Bayesian Regression in Pavement Deterioration Modeling: Revisiting the AASHO Road Test Rut Depth Model

Traditional pavement deterioration modeling is normally based on historical condition data alone without incorporating the fundamental relationships between the causal factors and the response. Also, typical approaches do not quantify the uncertainty of the predicted response. This paper uses Bayesi...

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Detalles Bibliográficos
Autores principales:	Amador Jiménez, Luis, Mrawira, Donath
Formato:	Online
Idioma:	spa
Publicado:	UCR 2012
Acceso en línea:	https://revistas.ucr.ac.cr/index.php/vial/article/view/3926

id	VIAL3926
record_format	ojs
spelling	VIAL39262021-01-29T01:58:56Z Bayesian Regression in Pavement Deterioration Modeling: Revisiting the AASHO Road Test Rut Depth Model Amador Jiménez, Luis Mrawira, Donath Pavement performance deterioration Bayesian Traditional pavement deterioration modeling is normally based on historical condition data alone without incorporating the fundamental relationships between the causal factors and the response. Also, typical approaches do not quantify the uncertainty of the predicted response. This paper uses Bayesian regression for pavement deterioration modeling. This method is applied to an existing model for the prediction of rut depth progression from the AASHO Road Test. A classical regression model developed elsewhere is herein summarized and its results are then compared with those from the Bayesian regression in order to validate. A second model based on the entire dataset of the AASHO road test is used to demonstrate the advantages of such approach. The models are capable of employing expert criteria combined with historical knowledge and current observations in order estimate posterior probabilistic distributions for the regression coefficients of the mechanistic equation. The predictive model calibrated to local conditions is able to forecast within pre-specified confidence intervals the range of values for the expected deterioration. Bayesian regression modeling produces more reliable predictions for deterioration performance, which in turn, can be used to improve decision-making on road management systems UCR 2012-11-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Article application/pdf https://revistas.ucr.ac.cr/index.php/vial/article/view/3926 10.15517/iv.v14i25.3926 Infraestructura Vial; Vol. 14 No. 25 (2012): Journal 25; 28-35 Infraestructura Vial; Vol. 14 Núm. 25 (2012): Revista 25; 28-35 Infraestructura Vial; Vol. 14 N.º 25 (2012): Revista 25; 28-35 2215-3705 1409-4045 spa https://revistas.ucr.ac.cr/index.php/vial/article/view/3926/3798 Derechos de autor 2012 Jiménez, Mrawira
institution	Universidad de Costa Rica
collection	Infraestructura Vial
language	spa
format	Online
author	Amador Jiménez, Luis Mrawira, Donath
spellingShingle	Amador Jiménez, Luis Mrawira, Donath Bayesian Regression in Pavement Deterioration Modeling: Revisiting the AASHO Road Test Rut Depth Model
author_facet	Amador Jiménez, Luis Mrawira, Donath
author_sort	Amador Jiménez, Luis
description	Traditional pavement deterioration modeling is normally based on historical condition data alone without incorporating the fundamental relationships between the causal factors and the response. Also, typical approaches do not quantify the uncertainty of the predicted response. This paper uses Bayesian regression for pavement deterioration modeling. This method is applied to an existing model for the prediction of rut depth progression from the AASHO Road Test. A classical regression model developed elsewhere is herein summarized and its results are then compared with those from the Bayesian regression in order to validate. A second model based on the entire dataset of the AASHO road test is used to demonstrate the advantages of such approach. The models are capable of employing expert criteria combined with historical knowledge and current observations in order estimate posterior probabilistic distributions for the regression coefficients of the mechanistic equation. The predictive model calibrated to local conditions is able to forecast within pre-specified confidence intervals the range of values for the expected deterioration. Bayesian regression modeling produces more reliable predictions for deterioration performance, which in turn, can be used to improve decision-making on road management systems
title	Bayesian Regression in Pavement Deterioration Modeling: Revisiting the AASHO Road Test Rut Depth Model
title_short	Bayesian Regression in Pavement Deterioration Modeling: Revisiting the AASHO Road Test Rut Depth Model
title_full	Bayesian Regression in Pavement Deterioration Modeling: Revisiting the AASHO Road Test Rut Depth Model
title_fullStr	Bayesian Regression in Pavement Deterioration Modeling: Revisiting the AASHO Road Test Rut Depth Model
title_full_unstemmed	Bayesian Regression in Pavement Deterioration Modeling: Revisiting the AASHO Road Test Rut Depth Model
title_sort	bayesian regression in pavement deterioration modeling: revisiting the aasho road test rut depth model
publisher	UCR
publishDate	2012
url	https://revistas.ucr.ac.cr/index.php/vial/article/view/3926
work_keys_str_mv	AT amadorjimenezluis bayesianregressioninpavementdeteriorationmodelingrevisitingtheaashoroadtestrutdepthmodel AT mrawiradonath bayesianregressioninpavementdeteriorationmodelingrevisitingtheaashoroadtestrutdepthmodel
_version_	1810112256457834496

Bayesian Regression in Pavement Deterioration Modeling: Revisiting the AASHO Road Test Rut Depth Model

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