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    Mechanistic-Empirical IRI Model Accounting for Potential Bias

    Source: Journal of Transportation Engineering, Part A: Systems:;2011:;Volume ( 137 ):;issue: 005
    Author:
    José P. Aguiar-Moya
    ,
    Jorge A. Prozzi
    ,
    Andre de Fortier Smit
    DOI: 10.1061/(ASCE)TE.1943-5436.0000200
    Publisher: American Society of Civil Engineers
    Abstract: The roughness prediction models in the International Roughness Index (IRI) currently incorporated into the Mechanistic-Empirical Pavement Design Guide (M-E PDG) have been developed by means of ordinary least squares (OLS). However, some of the variables used in predicting future IRI were previously estimated by means of separate performance models. This could cause bias because of the correlation between the previously estimated distress types and unobserved components on the IRI model. The bias can be corrected by considering additional variables that are correlated with the distress types causing the bias, thus eliminating the correlation to the unobserved terms in the model. Bias in the IRI model can also be generated by unobserved factors not included in the model. If these factors are section-specific, the bias can be removed considering variations in the performance history of different pavement sections. The writers have used updated Long-Term Pavement Performance (LTPP) data consistent with the data set originally used to fit the M-E IRI model for flexible pavements over thick granular bases contained in the M-E PDG. The data were then used in modeling IRI by means of OLS and instrumental variable (IV) regressions analyzing the data as pooled and as a panel data set (by random-effects, fixed-effects, and joint random-effects approach) to check for possible bias in the model. It was found that the current IRI model, as estimated by OLS, exhibits several types of biases attributed to heterogeneity and incorrect assumptions in the modeling process. It was identified that the preferred IRI model was the joint random-effects approach and, therefore, the model parameters were estimated by correcting the omitted-variable bias and simultaneous-equation bias. Estimating the model by accounting for possible bias in the data indicated considerable changes in the effects of different parameters affecting IRI through time, mainly rutting of the pavement structure.
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      Mechanistic-Empirical IRI Model Accounting for Potential Bias

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    http://yetl.yabesh.ir/yetl1/handle/yetl/69200
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    • Journal of Transportation Engineering, Part A: Systems

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    contributor authorJosé P. Aguiar-Moya
    contributor authorJorge A. Prozzi
    contributor authorAndre de Fortier Smit
    date accessioned2017-05-08T22:01:50Z
    date available2017-05-08T22:01:50Z
    date copyrightMay 2011
    date issued2011
    identifier other%28asce%29te%2E1943-5436%2E0000244.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/69200
    description abstractThe roughness prediction models in the International Roughness Index (IRI) currently incorporated into the Mechanistic-Empirical Pavement Design Guide (M-E PDG) have been developed by means of ordinary least squares (OLS). However, some of the variables used in predicting future IRI were previously estimated by means of separate performance models. This could cause bias because of the correlation between the previously estimated distress types and unobserved components on the IRI model. The bias can be corrected by considering additional variables that are correlated with the distress types causing the bias, thus eliminating the correlation to the unobserved terms in the model. Bias in the IRI model can also be generated by unobserved factors not included in the model. If these factors are section-specific, the bias can be removed considering variations in the performance history of different pavement sections. The writers have used updated Long-Term Pavement Performance (LTPP) data consistent with the data set originally used to fit the M-E IRI model for flexible pavements over thick granular bases contained in the M-E PDG. The data were then used in modeling IRI by means of OLS and instrumental variable (IV) regressions analyzing the data as pooled and as a panel data set (by random-effects, fixed-effects, and joint random-effects approach) to check for possible bias in the model. It was found that the current IRI model, as estimated by OLS, exhibits several types of biases attributed to heterogeneity and incorrect assumptions in the modeling process. It was identified that the preferred IRI model was the joint random-effects approach and, therefore, the model parameters were estimated by correcting the omitted-variable bias and simultaneous-equation bias. Estimating the model by accounting for possible bias in the data indicated considerable changes in the effects of different parameters affecting IRI through time, mainly rutting of the pavement structure.
    publisherAmerican Society of Civil Engineers
    titleMechanistic-Empirical IRI Model Accounting for Potential Bias
    typeJournal Paper
    journal volume137
    journal issue5
    journal titleJournal of Transportation Engineering, Part A: Systems
    identifier doi10.1061/(ASCE)TE.1943-5436.0000200
    treeJournal of Transportation Engineering, Part A: Systems:;2011:;Volume ( 137 ):;issue: 005
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian