Microbial-Based Self-Healing Concrete in Railway Engineering: Current Advances and Future Opportunities

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ID: 327520
2026
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Abstract
Abstract Microbial-based self-healing concrete offers a promising approach to extending the service life of railway infrastructure by autonomously repairing cracks through biologically induced mineral precipitation. Key mechanisms are achieved through the metabolic activities of sulfate-reducing bacteria (SRB), nitrate-reducing bacteria (NRB), ureolytic bacteria, fungi, and algae, supported by porous carriers and capsules that protect microbial viability, control activation, and enable targeted delivery of healing agents. Microbial-based self-healing performance has been evaluated through crack closure measurements, water permeability reduction, and recovery of mechanical strength, providing quantitative assessment of healing efficiency. Numerical modeling approaches help identify the governing factors that influence healing performance and enable optimization of design parameters, and machine-learning techniques provide powerful predictive capabilities for self-healing behavior, including crack closure, strength recovery, and reductions in water permeability. Field applications, such as the pioneering deployment in the Vijfeiken Tunnel and self-healing railway station structures, demonstrate significant potential for reducing maintenance needs, improving durability, and lowering environmental impact. However, future research needs to be conducted to maintain microbial viability under harsh environmental conditions, minimize associated costs and environmental impacts, experimentally validate self-healing performance under realistic service conditions, and develop intelligent predictive models to guide large-scale implementation. Effectively addressing these challenges will provide a strategic pathway for the wider adoption of microbial-based self-healing concrete in railway engineering.
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openalex_W7207827637 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors
Journal Intelligent Transportation Infrastructure
Year 2026
DOI
10.1093/iti/liag014
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