Pavement sustainability index for highway infrastructures: A case study of Maryland

Abstract Pavement deterioration creates conditions that undermine their performances, which gives rise to the need for maintenance and rehabilitation. This paper develops a mathematical multi-linear regression analysis (MLRA) model to determine a pavement sustainability index (PSTI) as dependent var...
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Autor*in:	Obazee-Igbinedion, Stella O. [verfasserIn] Owolabi, Oludare [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	pavement indices sustainability pavement performance flexible pavements

Übergeordnetes Werk:	Enthalten in: Frontiers of architecture and civil engineering in China - Beijing : Higher Education Press, 2007, 12(2017), 2 vom: 14. Nov., Seite 192-200
Übergeordnetes Werk:	volume:12 ; year:2017 ; number:2 ; day:14 ; month:11 ; pages:192-200

Links:	Volltext

DOI / URN:	10.1007/s11709-017-0413-y

Katalog-ID:	SPR02198087X

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allfields	10.1007/s11709-017-0413-y doi (DE-627)SPR02198087X (SPR)s11709-017-0413-y-e DE-627 ger DE-627 rakwb eng 690 ASE 56.00 bkl 56.60 bkl Obazee-Igbinedion, Stella O. verfasserin aut Pavement sustainability index for highway infrastructures: A case study of Maryland 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Pavement deterioration creates conditions that undermine their performances, which gives rise to the need for maintenance and rehabilitation. This paper develops a mathematical multi-linear regression analysis (MLRA) model to determine a pavement sustainability index (PSTI) as dependent variable for flexible pavements in Maryland. Four categories of pavement performance evaluation indicators are subdivided into seven pavement condition indices and analyzed as independent variables for each section of pavement. Data are collected from five different roadways using field evaluations and existing database. Results indicate that coefficient of determination (R2) is correlated and significant, R2 = 0.959. Of the seven independent variables, present serviceability index (PSI) is the most significant with a coefficient value of 0.032, present serviceability rating (PSR) coefficient value = 0.028, and international roughness index (IRI) coefficient value =‒0.001. Increasing each unit value of coefficients for PSI and PSR would increase the value of PSTI; thereby providing a more sustainable pavement infrastructure; which explains the significance of the model and why IRI will most likely impact environmental, economic and social values. pavement indices (dpeaa)DE-He213 sustainability (dpeaa)DE-He213 pavement performance (dpeaa)DE-He213 flexible pavements (dpeaa)DE-He213 Owolabi, Oludare verfasserin aut Enthalten in Frontiers of architecture and civil engineering in China Beijing : Higher Education Press, 2007 12(2017), 2 vom: 14. Nov., Seite 192-200 (DE-627)546007724 (DE-600)2389476-3 1673-7512 nnns volume:12 year:2017 number:2 day:14 month:11 pages:192-200 https://dx.doi.org/10.1007/s11709-017-0413-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2190 56.00 ASE 56.60 ASE AR 12 2017 2 14 11 192-200
spelling	10.1007/s11709-017-0413-y doi (DE-627)SPR02198087X (SPR)s11709-017-0413-y-e DE-627 ger DE-627 rakwb eng 690 ASE 56.00 bkl 56.60 bkl Obazee-Igbinedion, Stella O. verfasserin aut Pavement sustainability index for highway infrastructures: A case study of Maryland 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Pavement deterioration creates conditions that undermine their performances, which gives rise to the need for maintenance and rehabilitation. This paper develops a mathematical multi-linear regression analysis (MLRA) model to determine a pavement sustainability index (PSTI) as dependent variable for flexible pavements in Maryland. Four categories of pavement performance evaluation indicators are subdivided into seven pavement condition indices and analyzed as independent variables for each section of pavement. Data are collected from five different roadways using field evaluations and existing database. Results indicate that coefficient of determination (R2) is correlated and significant, R2 = 0.959. Of the seven independent variables, present serviceability index (PSI) is the most significant with a coefficient value of 0.032, present serviceability rating (PSR) coefficient value = 0.028, and international roughness index (IRI) coefficient value =‒0.001. Increasing each unit value of coefficients for PSI and PSR would increase the value of PSTI; thereby providing a more sustainable pavement infrastructure; which explains the significance of the model and why IRI will most likely impact environmental, economic and social values. pavement indices (dpeaa)DE-He213 sustainability (dpeaa)DE-He213 pavement performance (dpeaa)DE-He213 flexible pavements (dpeaa)DE-He213 Owolabi, Oludare verfasserin aut Enthalten in Frontiers of architecture and civil engineering in China Beijing : Higher Education Press, 2007 12(2017), 2 vom: 14. Nov., Seite 192-200 (DE-627)546007724 (DE-600)2389476-3 1673-7512 nnns volume:12 year:2017 number:2 day:14 month:11 pages:192-200 https://dx.doi.org/10.1007/s11709-017-0413-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2190 56.00 ASE 56.60 ASE AR 12 2017 2 14 11 192-200
