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Perpetual pavement temperature prediction model
Structural capacities of flexible pavements are determined from surface deflection measurements. These deflections must be corrected to a standard load and/or a reference pavement temperature. A number of models are available to predict pavement temperature, but they may not be applicable to perpetu...
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Published in: | Road materials and pavement design 2014-01, Vol.15 (1), p.55-65 |
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container_title | Road materials and pavement design |
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creator | Gedafa, Daba S. Hossain, Mustaque Romanoschi, Stefan A. |
description | Structural capacities of flexible pavements are determined from surface deflection measurements. These deflections must be corrected to a standard load and/or a reference pavement temperature. A number of models are available to predict pavement temperature, but they may not be applicable to perpetual (thicker) asphalt pavements. Mid-depth pavement temperature was measured in six sessions on four perpetual pavement sections in Kansas. Data from five sessions were used to develop the prediction model based on four independent variables. Data from the last session were used to validate it. Predicted mid-depth pavement temperatures from the new model and three other models were compared with the measured mid-depth pavement temperature. Sensitivity of the model to changes in all independent variables was also investigated. The effect of mid-depth pavement temperature on the centre deflection of the falling weight deflectometer was also studied. The prediction model developed in this study yields mid-depth pavement temperature that is closest to the measured mid-depth temperature. It also results in lowest bias in terms of centre deflection. Predicted mid-depth pavement temperature is most sensitive to the time of day when measurements are made and least sensitive to the layer mid-depth thickness. |
doi_str_mv | 10.1080/14680629.2013.852610 |
format | article |
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These deflections must be corrected to a standard load and/or a reference pavement temperature. A number of models are available to predict pavement temperature, but they may not be applicable to perpetual (thicker) asphalt pavements. Mid-depth pavement temperature was measured in six sessions on four perpetual pavement sections in Kansas. Data from five sessions were used to develop the prediction model based on four independent variables. Data from the last session were used to validate it. Predicted mid-depth pavement temperatures from the new model and three other models were compared with the measured mid-depth pavement temperature. Sensitivity of the model to changes in all independent variables was also investigated. The effect of mid-depth pavement temperature on the centre deflection of the falling weight deflectometer was also studied. The prediction model developed in this study yields mid-depth pavement temperature that is closest to the measured mid-depth temperature. It also results in lowest bias in terms of centre deflection. Predicted mid-depth pavement temperature is most sensitive to the time of day when measurements are made and least sensitive to the layer mid-depth thickness.</description><identifier>ISSN: 1468-0629</identifier><identifier>EISSN: 2164-7402</identifier><identifier>DOI: 10.1080/14680629.2013.852610</identifier><language>eng</language><publisher>Paris: Taylor & Francis</publisher><subject>Applied sciences ; Asphalt pavements ; Bias ; Buildings. Public works ; Computation methods. Tables. Charts ; Design ; Exact sciences and technology ; pavement deflection ; pavement temperature ; perpetual pavement ; prediction model ; Road construction. Pavements. Maintenance ; Road test: methods, equipments and results ; Roads & highways ; Structural analysis. Stresses ; Studies ; Surfacing ; Temperature ; Transportation infrastructure</subject><ispartof>Road materials and pavement design, 2014-01, Vol.15 (1), p.55-65</ispartof><rights>2013 Taylor & Francis 2013</rights><rights>2015 INIST-CNRS</rights><rights>Copyright Taylor & Francis Ltd. 2014</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c393t-45269dfb32e7a345afba0d22fe00df32dd7e4f2a18bb583b5367fc21425fba043</citedby><cites>FETCH-LOGICAL-c393t-45269dfb32e7a345afba0d22fe00df32dd7e4f2a18bb583b5367fc21425fba043</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28163068$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Gedafa, Daba S.</creatorcontrib><creatorcontrib>Hossain, Mustaque</creatorcontrib><creatorcontrib>Romanoschi, Stefan A.</creatorcontrib><title>Perpetual pavement temperature prediction model</title><title>Road materials and pavement design</title><description>Structural capacities of flexible pavements are determined from surface deflection measurements. These deflections must be corrected to a standard load and/or a reference pavement temperature. A number of models are available to predict pavement temperature, but they may not be applicable to perpetual (thicker) asphalt pavements. Mid-depth pavement temperature was measured in six sessions on four perpetual pavement sections in Kansas. Data from five sessions were used to develop the prediction model based on four independent variables. Data from the last session were used to validate it. Predicted mid-depth pavement temperatures from the new model and three other models were compared with the measured mid-depth pavement temperature. Sensitivity of the model to changes in all independent variables was also investigated. The effect of mid-depth pavement temperature on the centre deflection of the falling weight deflectometer was also studied. The prediction model developed in this study yields mid-depth pavement temperature that is closest to the measured mid-depth temperature. It also results in lowest bias in terms of centre deflection. Predicted mid-depth pavement temperature is most sensitive to the time of day when measurements are made and least sensitive to the layer mid-depth thickness.