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A New Method for Compaction Quality Evaluation of Asphalt Mixtures with the Intelligent Aggregate (IA)

To provide a new method for the evaluation of the compaction quality of asphalt mixture, a real-time data acquisition and processing system (RDAPS) for the motion state of aggregate with a small volume and high precision is developed. The system consists of an intelligent aggregate (IA), analysis so...

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Autores principales: Zhang, Chen, Wang, Hainian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8124658/
https://www.ncbi.nlm.nih.gov/pubmed/34066502
http://dx.doi.org/10.3390/ma14092422
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author Zhang, Chen
Wang, Hainian
author_facet Zhang, Chen
Wang, Hainian
author_sort Zhang, Chen
collection PubMed
description To provide a new method for the evaluation of the compaction quality of asphalt mixture, a real-time data acquisition and processing system (RDAPS) for the motion state of aggregate with a small volume and high precision is developed. The system consists of an intelligent aggregate (IA), analysis software and hardware equipment. The performance of the IA was tested by regarding data sensitivity, high-temperature resistance, and mechanical properties. A new evaluation method was proposed for evaluating the compaction quality of AC-25 and SMA-25 asphalt mixtures based on an IA. The results show that the best transmission baud rate for the IA was 9600 bps, and the corresponding signal transmission distance was 380 m. Only one IA was needed to complete the state data collection for the aggregate within the asphalt mixture in a circular area, with the IA layout point as the center of the circle and a radius of 5 m. The IA conducted reliable data transmission up to 200 °C; however, its compressive strength decreased with increasing temperature until reaching stability. Traditional aggregate could be replaced by an IA to withstand external forces and internal load transfer. Embedding an IA into AC-25 or SMA-25 asphalt mixtures did not have a significant impact on the original mechanical properties of the mixture. The effect of the gradation type of the asphalt mixture on the IA motion state was not significant. When the compaction degree met the specification requirements, the motion data of the IA did not reach a stable state, and the interlocking effect between aggregates in the asphalt mixture could be further optimized. An evaluation method is proposed based on the IA for the compaction quality of AC-25 and SMA-25 asphalt mixtures with the compaction degree as the main index and the spatial attitude angle and spatial acceleration of the IA as the auxiliary indexes.
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spelling pubmed-81246582021-05-17 A New Method for Compaction Quality Evaluation of Asphalt Mixtures with the Intelligent Aggregate (IA) Zhang, Chen Wang, Hainian Materials (Basel) Article To provide a new method for the evaluation of the compaction quality of asphalt mixture, a real-time data acquisition and processing system (RDAPS) for the motion state of aggregate with a small volume and high precision is developed. The system consists of an intelligent aggregate (IA), analysis software and hardware equipment. The performance of the IA was tested by regarding data sensitivity, high-temperature resistance, and mechanical properties. A new evaluation method was proposed for evaluating the compaction quality of AC-25 and SMA-25 asphalt mixtures based on an IA. The results show that the best transmission baud rate for the IA was 9600 bps, and the corresponding signal transmission distance was 380 m. Only one IA was needed to complete the state data collection for the aggregate within the asphalt mixture in a circular area, with the IA layout point as the center of the circle and a radius of 5 m. The IA conducted reliable data transmission up to 200 °C; however, its compressive strength decreased with increasing temperature until reaching stability. Traditional aggregate could be replaced by an IA to withstand external forces and internal load transfer. Embedding an IA into AC-25 or SMA-25 asphalt mixtures did not have a significant impact on the original mechanical properties of the mixture. The effect of the gradation type of the asphalt mixture on the IA motion state was not significant. When the compaction degree met the specification requirements, the motion data of the IA did not reach a stable state, and the interlocking effect between aggregates in the asphalt mixture could be further optimized. An evaluation method is proposed based on the IA for the compaction quality of AC-25 and SMA-25 asphalt mixtures with the compaction degree as the main index and the spatial attitude angle and spatial acceleration of the IA as the auxiliary indexes. MDPI 2021-05-06 /pmc/articles/PMC8124658/ /pubmed/34066502 http://dx.doi.org/10.3390/ma14092422 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Chen
Wang, Hainian
A New Method for Compaction Quality Evaluation of Asphalt Mixtures with the Intelligent Aggregate (IA)
title A New Method for Compaction Quality Evaluation of Asphalt Mixtures with the Intelligent Aggregate (IA)
title_full A New Method for Compaction Quality Evaluation of Asphalt Mixtures with the Intelligent Aggregate (IA)
title_fullStr A New Method for Compaction Quality Evaluation of Asphalt Mixtures with the Intelligent Aggregate (IA)
title_full_unstemmed A New Method for Compaction Quality Evaluation of Asphalt Mixtures with the Intelligent Aggregate (IA)
title_short A New Method for Compaction Quality Evaluation of Asphalt Mixtures with the Intelligent Aggregate (IA)
title_sort new method for compaction quality evaluation of asphalt mixtures with the intelligent aggregate (ia)
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8124658/
https://www.ncbi.nlm.nih.gov/pubmed/34066502
http://dx.doi.org/10.3390/ma14092422
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