allfields_unstemmed	10.1007/s11709-017-0413-y doi (DE-627)SPR02198087X (SPR)s11709-017-0413-y-e DE-627 ger DE-627 rakwb eng 690 ASE 56.00 bkl 56.60 bkl Obazee-Igbinedion, Stella O. verfasserin aut Pavement sustainability index for highway infrastructures: A case study of Maryland 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Pavement deterioration creates conditions that undermine their performances, which gives rise to the need for maintenance and rehabilitation. This paper develops a mathematical multi-linear regression analysis (MLRA) model to determine a pavement sustainability index (PSTI) as dependent variable for flexible pavements in Maryland. Four categories of pavement performance evaluation indicators are subdivided into seven pavement condition indices and analyzed as independent variables for each section of pavement. Data are collected from five different roadways using field evaluations and existing database. Results indicate that coefficient of determination (R2) is correlated and significant, R2 = 0.959. Of the seven independent variables, present serviceability index (PSI) is the most significant with a coefficient value of 0.032, present serviceability rating (PSR) coefficient value = 0.028, and international roughness index (IRI) coefficient value =‒0.001. Increasing each unit value of coefficients for PSI and PSR would increase the value of PSTI; thereby providing a more sustainable pavement infrastructure; which explains the significance of the model and why IRI will most likely impact environmental, economic and social values. pavement indices (dpeaa)DE-He213 sustainability (dpeaa)DE-He213 pavement performance (dpeaa)DE-He213 flexible pavements (dpeaa)DE-He213 Owolabi, Oludare verfasserin aut Enthalten in Frontiers of architecture and civil engineering in China Beijing : Higher Education Press, 2007 12(2017), 2 vom: 14. Nov., Seite 192-200 (DE-627)546007724 (DE-600)2389476-3 1673-7512 nnns volume:12 year:2017 number:2 day:14 month:11 pages:192-200 https://dx.doi.org/10.1007/s11709-017-0413-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2190 56.00 ASE 56.60 ASE AR 12 2017 2 14 11 192-200
allfieldsGer	10.1007/s11709-017-0413-y doi (DE-627)SPR02198087X (SPR)s11709-017-0413-y-e DE-627 ger DE-627 rakwb eng 690 ASE 56.00 bkl 56.60 bkl Obazee-Igbinedion, Stella O. verfasserin aut Pavement sustainability index for highway infrastructures: A case study of Maryland 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Pavement deterioration creates conditions that undermine their performances, which gives rise to the need for maintenance and rehabilitation. This paper develops a mathematical multi-linear regression analysis (MLRA) model to determine a pavement sustainability index (PSTI) as dependent variable for flexible pavements in Maryland. Four categories of pavement performance evaluation indicators are subdivided into seven pavement condition indices and analyzed as independent variables for each section of pavement. Data are collected from five different roadways using field evaluations and existing database. Results indicate that coefficient of determination (R2) is correlated and significant, R2 = 0.959. Of the seven independent variables, present serviceability index (PSI) is the most significant with a coefficient value of 0.032, present serviceability rating (PSR) coefficient value = 0.028, and international roughness index (IRI) coefficient value =‒0.001. Increasing each unit value of coefficients for PSI and PSR would increase the value of PSTI; thereby providing a more sustainable pavement infrastructure; which explains the significance of the model and why IRI will most likely impact environmental, economic and social values. pavement indices (dpeaa)DE-He213 sustainability (dpeaa)DE-He213 pavement performance (dpeaa)DE-He213 flexible pavements (dpeaa)DE-He213 Owolabi, Oludare verfasserin aut Enthalten in Frontiers of architecture and civil engineering in China Beijing : Higher Education Press, 2007 12(2017), 2 vom: 14. Nov., Seite 192-200 (DE-627)546007724 (DE-600)2389476-3 1673-7512 nnns volume:12 year:2017 number:2 day:14 month:11 pages:192-200 https://dx.doi.org/10.1007/s11709-017-0413-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2190 56.00 ASE 56.60 ASE AR 12 2017 2 14 11 192-200
