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Modeling the Dynamic Behavior of Recycled Concrete Aggregate-Virgin Aggregates Blend Using Artificial Neural Network

Author

Listed:
  • Xiao Zhi

    (China National Building Material Group Co., Ltd., Beijing 100036, China)

  • Umar Faruk Aminu

    (School of Civil Engineering, Central South University, Changsha 410075, China)

  • Wenjun Hua

    (School of Civil Engineering, Central South University, Changsha 410075, China)

  • Yi Huang

    (Hunan Communications Research Institute Co., Ltd., Changsha 410015, China)

  • Tingyu Li

    (Hunan Communications Research Institute Co., Ltd., Changsha 410015, China)

  • Pin Deng

    (China National Building Material Group Co., Ltd., Beijing 100036, China)

  • Yuliang Chen

    (Hunan Communications Research Institute Co., Ltd., Changsha 410015, China)

  • Yuanjie Xiao

    (School of Civil Engineering, Central South University, Changsha 410075, China
    Ministry of Education (MOE) Key Laboratory of Engineering Structures of Heavy Haul Railway (Central South University), Changsha 410075, China)

  • Joseph Ali

    (School of Civil Engineering, Central South University, Changsha 410075, China)

Abstract

Construction and demolition waste (CDW) aggregates have increased as a result of the rise in construction activities. Current research focuses on recycling of CDW to replace dwindling natural aggregates but pays little attention to permanent deformation behavior due to the anisotropic nature of the blended CDW aggregates. Accordingly, this study performs repeated load triaxial tests to evaluate the permanent deformation mechanism of the blended materials under various shear stress ratios and moisture conditions. An artificial neural network (ANN) deformation prediction model that accounts for the complex nature of the blended CDW and natural aggregate was developed. Moreover, a sensitivity analysis was performed to determine the relative importance of each input variable on the deformation. The results indicated that the shear stress ratio and confining pressure profoundly influence the deformation. It was demonstrated that the proposed prediction model is more robust than the conventional one. The sensitivity analysis revealed that the number of loading cycles, confining pressure, and shear stress ratios are the principal factors influencing the permanent deformation of the blended aggregates with sensitivity coefficients of 31%, 25%, and 21%, respectively, followed by the CDW and moisture contents. This model can assist practitioners and policymakers in predicting the permanent deformation of CDW materials for unbound pavement base/subbase construction.

Suggested Citation

  • Xiao Zhi & Umar Faruk Aminu & Wenjun Hua & Yi Huang & Tingyu Li & Pin Deng & Yuliang Chen & Yuanjie Xiao & Joseph Ali, 2023. "Modeling the Dynamic Behavior of Recycled Concrete Aggregate-Virgin Aggregates Blend Using Artificial Neural Network," Sustainability, MDPI, vol. 15(19), pages 1-21, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:19:p:14228-:d:1248086
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    References listed on IDEAS

    as
    1. Jiménez, José Ramón & Ayuso, Jesús & Agrela, Francisco & López, Martín & Galvín, Adela Pérez, 2012. "Utilisation of unbound recycled aggregates from selected CDW in unpaved rural roads," Resources, Conservation & Recycling, Elsevier, vol. 58(C), pages 88-97.
    2. Li, Xuping, 2009. "Recycling and reuse of waste concrete in China," Resources, Conservation & Recycling, Elsevier, vol. 53(3), pages 107-112.
    3. Akshay Sakhare & Hafsa Farooq & Sanjay Nimbalkar & Goudappa R. Dodagoudar, 2022. "Dynamic Behavior of the Transition Zone of an Integral Abutment Bridge," Sustainability, MDPI, vol. 14(7), pages 1-18, March.
    Full references (including those not matched with items on IDEAS)

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