Self-Healing Geopolymer Concrete for Long-Term Water Tightness in Water-Retaining Structures

Authors

  • Dr. E. Rani
  • D. Ashok kumar
  • Manikandan A
  • Putta Praveen
  • Gundla Srivani
  • Mr. S. Selvakumar
  • Dr. K. Mohan das

DOI:

https://doi.org/10.51483/IJAIML.6.8s.2026.241-251

Keywords:

Geopolymer concrete; self-healing; crack closure; water permeability; water-retaining structures; fly ash; GGBS; durability.

Abstract

Water-retaining structures are often exposed to continuous moisture, wetting and drying, and changes in temperature. Small cracks that develop in these structures can gradually increase water leakage and reduce service life. This study investigates the potential of self-healing geopolymer concrete to reduce water movement through cracks. Fly ash and ground granulated blast-furnace slag (GGBS) are proposed as the main binder materials, while a selected healing agent is incorporated at different dosages. The concrete is evaluated for compressive strength, splitting tensile strength, water absorption, sorptivity, crack closure and water permeability. Controlled cracks are introduced into the specimens and allowed to heal under water and wetting–drying conditions. Crack-width measurements are combined with permeability testing to determine whether visible crack closure also results in improved water tightness. SEM, XRD and FTIR analyses are used to examine changes in the cracked region after healing. The study is intended to identify an appropriate healing-agent dosage that provides a balance between mechanical performance and leakage control. The findings can contribute to the development of geopolymer concrete for tanks, reservoirs and other structures where control of water penetration is important.

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Published

2026-08-01

How to Cite

Rani, D. E., kumar, D. A., A, M., Praveen, P., Srivani, G., Selvakumar, M. S., & das, D. K. M. (2026). Self-Healing Geopolymer Concrete for Long-Term Water Tightness in Water-Retaining Structures. International Journal of Artificial Intelligence and Machine Learning, 6(8s), 241–251. https://doi.org/10.51483/IJAIML.6.8s.2026.241-251