allfieldsSound	10.1007/s11709-017-0413-y doi (DE-627)SPR02198087X (SPR)s11709-017-0413-y-e DE-627 ger DE-627 rakwb eng 690 ASE 56.00 bkl 56.60 bkl Obazee-Igbinedion, Stella O. verfasserin aut Pavement sustainability index for highway infrastructures: A case study of Maryland 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Pavement deterioration creates conditions that undermine their performances, which gives rise to the need for maintenance and rehabilitation. This paper develops a mathematical multi-linear regression analysis (MLRA) model to determine a pavement sustainability index (PSTI) as dependent variable for flexible pavements in Maryland. Four categories of pavement performance evaluation indicators are subdivided into seven pavement condition indices and analyzed as independent variables for each section of pavement. Data are collected from five different roadways using field evaluations and existing database. Results indicate that coefficient of determination (R2) is correlated and significant, R2 = 0.959. Of the seven independent variables, present serviceability index (PSI) is the most significant with a coefficient value of 0.032, present serviceability rating (PSR) coefficient value = 0.028, and international roughness index (IRI) coefficient value =‒0.001. Increasing each unit value of coefficients for PSI and PSR would increase the value of PSTI; thereby providing a more sustainable pavement infrastructure; which explains the significance of the model and why IRI will most likely impact environmental, economic and social values. pavement indices (dpeaa)DE-He213 sustainability (dpeaa)DE-He213 pavement performance (dpeaa)DE-He213 flexible pavements (dpeaa)DE-He213 Owolabi, Oludare verfasserin aut Enthalten in Frontiers of architecture and civil engineering in China Beijing : Higher Education Press, 2007 12(2017), 2 vom: 14. Nov., Seite 192-200 (DE-627)546007724 (DE-600)2389476-3 1673-7512 nnns volume:12 year:2017 number:2 day:14 month:11 pages:192-200 https://dx.doi.org/10.1007/s11709-017-0413-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2190 56.00 ASE 56.60 ASE AR 12 2017 2 14 11 192-200
language	English
source	Enthalten in Frontiers of architecture and civil engineering in China 12(2017), 2 vom: 14. Nov., Seite 192-200 volume:12 year:2017 number:2 day:14 month:11 pages:192-200
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author	Obazee-Igbinedion, Stella O.
spellingShingle	Obazee-Igbinedion, Stella O. ddc 690 bkl 56.00 bkl 56.60 misc pavement indices misc sustainability misc pavement performance misc flexible pavements Pavement sustainability index for highway infrastructures: A case study of Maryland
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topic_title	690 ASE 56.00 bkl 56.60 bkl Pavement sustainability index for highway infrastructures: A case study of Maryland pavement indices (dpeaa)DE-He213 sustainability (dpeaa)DE-He213 pavement performance (dpeaa)DE-He213 flexible pavements (dpeaa)DE-He213
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title	Pavement sustainability index for highway infrastructures: A case study of Maryland
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title_full	Pavement sustainability index for highway infrastructures: A case study of Maryland
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title_sort	pavement sustainability index for highway infrastructures: a case study of maryland
title_auth	Pavement sustainability index for highway infrastructures: A case study of Maryland
abstract	Abstract Pavement deterioration creates conditions that undermine their performances, which gives rise to the need for maintenance and rehabilitation. This paper develops a mathematical multi-linear regression analysis (MLRA) model to determine a pavement sustainability index (PSTI) as dependent variable for flexible pavements in Maryland. Four categories of pavement performance evaluation indicators are subdivided into seven pavement condition indices and analyzed as independent variables for each section of pavement. Data are collected from five different roadways using field evaluations and existing database. Results indicate that coefficient of determination (R2) is correlated and significant, R2 = 0.959. Of the seven independent variables, present serviceability index (PSI) is the most significant with a coefficient value of 0.032, present serviceability rating (PSR) coefficient value = 0.028, and international roughness index (IRI) coefficient value =‒0.001. Increasing each unit value of coefficients for PSI and PSR would increase the value of PSTI; thereby providing a more sustainable pavement infrastructure; which explains the significance of the model and why IRI will most likely impact environmental, economic and social values.
