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Assessing the impact of substituting hypo sludge (paper pulp) in cement and introducing natural fiber in the form of human hair to enhance compressive strength in concrete

  • Vijayan Selvam EMAIL logo , Tholkapiyan Muniyandi , Abiraami Ramakrishnan , Shantha Kumar Kandasamy , Hemalatha Alagar and Hemalatha Balasubramanian
Published/Copyright: April 23, 2025
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Abstract

This study investigates the impact of incorporating hypo sludge (paper pulp) and human hair fiber (HHF) into M25 grade concrete to enhance its compressive strength. The research evaluates the effect of 30 % hypo sludge in cement and introducing 2 % HHF, comparing the results with conventional M25 concrete. After 28 days, compressive strength tests revealed that the modified concrete containing 30 % hypo sludge and 2 % HHF achieved a significantly higher average compressive strength of 32.91 N/mm2, compared to 26.08 N/mm2 for conventional concrete. The statistical analysis, including an independent samples t-test, indicated no significant variance (p = 0.893) between the samples, confirming the reliability of the results. Group statistics showed consistent performance with a standard deviation of 1.67 for the modified concrete. A Bayesian Factor Test, using the Rouder method and assuming unequal variance, revealed a mean difference of 6.8289 N/mm2 with a pooled standard error of 0.53572, yielding a Bayesian factor of 0.000. This provides strong evidence against the null hypothesis, specifying significant improvement in compressive strength. ANOVA confirmed a statistically significant difference in compressive strength between the groups (F = 162.489, p = 0.000). This study highlights the potential of these novel materials for eco-friendly construction applications, providing a promising alternative to conventional methods.


Corresponding author: Vijayan Selvam, Department of Civil Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India, E-mail:

Acknowledgments

The author would like to thank Tholkapiyan Muniyandi for his valuable guidance and support throughout the research process.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: Author VS, and TM is responsible for data gathering, experimental research, software analysis, and paper writing. AR, SKK, HA, and HB was engaged in the manuscript’s development, supervision, software analysis, and critical evaluation.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: The raw data can be obtained on request from the corresponding author.

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Received: 2024-01-08
Accepted: 2025-01-07
Published Online: 2025-04-23
Published in Print: 2025-06-26

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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