The American Association of State Highway and Transportation Officials Mechanistic-Empirical Pavement DesignGuide (AASHTO M-E) offers an opportunity to design more economical and sustainable high-volume rigid pavement...The American Association of State Highway and Transportation Officials Mechanistic-Empirical Pavement DesignGuide (AASHTO M-E) offers an opportunity to design more economical and sustainable high-volume rigid pavementscompared to conventional design guidelines. It is achieved through optimizing pavement structural andthickness design under specified climate and traffic conditions using advanced M-E principles, thereby minimizingeconomic costs and environmental impact. However, the implementation of AASHTO M-E design for low-volumeconcrete 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.). Thispaper introduces a novel extension of the AASHTO M-E framework for the design of low-volume joint plain concretepavements (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 performanceanalyses. The developed MGGP models simulate the fatigue damage and differential energy accumulations. Thispermits the prediction of transverse cracking and joint faulting for a wide range of design input parameters and axlespectrum. The developed MGGP-based models match Pavement ME-predicted cracking and faulting for rigidpavements with conventional concrete slab thicknesses and enable rational extrapolation of performance predictionfor thinner JPCPs. This paper demonstrates how the developed computational model enables sustainable lowvolumepavement design using optimized ME solutions for Pittsburgh, PA, conditions.展开更多
A team is assigned to accomplish a task in each infinitely-repeated period. The guide of the team and his followers are allowed to have asymmetric productivity; also the followers have either a hostile or favorable il...A team is assigned to accomplish a task in each infinitely-repeated period. The guide of the team and his followers are allowed to have asymmetric productivity; also the followers have either a hostile or favorable illusion toward the guide. Re- spective efforts and the followers＇ illusion are private information. At the end of each period, the output of the joint task emerges and the followers evaluate the guide. The analysis shows （1） that potential for an unreasonable evaluation suppresses the guide＇s effort down to an average level; （2） letting the followers inform the guide of their il- lusion in advance increases both sides＇ payoffs; （3） abolishing the evaluation reduces both sides＇ payoffs in general; and （4） however, if the magnitude of the followers＇ hos- tile illusion weighted by its relative probability is enormous, abolishing the evaluation increases the output and the guide＇s payoff.展开更多
基金the financial support from the University of Pittsburgh Anthony Gill Chair and the Impactful Resilient Infrastructure Science and Engineering Consortium(IRISE)at University of Pittsburgh.
文摘The American Association of State Highway and Transportation Officials Mechanistic-Empirical Pavement DesignGuide (AASHTO M-E) offers an opportunity to design more economical and sustainable high-volume rigid pavementscompared to conventional design guidelines. It is achieved through optimizing pavement structural andthickness design under specified climate and traffic conditions using advanced M-E principles, thereby minimizingeconomic costs and environmental impact. However, the implementation of AASHTO M-E design for low-volumeconcrete 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.). Thispaper introduces a novel extension of the AASHTO M-E framework for the design of low-volume joint plain concretepavements (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 performanceanalyses. The developed MGGP models simulate the fatigue damage and differential energy accumulations. Thispermits the prediction of transverse cracking and joint faulting for a wide range of design input parameters and axlespectrum. The developed MGGP-based models match Pavement ME-predicted cracking and faulting for rigidpavements with conventional concrete slab thicknesses and enable rational extrapolation of performance predictionfor thinner JPCPs. This paper demonstrates how the developed computational model enables sustainable lowvolumepavement design using optimized ME solutions for Pittsburgh, PA, conditions.
文摘A team is assigned to accomplish a task in each infinitely-repeated period. The guide of the team and his followers are allowed to have asymmetric productivity; also the followers have either a hostile or favorable illusion toward the guide. Re- spective efforts and the followers＇ illusion are private information. At the end of each period, the output of the joint task emerges and the followers evaluate the guide. The analysis shows （1） that potential for an unreasonable evaluation suppresses the guide＇s effort down to an average level; （2） letting the followers inform the guide of their il- lusion in advance increases both sides＇ payoffs; （3） abolishing the evaluation reduces both sides＇ payoffs in general; and （4） however, if the magnitude of the followers＇ hos- tile illusion weighted by its relative probability is enormous, abolishing the evaluation increases the output and the guide＇s payoff.
