Integrating Self-Sensing in Self-Healing Concrete: Towards a Biomimetic Approach to Repair

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Abstract

Material degradation of our civil infrastructure is inevitable, and regular maintenance is required to mitigate against failure during the service-life. However, understanding and knowledge of composites is now leading to the creation of concretes with autonomic self-healing capabilities. This development will transform our infrastructure by embedding self-immunity and resilience so that structures evolve over their lifespan enhancing durability and serviceability, improving safety and reducing maintenance costs. Research in the UK under the auspices of Resilient Materials for Life (RM4L) has developed a suite of multiple-scale biomimetic self-healing concretes that can adapt and respond to damage without external intervention. This paper discusses the development of bacteria to precipitate calcite in cracks in concrete. Whilst bacteria-based healing is possible through several pathways, it is only now that a better understanding is permitting the optimization of the process. There are two key technologies for including bacteria healing in concrete: (i) encapsulation and (ii) vascular flow networks. Vascular flow networks permit continuous unlimited delivery of healing agents to internal areas of damage, facilitating repair on a reoccurring basis. However, in order to use them effectively human intervention is required to identify cracking and trigger healing processes. A more biomimetic approach is to provide the concrete with a form of self-sensing capability to enable it to initiate crack healing itself. Research using PZT sensors to detect cracking is described.

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APA

Paine, K., Reeksting, B., Taha, H., & Gebhard, S. (2020). Integrating Self-Sensing in Self-Healing Concrete: Towards a Biomimetic Approach to Repair. In Current Topics and Trends on Durability of Building Materials and Components - Proceedings of the 15th International Conference on Durability of Building Materials and Components, DBMC 2020 (pp. 129–136). International Center for Numerical Methods in Engineering. https://doi.org/10.23967/dbmc.2020.175

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