A formulation for asphalt concrete air void during service life by adopting a hybrid evolutionary polynomial regression and multi-gene genetic programming.

Autor: Ghanizadeh AR; Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran. ghanizadeh@sirjantech.ac.ir., Amlashi AT; School of Civil and Environmental Engineering and Construction Management, University of Texas at San Antonio, 1 UTSA Circle San Antonio, San Antonio, TX, 78249, USA., Bahrami A; Department of Building Engineering, Energy Systems and Sustainability Science, Faculty of Engineering and Sustainable Development, University of Gävle, 801 76, Gävle, Sweden. alireza.bahrami@hig.se., Isleem HF; School of Applied Technologies, Qujing Normal University, Qujing, 655011, Yunnan, China., Dessouky S; School of Civil and Environmental Engineering and Construction Management, University of Texas at San Antonio, 1 UTSA Circle San Antonio, San Antonio, TX, 78249, USA.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Jun 10; Vol. 14 (1), pp. 13254. Date of Electronic Publication: 2024 Jun 10.
DOI: 10.1038/s41598-024-61313-x
Abstrakt: Bitumen, aggregate, and air void (VA) are the three primary ingredients of asphalt concrete. VA changes over time as a function of four factors: traffic loads and repetitions, environmental regimes, compaction, and asphalt mix composition. Due to the high as-constructed VA content of the material, it is expected that VA will reduce over time, causing rutting during initial traffic periods. Eventually, the material will undergo shear flow when it reaches its densest state with optimum aggregate interlock or refusal VA content. Therefore, to ensure the quality of construction, VA in asphalt mixture need to be modeled throughout the service life. This study aims to implement a hybrid evolutionary polynomial regression (EPR) combined with a teaching-learning based optimization (TLBO) algorithm and multi-gene genetic programming (MGGP) to predict the VA percentage of asphalt mixture during the service life. For this purpose, 324 data records of VA were collected from the literature. The variables selected as inputs were original as-constructed VA, VA orig (%); mean annual air temperature, MAAT (°F); original viscosity at 77 °F, η o r i g , 77 (Mega-Poises); and time (months). EPR-TLBO was found to be superior to MGGP and existing empirical models due to the interquartile ranges of absolute error boxes equal to 0.67%. EPR-TLBO had an R 2 value of more than 0.90 in both the training and testing phases, and only less than 20% of the records were predicted utilizing this model with more than 20% deviation from the observed values. As determined by the sensitivity analysis, η o r i g , 77 is the most significant of the four input variables, while time is the least one. A parametric study showed that regardless of MAAT , η o r i g , 77 , of 0.3 Mega-Poises, and VA orig above 6% can be ideal for improving the pavement service life. It was also witnessed that with an increase of MAAT from 37 to 75 °F, the serviceability of asphalt concrete takes 15 months less on average.
(© 2024. The Author(s).)
Databáze: MEDLINE
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