Comparison of Master Sigmoidal Curve and Markov Chain Techniques for Pavement Performance Prediction

Abstract Recently, highway agencies have become in need to enhance their pavement management systems (PMSs) using sound engineering and economic principles that find more prudent solutions to support wise investment choices and preserve the value of infrastructure assets. As pavement performance pre...
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Gespeichert in:

Autor*in:	El-Khawaga, Mohamed [verfasserIn] El-Badawy, Sherif [verfasserIn] Gabr, Alaa [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Pavement management system (PMS) Pavement performance prediction model Master sigmoidal curve Markov chain International roughness index (IRI) Long-term pavement performance (LTPP)

Übergeordnetes Werk:	Enthalten in: The Arabian journal for science and engineering - Berlin : Springer, 2011, 45(2020), 5 vom: 02. Jan., Seite 3973-3982
Übergeordnetes Werk:	volume:45 ; year:2020 ; number:5 ; day:02 ; month:01 ; pages:3973-3982

Links:	Volltext

DOI / URN:	10.1007/s13369-019-04321-8

Katalog-ID:	SPR039443337

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520			\|a Abstract Recently, highway agencies have become in need to enhance their pavement management systems (PMSs) using sound engineering and economic principles that find more prudent solutions to support wise investment choices and preserve the value of infrastructure assets. As pavement performance prediction models are one of the most important and fundamental components in any PMS, this paper focuses on comparing the master sigmoidal curve-based model as a deterministic technique with the Markov chain-based model as a probabilistic technique for the prediction of the international roughness index (IRI) as a pavement performance indicator. The IRI data obtained from the long-term pavement performance (LTPP) program were used to evaluate and compare both methods. In this paper, 44 flexible pavement sections from the GPS-1 experiment incorporating 432 IRI measurements ranging from 0.32 to 5.12 m/km were selected. Results showed that the predictive ability of the investigated models for the selected LTPP data was good with a reasonable coincidence ratio of about 65 percent between the predicted results of the two modeling techniques.
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allfields	10.1007/s13369-019-04321-8 doi (DE-627)SPR039443337 (SPR)s13369-019-04321-8-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl El-Khawaga, Mohamed verfasserin aut Comparison of Master Sigmoidal Curve and Markov Chain Techniques for Pavement Performance Prediction 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Recently, highway agencies have become in need to enhance their pavement management systems (PMSs) using sound engineering and economic principles that find more prudent solutions to support wise investment choices and preserve the value of infrastructure assets. As pavement performance prediction models are one of the most important and fundamental components in any PMS, this paper focuses on comparing the master sigmoidal curve-based model as a deterministic technique with the Markov chain-based model as a probabilistic technique for the prediction of the international roughness index (IRI) as a pavement performance indicator. The IRI data obtained from the long-term pavement performance (LTPP) program were used to evaluate and compare both methods. In this paper, 44 flexible pavement sections from the GPS-1 experiment incorporating 432 IRI measurements ranging from 0.32 to 5.12 m/km were selected. Results showed that the predictive ability of the investigated models for the selected LTPP data was good with a reasonable coincidence ratio of about 65 percent between the predicted results of the two modeling techniques. Pavement management system (PMS) (dpeaa)DE-He213 Pavement performance prediction model (dpeaa)DE-He213 Master sigmoidal curve (dpeaa)DE-He213 Markov chain (dpeaa)DE-He213 International roughness index (IRI) (dpeaa)DE-He213 Long-term pavement performance (LTPP) (dpeaa)DE-He213 El-Badawy, Sherif verfasserin aut Gabr, Alaa verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 45(2020), 5 vom: 02. Jan., Seite 3973-3982 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:45 year:2020 number:5 day:02 month:01 pages:3973-3982 https://dx.doi.org/10.1007/s13369-019-04321-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 45 2020 5 02 01 3973-3982
