IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i17p7932-d1741334.html

A Mix-Design Method for the Specific Surface Area of Eco-Concrete Based on Statistical Analysis

Author

Listed:
  • Guofa Dong

    (College of Smart Water Conservancy Engineering, Xinjiang Institute of Technology, Akesu 843100, China)

  • Jiale Zhang

    (Institute of Port, Coastal, and Offshore Engineering, Ocean College, Zhejiang University, Zhoushan 316021, China)

  • Abdolhossein Naghizadeh

    (Department of Engineering Sciences, University of the Free State, Bloemfontein 9300, South Africa)

  • Chuangzhou Wu

    (Institute of Port, Coastal, and Offshore Engineering, Ocean College, Zhejiang University, Zhoushan 316021, China)

  • Zhen Zhang

    (College of Smart Water Conservancy Engineering, Xinjiang Institute of Technology, Akesu 843100, China)

  • Xinyu Zhan

    (College of Smart Water Conservancy Engineering, Xinjiang Institute of Technology, Akesu 843100, China)

Abstract

Ecological concrete designed by empirical method does not consider the mesoscopic influence of aggregates, resulting in problems such as low strength, excessive porosity, and poor stability with different gradations, which severely restricts the development and application of ecological concrete. To achieve the refined design of ecological concrete, a mesoscopic specific surface area design method based on statistical analysis is proposed. First, the meso-aggregate model with sub-millimeter precision was established using a high-precision 3D scanner, and CloudCompare was used to calculate the specific surface area of the mesoscopic aggregate model, laying the foundation for the statistical analysis of specific surface area. Second, statistical analysis methods verified that the mean specific surface area of 20 aggregates from a single random sampling reliably estimates the mean of the overall aggregate population. Third, the optimal water–cement ratio was calculated considering the water absorption characteristics and the mortar-wrapping capacity of aggregates; standard cubic specimens were prepared using this optimal water–cement ratio, with aggregates evenly coated with mortar and no obvious mortar settlement. Fourth, the cubic compressive strength of specimens naturally cured for 7 days was tested; experimental results showed that the cubic compressive strength of specimens formed by this project’s design method increased by more than 30% compared to the empirical design method. The results indicate that using the average volume-specific surface area of 20 aggregates to assess the overall average volume-specific surface area of aggregates is both reliable and relatively efficient. Based on the reliable estimation of the overall average volume-specific surface area of aggregates derived from this method, measurements were taken of the thickness of water films adsorbed on dry aggregates and the thickness of mortar coatings on surface-dry aggregates. Further, the optimal water–cement ratio for eco-concrete was deduced, and a comprehensive set of feasible refined methods for eco-concrete mix proportion design was proposed. In contrast to the empirical method, concrete designed via the subject’s methodology exhibits a marked enhancement in compressive strength while retaining favorable pore characteristics—rendering it well-suited for deployment in the slope protection of reservoirs and ponds and thereby facilitating the realization of ecological slope protection functionality.

