface, a systematic review. Llamkasun, 4(2), 10–23. https://doi.org/10.47797/llamkasun.v4i2.121

Abstract

Throughout the years in which a pavement is in service, it is exposed to climatic actions and traffic that wear out its useful life, as well as its quality of mechanical characteristics, as well as functional, causing various failures and types of failures to exist. clothing on flexible pavements; Therefore, using technological option tools has become essential for their respective evaluations. Aim. Perform an analysis of existing failure assessment technology tools in flexible pavement surfaces. Type of research. The research presents a qualitative approach, which is supported by obtaining reference information on technological tools for evaluating failures in flexible pavements. Method. It was carried out through bibliographic reviews of scientific articles; Therefore, for this study, the use of databases such as: REDALYC, SCIENCEDIRECT, DIALNET, SCIELO; whose applied criteria were in terms of temporality the last 16 years (2007 - 2023), working with a total of 31 original scientific articles, to later carry out the collection of information and finally the analyzes related to our objectives. Conclusion. Terrestrial photogrammetry and neural networks is the best technological option for the evaluation of failures in the surface condition of flexible pavements; since it contributes to the reduction of time and cost, being the most efficient of the other technological tools.

https://doi.org/10.47797/llamkasun.v4i2.121
PDF (Español (España))
HTML (Español (España))

References

Fernando-Branco, Luís-Picado, Paulo-Pereira. Pavimentos Rodoviários [en línea] .Brasil: Edições Almedina , 2011. ISBN: 9789724026480 . [Consulta: 22 de abril de 2023]. https://www.almedina.net/pavimentos-rodovi-rios-1563797318.html

Manuel-Minhoto .Consideração da temperatura no comportamento à reflexão de fendas dos reforços de pavimentos rodoviários flexíveis . Tesis Doctoral. Instituto Politécnico de Braganca [online]. 2005. [fecha de Consulta 22 de Abril de 2023]. Disponible en: http://repositorium.sdum.uminho.pt/handle/1822/6751

Juan-Orozco, Rodolfo-Téllez, Ricardo-Solorio, n: https://www.uc.pt/bguc/LigacoesUteis/Teses

M. Á. Morillo Romero, Digitalización 3D con escáner de luz estructurada aplicada al área de la gestión de calidad y la conservación del patrimonio histórico artístico, tesis ba, Departamento de Física Aplicada de la Escuela Técnica Superior de Ingeniería de la Universidad de Sevilla, Sevilla, 2015.

H. Xing-Fei y O. Nixon, "Time Delay Integration Speeds Up Imaging", Jour. Phot. Spect., vol. 46, n.° 5, p. 50, may. 2012.

Y. Pan, X. Zhang, G. Cervone y L. Yang, "Detection of asphalt pavement potholes and cracks based on the unmanned aerial vehicle multispectral imagery", ieee Jour. of Select. Top. in Appl. Earth Observ. and Remote Sens., vol. 11, n.° 10, pp. 3701-3712, 2018. doi: https://doi.org/10.1109/JSTARS.2018.2865528

S. Mokhtari, Analytical Study of Computer Vision-Based Pavement Crack Quantification Using Machine Learning Techniques, tesis Ph. D., Departamento de ingeniería civil, ambiental y de construcción, College of Engineering and Computer Science, University of Central Florida, Orlando, Florida, 2015.

Y. Turkan, J. Hong, S. Laflamme y N. Puri, "Adaptive wavelet neural network for terrestrial laser scanner-based crack detection", Automat. in Const., vol. 94, pp. 191-202, 2018. doi: https://doi.org/10.1016/j. autcon.2018.06.017

N.-D. Hoang, "An artificial intelligence method for asphalt pavement pothole detection using least squares support vector machine and detection", Adv. in Civ. Eng., vol. 2018, pp. 1-12, 2018. doi: https://doi. org/10.1155/2018/7419058

F. M. Nejad y H. Zakeri, "An optimum feature extraction method based on Wavelet-Radon transform and dynamic neural network for pavement distress classification", Expert Syst. with Applic., vol. 38, n.° 8, pp. 9442-9460, 2011. doi: https://doi.org/10.1016/j. eswa.2011.01.089

N. Shatnawi, "Automatic pavement cracks detection using image processing techniques and neural network", Internat. Jour. of Adv. Comp. Sci. and Applic., vol. 9, n.° 9, pp. 399-402, 2018. doi: https://doi. org/10.14569/IJACSA.2018.0909508015

L. Tello-Cifuentes, M. Aguirre-Sánchez, J. P. Díaz-Paz, y F. . Hernández, «Evaluación de daños en pavimento flexible usando fotogrametría terrestre y redes neuronales», TecnoL., vol. 24, n.º 50, p. e1686, ene. 2021.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright (c) 2023 Claudia Luz Cárdenas Resines , Jerold Luis Carrillo Sinche, Angela Dayana Izarra Vargas, Christian Edinson Murga Tirado, Anais Gabriela Vásquez Salazar

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...