spelling	10.1007/s13369-019-04321-8 doi (DE-627)SPR039443337 (SPR)s13369-019-04321-8-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl El-Khawaga, Mohamed verfasserin aut Comparison of Master Sigmoidal Curve and Markov Chain Techniques for Pavement Performance Prediction 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Recently, highway agencies have become in need to enhance their pavement management systems (PMSs) using sound engineering and economic principles that find more prudent solutions to support wise investment choices and preserve the value of infrastructure assets. As pavement performance prediction models are one of the most important and fundamental components in any PMS, this paper focuses on comparing the master sigmoidal curve-based model as a deterministic technique with the Markov chain-based model as a probabilistic technique for the prediction of the international roughness index (IRI) as a pavement performance indicator. The IRI data obtained from the long-term pavement performance (LTPP) program were used to evaluate and compare both methods. In this paper, 44 flexible pavement sections from the GPS-1 experiment incorporating 432 IRI measurements ranging from 0.32 to 5.12 m/km were selected. Results showed that the predictive ability of the investigated models for the selected LTPP data was good with a reasonable coincidence ratio of about 65 percent between the predicted results of the two modeling techniques. Pavement management system (PMS) (dpeaa)DE-He213 Pavement performance prediction model (dpeaa)DE-He213 Master sigmoidal curve (dpeaa)DE-He213 Markov chain (dpeaa)DE-He213 International roughness index (IRI) (dpeaa)DE-He213 Long-term pavement performance (LTPP) (dpeaa)DE-He213 El-Badawy, Sherif verfasserin aut Gabr, Alaa verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 45(2020), 5 vom: 02. Jan., Seite 3973-3982 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:45 year:2020 number:5 day:02 month:01 pages:3973-3982 https://dx.doi.org/10.1007/s13369-019-04321-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 45 2020 5 02 01 3973-3982
allfields_unstemmed	10.1007/s13369-019-04321-8 doi (DE-627)SPR039443337 (SPR)s13369-019-04321-8-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl El-Khawaga, Mohamed verfasserin aut Comparison of Master Sigmoidal Curve and Markov Chain Techniques for Pavement Performance Prediction 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Recently, highway agencies have become in need to enhance their pavement management systems (PMSs) using sound engineering and economic principles that find more prudent solutions to support wise investment choices and preserve the value of infrastructure assets. As pavement performance prediction models are one of the most important and fundamental components in any PMS, this paper focuses on comparing the master sigmoidal curve-based model as a deterministic technique with the Markov chain-based model as a probabilistic technique for the prediction of the international roughness index (IRI) as a pavement performance indicator. The IRI data obtained from the long-term pavement performance (LTPP) program were used to evaluate and compare both methods. In this paper, 44 flexible pavement sections from the GPS-1 experiment incorporating 432 IRI measurements ranging from 0.32 to 5.12 m/km were selected. Results showed that the predictive ability of the investigated models for the selected LTPP data was good with a reasonable coincidence ratio of about 65 percent between the predicted results of the two modeling techniques. Pavement management system (PMS) (dpeaa)DE-He213 Pavement performance prediction model (dpeaa)DE-He213 Master sigmoidal curve (dpeaa)DE-He213 Markov chain (dpeaa)DE-He213 International roughness index (IRI) (dpeaa)DE-He213 Long-term pavement performance (LTPP) (dpeaa)DE-He213 El-Badawy, Sherif verfasserin aut Gabr, Alaa verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 45(2020), 5 vom: 02. Jan., Seite 3973-3982 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:45 year:2020 number:5 day:02 month:01 pages:3973-3982 https://dx.doi.org/10.1007/s13369-019-04321-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 45 2020 5 02 01 3973-3982
allfieldsGer	10.1007/s13369-019-04321-8 doi (DE-627)SPR039443337 (SPR)s13369-019-04321-8-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl El-Khawaga, Mohamed verfasserin aut Comparison of Master Sigmoidal Curve and Markov Chain Techniques for Pavement Performance Prediction 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Recently, highway agencies have become in need to enhance their pavement management systems (PMSs) using sound engineering and economic principles that find more prudent solutions to support wise investment choices and preserve the value of infrastructure assets. As pavement performance prediction models are one of the most important and fundamental components in any PMS, this paper focuses on comparing the master sigmoidal curve-based model as a deterministic technique with the Markov chain-based model as a probabilistic technique for the prediction of the international roughness index (IRI) as a pavement performance indicator. The IRI data obtained from the long-term pavement performance (LTPP) program were used to evaluate and compare both methods. In this paper, 44 flexible pavement sections from the GPS-1 experiment incorporating 432 IRI measurements ranging from 0.32 to 5.12 m/km were selected. Results showed that the predictive ability of the investigated models for the selected LTPP data was good with a reasonable coincidence ratio of about 65 percent between the predicted results of the two modeling techniques. Pavement management system (PMS) (dpeaa)DE-He213 Pavement performance prediction model (dpeaa)DE-He213 Master sigmoidal curve (dpeaa)DE-He213 Markov chain (dpeaa)DE-He213 International roughness index (IRI) (dpeaa)DE-He213 Long-term pavement performance (LTPP) (dpeaa)DE-He213 El-Badawy, Sherif verfasserin aut Gabr, Alaa verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 45(2020), 5 vom: 02. Jan., Seite 3973-3982 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:45 year:2020 number:5 day:02 month:01 pages:3973-3982 https://dx.doi.org/10.1007/s13369-019-04321-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 45 2020 5 02 01 3973-3982