Suggested Citation

  • Guofa Dong & Jiale Zhang & Abdolhossein Naghizadeh & Chuangzhou Wu & Zhen Zhang & Xinyu Zhan, 2025. "A Mix-Design Method for the Specific Surface Area of Eco-Concrete Based on Statistical Analysis," Sustainability, MDPI, vol. 17(17), pages 1-21, September.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:17:p:7932-:d:1741334
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/17/7932/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/17/7932/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. J. P. Royston, 1982. "An Extension of Shapiro and Wilk's W Test for Normality to Large Samples," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(2), pages 115-124, June.
    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. Minguez, Ana & Javier Sese, F., 2022. "Why do you want a relationship, anyway? Consent to receive marketing communications and donors’ willingness to engage with nonprofits," Journal of Business Research, Elsevier, vol. 148(C), pages 356-367.
    2. Halldén, Filip & Hultberg, Anna & Ahmed, Ali & Uddin, Gazi Salah & Yahya, Muhammad & Troster, Victor, 2025. "The role of institutional quality on public renewable energy investments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 215(C).
    3. Murigi, Michael & Muchai, Dianah Ngui & Ogada, Maurice Juma, 2024. "Drivers of Participation in Smallholders Banana Contract Farming in Kenya," African Journal of Economic Review, African Journal of Economic Review, vol. 12(2), June.
    4. Lawrence L. Giventer, 1992. "Kolmogorov-Smirnov One and Two Variable Tests," Stata Technical Bulletin, StataCorp LLC, vol. 1(2).
    5. Lauren Bin Dong & David E. A. Giles, 2004. "An Empirical Likelihood Ratio Test for Normality," Econometrics Working Papers 0401, Department of Economics, University of Victoria.
    6. Richard Goldstein, 1992. "Test for General Specification Error in Linear Regression," Stata Technical Bulletin, StataCorp LLC, vol. 1(2).
    7. Tomasz Górecki & Lajos Horváth & Piotr Kokoszka, 2020. "Tests of Normality of Functional Data," International Statistical Review, International Statistical Institute, vol. 88(3), pages 677-697, December.
    8. Steven Dubnoff, 1992. "Questions and Answers about Stat/Transfer," Stata Technical Bulletin, StataCorp LLC, vol. 1(2).
    9. Kyei, Collins Baffour & Cantah, William Godfred & Junior Owusu, Peterson, 2023. "Effect of commodity prices on financial soundness; insight from adaptive market hypothesis in the Ghanaian setting," Resources Policy, Elsevier, vol. 86(PA).
    10. Manuelita Ureta, 1992. "Data Calculator," Stata Technical Bulletin, StataCorp LLC, vol. 1(2).
    11. Phil Lignier & Diane Jarvis & Daniel Grainger & Taha Chaiechi, 2024. "Spatial Heterogeneity and Subjective Wellbeing: Exploring the Role of Social Capital in Metropolitan Areas Using Multilevel Modelling," Journal of Happiness Studies, Springer, vol. 25(5), pages 1-23, June.
    12. Lawrence C. Hamilton, 1992. "How Robust is Robust Regression?," Stata Technical Bulletin, StataCorp LLC, vol. 1(2).
    13. Amir Abolhassani & Gale Boyd & Majid Jaridi & Bhaskaran Gopalakrishnan & James Harner, 2023. "“Is Energy That Different from Labor?” Similarity in Determinants of Intensity for Auto Assembly Plants," Energies, MDPI, vol. 16(4), pages 1-35, February.
    14. Francesco Danuso, 1992. "Triangle Graphic for Soil Texture," Stata Technical Bulletin, StataCorp LLC, vol. 1(2).
    15. Ted Anagnoson, 1992. "Why is the Cubic Spline So-Called?," Stata Technical Bulletin, StataCorp LLC, vol. 1(2).
    16. Richard E. Deleon & J. Theodore Anagnoson, 1992. "Importing Stata's Graphs Into MS-Word or Wordperfect," Stata Technical Bulletin, StataCorp LLC, vol. 1(2).
    17. Renske E. Kuijpers & Ingmar Visser & Dylan Molenaar, 2021. "Testing the Within-State Distribution in Mixture Models for Responses and Response Times," Journal of Educational and Behavioral Statistics, , vol. 46(3), pages 348-373, June.
    18. Jurgita Arnastauskaitė & Tomas Ruzgas & Mindaugas Bražėnas, 2021. "A New Goodness of Fit Test for Multivariate Normality and Comparative Simulation Study," Mathematics, MDPI, vol. 9(23), pages 1-20, November.
    19. Ngoc Thien Le & Watit Benjapolakul, 2019. "Evaluation of Contribution of PV Array and Inverter Configurations to Rooftop PV System Energy Yield Using Machine Learning Techniques," Energies, MDPI, vol. 12(16), pages 1-13, August.
    20. Richard Goldstein, 1992. "Test for Multivariate Normality," Stata Technical Bulletin, StataCorp LLC, vol. 1(2).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:17:y:2025:i:17:p:7932-:d:1741334. 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.