The study aims to develop a self-monitoring platform to detect self-healing processes in textile reinforced concrete (TRC) structures. TRC technology is a promising method for constructing thin-walled concrete elements, as well as for strengthening, repairing, and retrofitting existing RC structural elements. The technology is characterized by high strength, a malleable nature, and resistance to corrosion, resulting in structures that are durable, efficient, and cost-effective. TRC technology is characterized by a unique microstructural mechanism in which distribution of multiple micro-cracks (each less than 100 μm in width) are formed in its design state. The formation of such micro-cracks during their service life enhances self-healing and self-sealing processes, emphasizing their sustainable and durable nature.

State-of-the-art monitoring techniques for healing or sealing process are either based on tests that use visualization platform to determine crack closure; tests that assess the recovery of durability properties by measuring water or gas permeability; or mechanical tests that estimate strength and stiffness. There are several drawbacks of these technologies, including the requirement for prior knowledge of the location of cracked or healed zones; prolonged monitoring process; the random nature of various internal phenomena; and the need to install external or internal devices within the element. Thus, there is an advantage in exploring the self-healing process by an integrated smart-self-sensory system that can simultaneously reflect and monitor the healing process and the associated structural health.

The proposed study offers to leverage the electrical properties of carbon yarns to monitor the healing process and to assess the structural health. The idea is to correlate changes in the electrical properties of the carbon yarns with the condition of the structure. In this configuration, the carbon yarns serve simultaneously as the main reinforcement system and as the sensory agent. The sensory concept is based on changes in the electrical characteristics on of the carbon yarns due to wetting events in cracked zones. In the proposed process, these wetting events serve dual purposes: accelerating the healing process and enabling monitoring. The current study argues that, given the enhancement of the healing in wet environments for cementitious matrices, and considering that the impedance value correlate with the magnitude of water infiltration, which is associated with the severity of the cracks, the sealing process resulting from repeated wetting cycles can be monitored through changes in electrical readings obtained from the carbon yarns.

A preliminary demonstration of the concept is presented in Figure 1. Two rounds of wetting events were performed at micro- and macro cracks, with 28-day interval between them. The impedance changes are shown in the figure. It is clearly seen that the level of the impedance change depends on the crack width. Furthermore, repeated wetting events at the micro-crack led to a further reduction in impedance, demonstrating the sealing process of the crack. These preliminary results will be further discussed and developed.

Biomimetic civil Structural Health Monitoring (SHM) systems have gained great attention over the last years due to their potential to configure smart and resilient infrastructures. With reference to reinforced concrete (RC) structures, these systems can be implemented by means of smart cement-based strain sensors, obtained through the incorporation of conductive nanofillers to cementitious matrices. The conductive filler significantly enhances the piezoresistive behavior of the composite material, which exhibits strain self-sensing capability under external loading, if subjected to an electric field, making it a promising alternative to traditional externally distributed strain sensors, also due to the intrinsic compatibility with RC structures. Despite the increasing research interest, so far, several issues are recognized in the literature as not fully solved, like dispersion of the filler, electrical measuring methods, and calibration. This study deals with an experimental activity aimed at assessing the electromechanical behavior of self-sensing cement-based composites (SSCCs) obtained by adding functionalized multi-walled carbon nanotubes to ordinary cement paste. Quasi-static pure compression cyclic tests have been carried out on standard SSCC specimens, and the self-sensing performance has been assessed.

Over 30% of European bridges are over 50 years old, raising serious safety concerns and maintenance challenges. Despite this urgency, Structural Health Monitoring (SHM) systems remain underutilized due to high costs, complex installation, and limited scalability. Building upon the pioneering work of Camo et al. (2023), this study introduces a novel monitoring technique that uses Cholesteric Liquid Crystal Elastomers (CLCEs), mechanochromic polymers that change color under mechanical strain. Applied as a paint on the concrete surface, CLCEs function as passive, 2D spatially-distributed sensors that visually indicate crack formation through localized color shifts. This enables continuous, distributed monitoring without the need for power or embedded electronics.

The project combines CLCE coatings with camera-based monitoring and machine learning to detect and quantify structural damage. This data-driven system supports predictive maintenance and aims to deliver a low-cost, scalable solution for real bridges. To validate feasibility and optimize the system, initial experimental campaigns were carried out.

First, the applicability of CLCE coatings on concrete substrates was confirmed through mechanical testing. The coatings showed clear mechanochromic responses (color shifts) to crack initiation, validating their potential for SHM applications.

Second, a systematic thickness optimization study was performed to enhance sensitivity, durability, and material efficiency. Using a custom metallic frame, coatings ranging from 10 μm to 30 μm were applied, and an optimal thickness range was identified, balancing visibility sensitivity, coating mechanical integrity, and resource use.

Third, a methodology was developed to quantitatively correlate crack openings in concrete specimens with the coating response area, enabling not only detection but also quantification of damage in laboratory setups. The results were validated using digital image correlation, paving the way for its use in SHM applications.

This work contributes to the development of a novel monitoring technique that is scalable, cost-efficient, and environmentally responsible. By validating the coating’s application, optimizing its parameters, and enabling crack quantification, the project moves closer to real-world deployment on aging infrastructure. Next steps include full-scale bridge trials and integration into maintenance strategies under real conditions.