allfieldsSound	10.1007/s13369-019-04321-8 doi (DE-627)SPR039443337 (SPR)s13369-019-04321-8-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl El-Khawaga, Mohamed verfasserin aut Comparison of Master Sigmoidal Curve and Markov Chain Techniques for Pavement Performance Prediction 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Recently, highway agencies have become in need to enhance their pavement management systems (PMSs) using sound engineering and economic principles that find more prudent solutions to support wise investment choices and preserve the value of infrastructure assets. As pavement performance prediction models are one of the most important and fundamental components in any PMS, this paper focuses on comparing the master sigmoidal curve-based model as a deterministic technique with the Markov chain-based model as a probabilistic technique for the prediction of the international roughness index (IRI) as a pavement performance indicator. The IRI data obtained from the long-term pavement performance (LTPP) program were used to evaluate and compare both methods. In this paper, 44 flexible pavement sections from the GPS-1 experiment incorporating 432 IRI measurements ranging from 0.32 to 5.12 m/km were selected. Results showed that the predictive ability of the investigated models for the selected LTPP data was good with a reasonable coincidence ratio of about 65 percent between the predicted results of the two modeling techniques. Pavement management system (PMS) (dpeaa)DE-He213 Pavement performance prediction model (dpeaa)DE-He213 Master sigmoidal curve (dpeaa)DE-He213 Markov chain (dpeaa)DE-He213 International roughness index (IRI) (dpeaa)DE-He213 Long-term pavement performance (LTPP) (dpeaa)DE-He213 El-Badawy, Sherif verfasserin aut Gabr, Alaa verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 45(2020), 5 vom: 02. Jan., Seite 3973-3982 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:45 year:2020 number:5 day:02 month:01 pages:3973-3982 https://dx.doi.org/10.1007/s13369-019-04321-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 45 2020 5 02 01 3973-3982
language	English
source	Enthalten in The Arabian journal for science and engineering 45(2020), 5 vom: 02. Jan., Seite 3973-3982 volume:45 year:2020 number:5 day:02 month:01 pages:3973-3982
sourceStr	Enthalten in The Arabian journal for science and engineering 45(2020), 5 vom: 02. Jan., Seite 3973-3982 volume:45 year:2020 number:5 day:02 month:01 pages:3973-3982
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Pavement management system (PMS) Pavement performance prediction model Master sigmoidal curve Markov chain International roughness index (IRI) Long-term pavement performance (LTPP)
dewey-raw	600
isfreeaccess_bool	false
container_title	The Arabian journal for science and engineering
authorswithroles_txt_mv	El-Khawaga, Mohamed @@aut@@ El-Badawy, Sherif @@aut@@ Gabr, Alaa @@aut@@
publishDateDaySort_date	2020-01-02T00:00:00Z
hierarchy_top_id	588780731
dewey-sort	3600
id	SPR039443337
language_de	englisch
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author	El-Khawaga, Mohamed
spellingShingle	El-Khawaga, Mohamed ddc 600 bkl 31.00 misc Pavement management system (PMS) misc Pavement performance prediction model misc Master sigmoidal curve misc Markov chain misc International roughness index (IRI) misc Long-term pavement performance (LTPP) Comparison of Master Sigmoidal Curve and Markov Chain Techniques for Pavement Performance Prediction
authorStr	El-Khawaga, Mohamed
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topic_title	600 500 ASE 31.00 bkl Comparison of Master Sigmoidal Curve and Markov Chain Techniques for Pavement Performance Prediction Pavement management system (PMS) (dpeaa)DE-He213 Pavement performance prediction model (dpeaa)DE-He213 Master sigmoidal curve (dpeaa)DE-He213 Markov chain (dpeaa)DE-He213 International roughness index (IRI) (dpeaa)DE-He213 Long-term pavement performance (LTPP) (dpeaa)DE-He213
topic	ddc 600 bkl 31.00 misc Pavement management system (PMS) misc Pavement performance prediction model misc Master sigmoidal curve misc Markov chain misc International roughness index (IRI) misc Long-term pavement performance (LTPP)
topic_unstemmed	ddc 600 bkl 31.00 misc Pavement management system (PMS) misc Pavement performance prediction model misc Master sigmoidal curve misc Markov chain misc International roughness index (IRI) misc Long-term pavement performance (LTPP)
