Real-time data sensing and digital twin model development for pavement material mixing: enhancing workability and optimisation

Chonghui Wang; Xiaodong Zhou; Yuqing Zhang; Hainian Wang; Markus Oeser; Pengfei Liu

doi:10.1080/10298436.2024.2417973

Real-time data sensing and digital twin model development for pavement material mixing: enhancing workability and optimisation

Chonghui Wang
, Xiaodong Zhou
, Yuqing Zhang^*
, Hainian Wang
, Markus Oeser
, Pengfei Liu

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

2 Citations (SciVal)

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Abstract

An essential aspect of pavement construction sustainability is its low-energy consumption and emissions. The study of pavement materials workability tests holds significant importance in terms of achieving well-mixed conditions with low-energy consumption. The complex components of the material and the uncertain kinematic behaviours of aggregates during mixing make this process challenging. And, few studies of the signal response of pavement materials have been found in the field of civil engineering. For this purpose, an accurate evaluation and monitoring approach for mixing are needed. In this paper, a wireless real-time sensing method is used to monitor the dynamic behaviour of aggregates during mixing. A 3D digital twin model, combining data-sensing techniques and numerical simulation, has been proposed for rapid identification of the mixing material. This model has been validated via a data-fusion algorithm. The application of this model makes a contribution to the data-intensive analysing jobs and decision-making tasks in pavement construction engineering.

Original language	English
Article number	2417973
Number of pages	16
Journal	International Journal of Pavement Engineering
Volume	25
Issue number	1
Early online date	23 Oct 2024
DOIs	https://doi.org/10.1080/10298436.2024.2417973
Publication status	Published - 23 Oct 2024

Bibliographical note

Copyright © 2024 Informa UK Limited, trading as Taylor & Francis Group. This is an Accepted Manuscript version of the following article, accepted for publication in International Journal of Pavement Engineering [Wang, C. et al. (2024) ‘Real-time data sensing and digital twin model development for pavement material mixing: enhancing workability and optimisation’, International Journal of Pavement Engineering, 25(1). doi: 10.1080/10298436.2024.2417973]. It is deposited under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

Data Access Statement

The open source code (licensed under a Creative Commons Attribution 4.0 International licence: https://creativecommons.org/licenses/by/4.0/) shared in the article can be seen and downloaded from the following link: https://data.mendeley.com/datasets/bggwfsrfrj/1

Funding

This paper is supported by the Engineering and Physical Sciences Research Council: [Grant Number EP/W000334/1 & EP/W000369/1]; Start-up Research Fund of Southeast University: [Grant Number RF1028623231], and Distinguished Youth Fund (Overseas) under National Natural Science Foundation of China: [Grant Number 1121002327]. The first author of this manuscript also appreciates the support from the China Scholarship Council: [Grant Number 201706560034]. The authors would like to express their sincere gratitude to Dr. Frederic Otto from the Institute of Highway Engineering at RWTH Aachen University for his valuable help and support in refining the MATLAB code used to process the signal data. Special thanks also go to Dirk Blotenberg, Georgios Gerokonstantis, Andr\u00E9 Dahmen, Jan Schulz, and Harald Widemeyer for their invaluable assistance and support during the laboratory tests at RWTH Aachen. The authors are solely responsible for the content.

Keywords

Real-time data sensing
data-fusion
digital transportation engineering
digital twin
infrastructure construction
pavement workability
signal response

Access to Document

10.1080/10298436.2024.2417973

Real-time Data Sensing and DIgital Twin Model Development_AAM_Wang
Copyright © 2024 Informa UK Limited, trading as Taylor & Francis Group. This is an Accepted Manuscript version of the following article, accepted for publication in International Journal of Pavement Engineering [Wang, C. et al. (2024) ‘Real-time data sensing and digital twin model development for pavement material mixing: enhancing workability and optimisation’, International Journal of Pavement Engineering, 25(1). doi: 10.1080/10298436.2024.2417973]. It is deposited under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Accepted author manuscript, 3.54 MBLicence: CC BY-NC 4.0

Cite this

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title = "Real-time data sensing and digital twin model development for pavement material mixing: enhancing workability and optimisation",

abstract = "An essential aspect of pavement construction sustainability is its low-energy consumption and emissions. The study of pavement materials workability tests holds significant importance in terms of achieving well-mixed conditions with low-energy consumption. The complex components of the material and the uncertain kinematic behaviours of aggregates during mixing make this process challenging. And, few studies of the signal response of pavement materials have been found in the field of civil engineering. For this purpose, an accurate evaluation and monitoring approach for mixing are needed. In this paper, a wireless real-time sensing method is used to monitor the dynamic behaviour of aggregates during mixing. A 3D digital twin model, combining data-sensing techniques and numerical simulation, has been proposed for rapid identification of the mixing material. This model has been validated via a data-fusion algorithm. The application of this model makes a contribution to the data-intensive analysing jobs and decision-making tasks in pavement construction engineering.",

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note = "Copyright {\textcopyright} 2024 Informa UK Limited, trading as Taylor \& Francis Group. This is an Accepted Manuscript version of the following article, accepted for publication in International Journal of Pavement Engineering [Wang, C. et al. (2024) {\textquoteleft}Real-time data sensing and digital twin model development for pavement material mixing: enhancing workability and optimisation{\textquoteright}, International Journal of Pavement Engineering, 25(1). doi: 10.1080/10298436.2024.2417973]. It is deposited under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.",

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N2 - An essential aspect of pavement construction sustainability is its low-energy consumption and emissions. The study of pavement materials workability tests holds significant importance in terms of achieving well-mixed conditions with low-energy consumption. The complex components of the material and the uncertain kinematic behaviours of aggregates during mixing make this process challenging. And, few studies of the signal response of pavement materials have been found in the field of civil engineering. For this purpose, an accurate evaluation and monitoring approach for mixing are needed. In this paper, a wireless real-time sensing method is used to monitor the dynamic behaviour of aggregates during mixing. A 3D digital twin model, combining data-sensing techniques and numerical simulation, has been proposed for rapid identification of the mixing material. This model has been validated via a data-fusion algorithm. The application of this model makes a contribution to the data-intensive analysing jobs and decision-making tasks in pavement construction engineering.

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Real-time data sensing and digital twin model development for pavement material mixing: enhancing workability and optimisation

Abstract

Bibliographical note

Data Access Statement

Funding

Keywords

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