IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i13p8189-d855940.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/13/8189/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/13/8189/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    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. 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.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Mohammed A. Mansour & Mohd Hanif Bin Ismail & Qadir Bux alias Imran Latif & Abdullah Faisal Alshalif & Abdalrhman Milad & Walid Abdullah Al Bargi, 2023. "A Systematic Review of the Concrete Durability Incorporating Recycled Glass," Sustainability, MDPI, vol. 15(4), pages 1-33, February.
    3. Elham Alzain & Shaha Al-Otaibi & Theyazn H. H. Aldhyani & Ali Saleh Alshebami & Mohammed Amin Almaiah & Mukti E. Jadhav, 2023. "Revolutionizing Solar Power Production with Artificial Intelligence: A Sustainable Predictive Model," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
    4. Fazal Hussain & Shayan Ali Khan & Rao Arsalan Khushnood & Ameer Hamza & Fazal Rehman, 2022. "Machine Learning-Based Predictive Modeling of Sustainable Lightweight Aggregate Concrete," Sustainability, MDPI, vol. 15(1), pages 1-22, December.
    5. Amr El-said & Ahmed Awad & Mahmood Ahmad & Mohanad Muayad Sabri Sabri & Ahmed Farouk Deifalla & Maged Tawfik, 2022. "The Mechanical Behavior of Sustainable Concrete Using Raw and Processed Sugarcane Bagasse Ash," Sustainability, MDPI, vol. 14(18), pages 1-21, September.
    6. Kamran Iqbal & Hafiz Suliman Munawar & Hina Inam & Siddra Qayyum, 2021. "Promoting Customer Loyalty and Satisfaction in Financial Institutions through Technology Integration: The Roles of Service Quality, Awareness, and Perceptions," Sustainability, MDPI, vol. 13(23), pages 1-20, November.
    7. Xuhong Yang & Haoxu Fang & Yaxiong Wu & Wei Jia, 2022. "RBF Neural Network Fractional-Order Sliding Mode Control with an Application to Direct a Three Matrix Converter under an Unbalanced Grid," Sustainability, MDPI, vol. 14(6), pages 1-17, March.
    8. Celal Cakiroglu & Gebrail Bekdaş, 2023. "Predictive Modeling of Recycled Aggregate Concrete Beam Shear Strength Using Explainable Ensemble Learning Methods," Sustainability, MDPI, vol. 15(6), pages 1-21, March.
    9. Kaiyue Zhao & Peng Zhang & Bing Wang & Yupeng Tian & Shanbin Xue & Yuan Cong, 2021. "Preparation of Electric- and Magnetic-Activated Water and Its Influence on the Workability and Mechanical Properties of Cement Mortar," Sustainability, MDPI, vol. 13(8), pages 1-17, April.
    10. Mosleh Hmoud Al-Adhaileh & Fawaz Waselallah Alsaade, 2021. "Modelling and Prediction of Water Quality by Using Artificial Intelligence," Sustainability, MDPI, vol. 13(8), pages 1-18, April.

    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:13:p:8189-:d:855940. 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.