topic_browse	ddc 600 bkl 31.00 misc Pavement management system (PMS) misc Pavement performance prediction model misc Master sigmoidal curve misc Markov chain misc International roughness index (IRI) misc Long-term pavement performance (LTPP)
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title	Comparison of Master Sigmoidal Curve and Markov Chain Techniques for Pavement Performance Prediction
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title_full	Comparison of Master Sigmoidal Curve and Markov Chain Techniques for Pavement Performance Prediction
author_sort	El-Khawaga, Mohamed
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author_browse	El-Khawaga, Mohamed El-Badawy, Sherif Gabr, Alaa
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author-letter	El-Khawaga, Mohamed
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title_sort	comparison of master sigmoidal curve and markov chain techniques for pavement performance prediction
title_auth	Comparison of Master Sigmoidal Curve and Markov Chain Techniques for Pavement Performance Prediction
abstract	Abstract Recently, highway agencies have become in need to enhance their pavement management systems (PMSs) using sound engineering and economic principles that find more prudent solutions to support wise investment choices and preserve the value of infrastructure assets. As pavement performance prediction models are one of the most important and fundamental components in any PMS, this paper focuses on comparing the master sigmoidal curve-based model as a deterministic technique with the Markov chain-based model as a probabilistic technique for the prediction of the international roughness index (IRI) as a pavement performance indicator. The IRI data obtained from the long-term pavement performance (LTPP) program were used to evaluate and compare both methods. In this paper, 44 flexible pavement sections from the GPS-1 experiment incorporating 432 IRI measurements ranging from 0.32 to 5.12 m/km were selected. Results showed that the predictive ability of the investigated models for the selected LTPP data was good with a reasonable coincidence ratio of about 65 percent between the predicted results of the two modeling techniques.
abstractGer	Abstract Recently, highway agencies have become in need to enhance their pavement management systems (PMSs) using sound engineering and economic principles that find more prudent solutions to support wise investment choices and preserve the value of infrastructure assets. As pavement performance prediction models are one of the most important and fundamental components in any PMS, this paper focuses on comparing the master sigmoidal curve-based model as a deterministic technique with the Markov chain-based model as a probabilistic technique for the prediction of the international roughness index (IRI) as a pavement performance indicator. The IRI data obtained from the long-term pavement performance (LTPP) program were used to evaluate and compare both methods. In this paper, 44 flexible pavement sections from the GPS-1 experiment incorporating 432 IRI measurements ranging from 0.32 to 5.12 m/km were selected. Results showed that the predictive ability of the investigated models for the selected LTPP data was good with a reasonable coincidence ratio of about 65 percent between the predicted results of the two modeling techniques.
abstract_unstemmed	Abstract Recently, highway agencies have become in need to enhance their pavement management systems (PMSs) using sound engineering and economic principles that find more prudent solutions to support wise investment choices and preserve the value of infrastructure assets. As pavement performance prediction models are one of the most important and fundamental components in any PMS, this paper focuses on comparing the master sigmoidal curve-based model as a deterministic technique with the Markov chain-based model as a probabilistic technique for the prediction of the international roughness index (IRI) as a pavement performance indicator. The IRI data obtained from the long-term pavement performance (LTPP) program were used to evaluate and compare both methods. In this paper, 44 flexible pavement sections from the GPS-1 experiment incorporating 432 IRI measurements ranging from 0.32 to 5.12 m/km were selected. Results showed that the predictive ability of the investigated models for the selected LTPP data was good with a reasonable coincidence ratio of about 65 percent between the predicted results of the two modeling techniques.
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container_issue	5
title_short	Comparison of Master Sigmoidal Curve and Markov Chain Techniques for Pavement Performance Prediction
url	https://dx.doi.org/10.1007/s13369-019-04321-8
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author2	El-Badawy, Sherif Gabr, Alaa
author2Str	El-Badawy, Sherif Gabr, Alaa
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doi_str	10.1007/s13369-019-04321-8
up_date	2024-07-03T23:57:44.665Z
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