Multi-gene genetic programming extension of AASHTO M-E for design of low-volume concrete pavements

The American Association of State Highway and Transportation Officials Mechanistic-Empirical Pavement Design Guide (AASHTO M-E) offers an opportunity to design more economical and sustainable high-volume rigid pavements compared to conventional design guidelines. It is achieved through optimizing pa...

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Main Authors: Haoran Li, Lev Khazanovich
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2022-09-01
Series:Journal of Road Engineering
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2097049822000464
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author Haoran Li
Lev Khazanovich
author_facet Haoran Li
Lev Khazanovich
author_sort Haoran Li
collection DOAJ
description The American Association of State Highway and Transportation Officials Mechanistic-Empirical Pavement Design Guide (AASHTO M-E) offers an opportunity to design more economical and sustainable high-volume rigid pavements compared to conventional design guidelines. It is achieved through optimizing pavement structural and thickness design under specified climate and traffic conditions using advanced M-E principles, thereby minimizing economic costs and environmental impact. However, the implementation of AASHTO M-E design for low-volume concrete pavements using AASHTOWare Pavement ME Design (Pavement ME) software is often overly conservative. This is because Pavement ME specifies the minimum design thickness of concrete slab as 152.4 ​mm (6 in.). This paper introduces a novel extension of the AASHTO M-E framework for the design of low-volume joint plain concrete pavements (JPCPs) without modification of Pavement ME. It utilizes multi-gene genetic programming (MGGP)-based computational models to obtain rapid solutions for JPCP damage accumulation and long-term performance analyses. The developed MGGP models simulate the fatigue damage and differential energy accumulations. This permits the prediction of transverse cracking and joint faulting for a wide range of design input parameters and axle spectrum. The developed MGGP-based models match Pavement ME-predicted cracking and faulting for rigid pavements with conventional concrete slab thicknesses and enable rational extrapolation of performance prediction for thinner JPCPs. This paper demonstrates how the developed computational model enables sustainable low-volume pavement design using optimized ME solutions for Pittsburgh, PA, conditions.
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spelling doaj.art-3deb3db4f75b40fdbbd1b3bcefc30b702023-08-17T04:27:27ZengKeAi Communications Co., Ltd.Journal of Road Engineering2097-04982022-09-0123252266Multi-gene genetic programming extension of AASHTO M-E for design of low-volume concrete pavementsHaoran Li0Lev Khazanovich1Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USACorresponding author.; Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USAThe American Association of State Highway and Transportation Officials Mechanistic-Empirical Pavement Design Guide (AASHTO M-E) offers an opportunity to design more economical and sustainable high-volume rigid pavements compared to conventional design guidelines. It is achieved through optimizing pavement structural and thickness design under specified climate and traffic conditions using advanced M-E principles, thereby minimizing economic costs and environmental impact. However, the implementation of AASHTO M-E design for low-volume concrete pavements using AASHTOWare Pavement ME Design (Pavement ME) software is often overly conservative. This is because Pavement ME specifies the minimum design thickness of concrete slab as 152.4 ​mm (6 in.). This paper introduces a novel extension of the AASHTO M-E framework for the design of low-volume joint plain concrete pavements (JPCPs) without modification of Pavement ME. It utilizes multi-gene genetic programming (MGGP)-based computational models to obtain rapid solutions for JPCP damage accumulation and long-term performance analyses. The developed MGGP models simulate the fatigue damage and differential energy accumulations. This permits the prediction of transverse cracking and joint faulting for a wide range of design input parameters and axle spectrum. The developed MGGP-based models match Pavement ME-predicted cracking and faulting for rigid pavements with conventional concrete slab thicknesses and enable rational extrapolation of performance prediction for thinner JPCPs. This paper demonstrates how the developed computational model enables sustainable low-volume pavement design using optimized ME solutions for Pittsburgh, PA, conditions.http://www.sciencedirect.com/science/article/pii/S2097049822000464Mechanistic-empirical pavement design guideLow-volume roadsConcrete pavementTransverse crackingJoint faultingMulti-gene genetic programming (MGGP)
spellingShingle Haoran Li
Lev Khazanovich
Multi-gene genetic programming extension of AASHTO M-E for design of low-volume concrete pavements
Journal of Road Engineering
Mechanistic-empirical pavement design guide
Low-volume roads
Concrete pavement
Transverse cracking
Joint faulting
Multi-gene genetic programming (MGGP)
title Multi-gene genetic programming extension of AASHTO M-E for design of low-volume concrete pavements
title_full Multi-gene genetic programming extension of AASHTO M-E for design of low-volume concrete pavements
title_fullStr Multi-gene genetic programming extension of AASHTO M-E for design of low-volume concrete pavements
title_full_unstemmed Multi-gene genetic programming extension of AASHTO M-E for design of low-volume concrete pavements
title_short Multi-gene genetic programming extension of AASHTO M-E for design of low-volume concrete pavements
title_sort multi gene genetic programming extension of aashto m e for design of low volume concrete pavements
topic Mechanistic-empirical pavement design guide
Low-volume roads
Concrete pavement
Transverse cracking
Joint faulting
Multi-gene genetic programming (MGGP)
url http://www.sciencedirect.com/science/article/pii/S2097049822000464
work_keys_str_mv AT haoranli multigenegeneticprogrammingextensionofaashtomefordesignoflowvolumeconcretepavements
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