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Influence of a Novel Carbon-Based Nano-Material on the Thermal Conductivity of Mortar

Author

Listed:
  • Sergiu-Mihai Alexa-Stratulat

    (Faculty of Civil Engineering and Building Services, The “Gheorghe Asachi” Technical University of Iasi, 700050 Iasi, Romania)

  • Daniel Covatariu

    (Faculty of Civil Engineering and Building Services, The “Gheorghe Asachi” Technical University of Iasi, 700050 Iasi, Romania)

  • Ana-Maria Toma

    (Faculty of Civil Engineering and Building Services, The “Gheorghe Asachi” Technical University of Iasi, 700050 Iasi, Romania)

  • Ancuta Rotaru

    (Faculty of Civil Engineering and Building Services, The “Gheorghe Asachi” Technical University of Iasi, 700050 Iasi, Romania)

  • Gabriela Covatariu

    (Faculty of Civil Engineering and Building Services, The “Gheorghe Asachi” Technical University of Iasi, 700050 Iasi, Romania)

  • Ionut-Ovidiu Toma

    (Faculty of Civil Engineering and Building Services, The “Gheorghe Asachi” Technical University of Iasi, 700050 Iasi, Romania)

Abstract

The paper presents the results of research work to assess the thermal conductivity of mortar incorporating a novel carbon-based nano-material (CBN). The data from the laboratory tests served as the starting point in training an artificial neural network (ANN) based on the Levenberg–Marquardt backpropagation algorithm that was used to predict the values of the thermal conductivity at later ages. The used CBNs were essential precursors of multi-walled carbon nano-tubes but different from their counterparts in the fact that they were capped at the ends. This configuration should result in lower surface tension and should prevent the bundling even without the use of surfactants and sonication. The obtained results show that the mortar mixes with CBN exhibit higher values for the thermal coefficient at early ages compared to the reference mix, even at very low percentages of CBN by weight of cement. The ANN is able to accurately predict the experimental results both at 28 days and at later ages. The obtained results should serve as the starting point for further investigations into the microstructure of cement-based materials enhanced with CBNs.

Suggested Citation

  • Sergiu-Mihai Alexa-Stratulat & Daniel Covatariu & Ana-Maria Toma & Ancuta Rotaru & Gabriela Covatariu & Ionut-Ovidiu Toma, 2022. "Influence of a Novel Carbon-Based Nano-Material on the Thermal Conductivity of Mortar," Sustainability, MDPI, vol. 14(13), pages 1-14, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:8189-:d:855940
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    References listed on IDEAS

    as
    1. Heba A. Gamal & M. S. El-Feky & Yousef R. Alharbi & Aref A. Abadel & Mohamed Kohail, 2021. "Enhancement of the Concrete Durability with Hybrid Nano Materials," Sustainability, MDPI, vol. 13(3), pages 1-17, January.
    2. Mohammad Mehdi Roshani & Seyed Hamidreza Kargar & Visar Farhangi & Moses Karakouzian, 2021. "Predicting the Effect of Fly Ash on Concrete’s Mechanical Properties by ANN," Sustainability, MDPI, vol. 13(3), pages 1-16, January.
    3. Emerson Felipe Felix & Edna Possan & Rogério Carrazedo, 2021. "A New Formulation to Estimate the Elastic Modulus of Recycled Concrete Based on Regression and ANN," Sustainability, MDPI, vol. 13(15), pages 1-21, July.
    4. Xu Huang & Jiaqi Zhang & Jessada Sresakoolchai & Sakdirat Kaewunruen, 2021. "Machine Learning Aided Design and Prediction of Environmentally Friendly Rubberised Concrete," Sustainability, MDPI, vol. 13(4), pages 1-26, February.
    5. Ahsen Maqsoom & Bilal Aslam & Muhammad Ehtisham Gul & Fahim Ullah & Abbas Z. Kouzani & M. A. Parvez Mahmud & Adnan Nawaz, 2021. "Using Multivariate Regression and ANN Models to Predict Properties of Concrete Cured under Hot Weather," Sustainability, MDPI, vol. 13(18), pages 1-28, September.
    6. Tawfiq Al-Mughanam & Theyazn H. H. Aldhyani & Belal Alsubari & Mohammed Al-Yaari, 2020. "Modeling of Compressive Strength of Sustainable Self-Compacting Concrete Incorporating Treated Palm Oil Fuel Ash Using Artificial Neural Network," Sustainability, MDPI, vol. 12(22), pages 1-13, November.
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