abstractGer	Abstract Pavement deterioration creates conditions that undermine their performances, which gives rise to the need for maintenance and rehabilitation. This paper develops a mathematical multi-linear regression analysis (MLRA) model to determine a pavement sustainability index (PSTI) as dependent variable for flexible pavements in Maryland. Four categories of pavement performance evaluation indicators are subdivided into seven pavement condition indices and analyzed as independent variables for each section of pavement. Data are collected from five different roadways using field evaluations and existing database. Results indicate that coefficient of determination (R2) is correlated and significant, R2 = 0.959. Of the seven independent variables, present serviceability index (PSI) is the most significant with a coefficient value of 0.032, present serviceability rating (PSR) coefficient value = 0.028, and international roughness index (IRI) coefficient value =‒0.001. Increasing each unit value of coefficients for PSI and PSR would increase the value of PSTI; thereby providing a more sustainable pavement infrastructure; which explains the significance of the model and why IRI will most likely impact environmental, economic and social values.
abstract_unstemmed	Abstract Pavement deterioration creates conditions that undermine their performances, which gives rise to the need for maintenance and rehabilitation. This paper develops a mathematical multi-linear regression analysis (MLRA) model to determine a pavement sustainability index (PSTI) as dependent variable for flexible pavements in Maryland. Four categories of pavement performance evaluation indicators are subdivided into seven pavement condition indices and analyzed as independent variables for each section of pavement. Data are collected from five different roadways using field evaluations and existing database. Results indicate that coefficient of determination (R2) is correlated and significant, R2 = 0.959. Of the seven independent variables, present serviceability index (PSI) is the most significant with a coefficient value of 0.032, present serviceability rating (PSR) coefficient value = 0.028, and international roughness index (IRI) coefficient value =‒0.001. Increasing each unit value of coefficients for PSI and PSR would increase the value of PSTI; thereby providing a more sustainable pavement infrastructure; which explains the significance of the model and why IRI will most likely impact environmental, economic and social values.
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title_short	Pavement sustainability index for highway infrastructures: A case study of Maryland
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This paper develops a mathematical multi-linear regression analysis (MLRA) model to determine a pavement sustainability index (PSTI) as dependent variable for flexible pavements in Maryland. Four categories of pavement performance evaluation indicators are subdivided into seven pavement condition indices and analyzed as independent variables for each section of pavement. Data are collected from five different roadways using field evaluations and existing database. Results indicate that coefficient of determination (R2) is correlated and significant, R2 = 0.959. Of the seven independent variables, present serviceability index (PSI) is the most significant with a coefficient value of 0.032, present serviceability rating (PSR) coefficient value = 0.028, and international roughness index (IRI) coefficient value =‒0.001. Increasing each unit value of coefficients for PSI and PSR would increase the value of PSTI; thereby providing a more sustainable pavement infrastructure; which explains the significance of the model and why IRI will most likely impact environmental, economic and social values.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">pavement indices</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">sustainability</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">pavement performance</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">flexible pavements</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Owolabi, Oludare</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Frontiers of architecture and civil engineering in China</subfield><subfield code="d">Beijing : Higher Education Press, 2007</subfield><subfield code="g">12(2017), 2 vom: 14. 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Pavement sustainability index for highway infrastructures: A case study of Maryland

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