</description><subject>Applied sciences</subject><subject>Asphalt pavements</subject><subject>Bias</subject><subject>Buildings. Public works</subject><subject>Computation methods. Tables. Charts</subject><subject>Design</subject><subject>Exact sciences and technology</subject><subject>pavement deflection</subject><subject>pavement temperature</subject><subject>perpetual pavement</subject><subject>prediction model</subject><subject>Road construction. Pavements. Maintenance</subject><subject>Road test: methods, equipments and results</subject><subject>Roads & highways</subject><subject>Structural analysis. Stresses</subject><subject>Studies</subject><subject>Surfacing</subject><subject>Temperature</subject><subject>Transportation infrastructure</subject><issn>1468-0629</issn><issn>2164-7402</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LxDAQhoMouOj-Aw8F8djdZJKm3ZPI4hcs6EHPIW0m0KVtapIq--9t6a5H5zKX531neAi5YXTFaEHXTMiCStisgDK-KjKQjJ6RBTAp0lxQOCeLCUkn5pIsQ9jTcThnMmMLsn5H32McdJP0-htb7GISse3R6zh4THqPpq5i7bqkdQaba3JhdRNwedxX5PPp8WP7ku7enl-3D7u04hseUzG-sTG25IC55iLTttTUAFik1FgOxuQoLGhWlGVW8DLjMrcVMAHZRAp-RW7n3t67rwFDVHs3-G48qZhkIDYZCDZSYqYq70LwaFXv61b7g2JUTXbUyY6a7KjZzhi7O5brUOnGet1VdfjLQsEkp7IYufuZqzvrfKt_nG-MivrQOH8K8X8v_QIyRXdD</recordid><startdate>20140102</startdate><enddate>20140102</enddate><creator>Gedafa, Daba S.</creator><creator>Hossain, Mustaque</creator><creator>Romanoschi, Stefan A.</creator><general>Taylor & Francis</general><general>Lavoisier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7XB</scope><scope>88I</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>KB.</scope><scope>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>PDBOC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope></search><sort><creationdate>20140102</creationdate><title>Perpetual pavement temperature prediction model</title><author>Gedafa, Daba S. ; Hossain, Mustaque ; Romanoschi, Stefan A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c393t-45269dfb32e7a345afba0d22fe00df32dd7e4f2a18bb583b5367fc21425fba043</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Applied sciences</topic><topic>Asphalt pavements</topic><topic>Bias</topic><topic>Buildings. Public works</topic><topic>Computation methods. Tables. Charts</topic><topic>Design</topic><topic>Exact sciences and technology</topic><topic>pavement deflection</topic><topic>pavement temperature</topic><topic>perpetual pavement</topic><topic>prediction model</topic><topic>Road construction. Pavements. Maintenance</topic><topic>Road test: methods, equipments and results</topic><topic>Roads & highways</topic><topic>Structural analysis. Stresses</topic><topic>Studies</topic><topic>Surfacing</topic><topic>Temperature</topic><topic>Transportation infrastructure</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gedafa, Daba S.</creatorcontrib><creatorcontrib>Hossain, Mustaque</creatorcontrib><creatorcontrib>Romanoschi, Stefan A.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Materials Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Materials science collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering collection</collection><collection>ProQuest Central Basic</collection><jtitle>Road materials and pavement design</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gedafa, Daba S.</au><au>Hossain, Mustaque</au><au>Romanoschi, Stefan A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Perpetual pavement temperature prediction model</atitle><jtitle>Road materials and pavement design</jtitle><date>2014-01-02</date><risdate>2014</risdate><volume>15</volume><issue>1</issue><spage>55</spage><epage>65</epage><pages>55-65</pages><issn>1468-0629</issn><eissn>2164-7402</eissn><abstract>Structural capacities of flexible pavements are determined from surface deflection measurements. These deflections must be corrected to a standard load and/or a reference pavement temperature. A number of models are available to predict pavement temperature, but they may not be applicable to perpetual (thicker) asphalt pavements. Mid-depth pavement temperature was measured in six sessions on four perpetual pavement sections in Kansas. Data from five sessions were used to develop the prediction model based on four independent variables. Data from the last session were used to validate it. Predicted mid-depth pavement temperatures from the new model and three other models were compared with the measured mid-depth pavement temperature. Sensitivity of the model to changes in all independent variables was also investigated. The effect of mid-depth pavement temperature on the centre deflection of the falling weight deflectometer was also studied. The prediction model developed in this study yields mid-depth pavement temperature that is closest to the measured mid-depth temperature. It also results in lowest bias in terms of centre deflection. Predicted mid-depth pavement temperature is most sensitive to the time of day when measurements are made and least sensitive to the layer mid-depth thickness.</abstract><cop>Paris</cop><pub>Taylor & Francis</pub><doi>10.1080/14680629.2013.852610</doi><tpages>11</tpages></addata></record> |
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subjects | Applied sciences Asphalt pavements Bias Buildings. Public works Computation methods. Tables. Charts Design Exact sciences and technology pavement deflection pavement temperature perpetual pavement prediction model Road construction. Pavements. Maintenance Road test: methods, equipments and results Roads & highways Structural analysis. Stresses Studies Surfacing Temperature Transportation infrastructure |
title | Perpetual pavement temperature prediction model |
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