dc.contributor.author |
Walubita, LF
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|
dc.contributor.author |
Fuentes, F
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|
dc.contributor.author |
Faruk, NM
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|
dc.contributor.author |
Komba, Julius J
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dc.contributor.author |
Naik, B
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dc.contributor.author |
Prakoso, A
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dc.date.accessioned |
2020-10-12T07:32:18Z |
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dc.date.available |
2020-10-12T07:32:18Z |
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dc.date.issued |
2020-05 |
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dc.identifier.citation |
Walubita, L.F. 2020. Mechanistic-empirical compatible traffic data generation: Portable WIM versus cluster analysis. Journal of Testing and Evaluation, v48(3), pp 2377-2392. |
en_US |
dc.identifier.issn |
0090-3978 |
|
dc.identifier.issn |
1945-7553 |
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dc.identifier.uri |
https://www.astm.org/DIGITAL_LIBRARY/JOURNALS/TESTEVAL/PAGES/JTE20190745.htm
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|
dc.identifier.uri |
https://doi.org/10.1520/JTE20190745
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|
dc.identifier.uri |
http://hdl.handle.net/10204/11625
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|
dc.description |
Copyright: 2020 ASTM International. This is the preprint version of the work. Kindly visit the publisher's website for access to the fulltext version. |
en_US |
dc.description.abstract |
Axle load distribution factors (ALDFs) are used as one of the primary traffic data inputs for mechanistic-empirical (ME) pavement design methods for predicting the impact of varying traffic loads on pavement performance with a higher degree of accuracy than empirical methods that are solely based on equivalent single axle load (ESAL) concept. Ideally, to ensure optimal pavement structural design, site-specific traffic load spectra data—generated from weigh-in-motion (WIM) systems—should be used during the pavement design process. However, because of the limited number of available permanent WIM stations (in Texas, for example), it is not feasible to generate a statewide ALDFs database for each highway or project from permanent WIM data. In this study, two possible alternative methods, namely, the direct measurement using a portable WIM system and the cluster analysis technique, were explored for generating site-specific ME-compatible traffic data for a highway test section, namely, state highway (SH) 7 in Bryan District (Texas). The traffic data were then used for estimating pavement performance using a ME pavement design software, namely, the Texas Mechanistic-Empirical Thickness Design System (TxME). The TxME-predicted pavement performance (e.g., rutting) using the portable WIM-generated traffic input parameters closely matched with the actual field performance. Overall, the study findings indicated that the portable WIM (with proper installation and calibration) constitutes an effective means for rapidly collecting reliable site-specific ME-compatible traffic data. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
ASTM International |
en_US |
dc.relation.ispartofseries |
Worklist;23812 |
|
dc.subject |
Traffic load spectra |
en_US |
dc.subject |
Axle load distribution factors |
en_US |
dc.subject |
ALDFs |
en_US |
dc.subject |
Weigh-in-motion |
en_US |
dc.subject |
WIM |
en_US |
dc.subject |
Cluster analysis |
en_US |
dc.subject |
Mechanistic-empirical |
en_US |
dc.subject |
Pavement design |
en_US |
dc.title |
Mechanistic-empirical compatible traffic data generation: Portable WIM versus cluster analysis |
en_US |
dc.type |
Article |
en_US |
dc.identifier.apacitation |
Walubita, L., Fuentes, F., Faruk, N., Komba, J. J., Naik, B., & Prakoso, A. (2020). Mechanistic-empirical compatible traffic data generation: Portable WIM versus cluster analysis. http://hdl.handle.net/10204/11625 |
en_ZA |
dc.identifier.chicagocitation |
Walubita, LF, F Fuentes, NM Faruk, Julius J Komba, B Naik, and A Prakoso "Mechanistic-empirical compatible traffic data generation: Portable WIM versus cluster analysis." (2020) http://hdl.handle.net/10204/11625 |
en_ZA |
dc.identifier.vancouvercitation |
Walubita L, Fuentes F, Faruk N, Komba JJ, Naik B, Prakoso A. Mechanistic-empirical compatible traffic data generation: Portable WIM versus cluster analysis. 2020; http://hdl.handle.net/10204/11625. |
en_ZA |
dc.identifier.ris |
TY - Article
AU - Walubita, LF
AU - Fuentes, F
AU - Faruk, NM
AU - Komba, Julius J
AU - Naik, B
AU - Prakoso, A
AB - Axle load distribution factors (ALDFs) are used as one of the primary traffic data inputs for mechanistic-empirical (ME) pavement design methods for predicting the impact of varying traffic loads on pavement performance with a higher degree of accuracy than empirical methods that are solely based on equivalent single axle load (ESAL) concept. Ideally, to ensure optimal pavement structural design, site-specific traffic load spectra data—generated from weigh-in-motion (WIM) systems—should be used during the pavement design process. However, because of the limited number of available permanent WIM stations (in Texas, for example), it is not feasible to generate a statewide ALDFs database for each highway or project from permanent WIM data. In this study, two possible alternative methods, namely, the direct measurement using a portable WIM system and the cluster analysis technique, were explored for generating site-specific ME-compatible traffic data for a highway test section, namely, state highway (SH) 7 in Bryan District (Texas). The traffic data were then used for estimating pavement performance using a ME pavement design software, namely, the Texas Mechanistic-Empirical Thickness Design System (TxME). The TxME-predicted pavement performance (e.g., rutting) using the portable WIM-generated traffic input parameters closely matched with the actual field performance. Overall, the study findings indicated that the portable WIM (with proper installation and calibration) constitutes an effective means for rapidly collecting reliable site-specific ME-compatible traffic data.
DA - 2020-05
DB - ResearchSpace
DP - CSIR
KW - Traffic load spectra
KW - Axle load distribution factors
KW - ALDFs
KW - Weigh-in-motion
KW - WIM
KW - Cluster analysis
KW - Mechanistic-empirical
KW - Pavement design
LK - https://researchspace.csir.co.za
PY - 2020
SM - 0090-3978
SM - 1945-7553
T1 - Mechanistic-empirical compatible traffic data generation: Portable WIM versus cluster analysis
TI - Mechanistic-empirical compatible traffic data generation: Portable WIM versus cluster analysis
UR - http://hdl.handle.net/10204/11625
ER -
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en_ZA |