IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i10p5938-d815073.html
   My bibliography  Save this article

A Novel Neural Computing Model Applied to Estimate the Dynamic Modulus (DM) of Asphalt Mixtures by the Improved Beetle Antennae Search

Author

Listed:
  • Jiandong Huang

    (School of Mines, China University of Mining and Technology, Xuzhou 221116, China
    Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia)

  • Mengmeng Zhou

    (School of Mines, China University of Mining and Technology, Xuzhou 221116, China)

  • Mohanad Muayad Sabri Sabri

    (Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia)

  • Hongwei Yuan

    (School of Mines, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

To accurately estimate the dynamic properties of the asphalt mixtures to be used in the Mechanistic-Empirical Pavement Design Guide (MEPDG), a novel neural computing model using the improved beetle antennae search was developed. Asphalt mixtures were designed conventionally by eight types of aggregate gradations and two types of asphalt binders. The dynamic modulus (DM) tests were conducted under 3 temperatures and 3 loading frequencies to construct 144 datasets for the machine learning process. A novel neural network model was developed by using an improved beetle antennae search (BAS) algorithm to adjust the hyperparameters more efficiently. The predictive results of the proposed model were determined by R and RMSE and the importance score of the input parameters was assessed as well. The prediction performance showed that the improved BAS algorithm can effectively adjust the hyperparameters of the neural network calculation model, and built the asphalt mixture DM prediction model has higher reliability and effectiveness than the random hyperparameter selection. The mixture model can accurately evaluate and predict the DM of the asphalt mixture to be used in MEPDG. The dynamic shear modulus of the asphalt binder is the most important parameter that affects the DM of the asphalt mixtures because of its high correlation with the adhesive effect in the composition. The phase angle of the binder showed the highest influence on the DM of the asphalt mixtures in the remaining variables. The importance of these influences can provide a reference for the future design of asphalt mixtures.

Suggested Citation

  • Jiandong Huang & Mengmeng Zhou & Mohanad Muayad Sabri Sabri & Hongwei Yuan, 2022. "A Novel Neural Computing Model Applied to Estimate the Dynamic Modulus (DM) of Asphalt Mixtures by the Improved Beetle Antennae Search," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:5938-:d:815073
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/10/5938/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/10/5938/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuan Gao & Jiandong Huang & Meng Li & Zhongran Dai & Rongli Jiang & Jixiong Zhang, 2021. "Chemical Modification of Combusted Coal Gangue for U(VI) Adsorption: Towards a Waste Control by Waste Strategy," Sustainability, MDPI, vol. 13(15), pages 1-13, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Heng Zhang & Qian Chang & Sui Li & Jiandong Huang, 2022. "Determining the Efficiency of the Sponge City Construction Pilots in China Based on the DEA-Malmquist Model," IJERPH, MDPI, vol. 19(18), pages 1-17, September.
    2. Ahmed M. Awed & Ahmed N. Awaad & Mosbeh R. Kaloop & Jong Wan Hu & Sherif M. El-Badawy & Ragaa T. Abd El-Hakim, 2023. "Boosting Hot Mix Asphalt Dynamic Modulus Prediction Using Statistical and Machine Learning Regression Modeling Techniques," Sustainability, MDPI, vol. 15(19), pages 1-27, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Heng Zhang & Qian Chang & Sui Li & Jiandong Huang, 2022. "Determining the Efficiency of the Sponge City Construction Pilots in China Based on the DEA-Malmquist Model," IJERPH, MDPI, vol. 19(18), pages 1-17, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:5938-:d:815073. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.