Conference Agenda

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.

The design and development of advanced, environmentally friendly, and sustainable concrete structures with integrated monitoring capabilities is one of the challenges of modern society. To address these challenges, the present study aims to investigate intelligent concrete elements by combining short and continuous fiber reinforcement platforms. Previous studies have employed either short, dispersed carbon fibers or continuous carbon yarns for self-monitoring applications in concrete elements. Each configuration serves distinct monitoring purposes and offers different structural capabilities. Short carbon fibers mainly enhance the electrical properties of the concrete matrix by achieving the percolation threshold, whereas continuous carbon yarns function as hybrid internal sensors. The use of short carbon fibers requires external measurement devices and lacks a main reinforcement system to carry tensile stress in the post cracking stage. Conversely, continuous carbon yarns, that are integrated within textile meshes, can serve both as the main reinforcement platform and as the sensory agents. These carbon-based textile reinforced concrete elements combine reinforcement with self-sensing capabilities, offering efficient and environmentally friendly solutions. However, their performance requires electrical connections at both ends to the data acquisition system and is highly dependent on the unique microstructural bond mechanism with the concrete matrix.

The present study aims to overcome the limitations of individual technologies by developing self-sensory hybrid concrete structures that integrate both short and continuous carbon fibers. This concept leverages the synergy between the two reinforcement systems, enabling the hybrid elements to exhibit enhanced sensing performance, a broader range of monitoring capabilities, and improved
structural behavior. Schematic demonstration of the new concept is presented in Fig. 1. In the proposed configuration, we aim to utilize the electrical properties of the carbon-based textile as a smart sensing device for the conductive concrete matrix. From a sensing perspective, this approach allows for a single electrical connection along the element, while from the structural perspective, the textile serves as the main reinforcement system. The study will demonstrate the advantages of this new concept through experimental investigation, designed to explore both mechanical electrical responses of the hybrid system under flexural and uniaxial tensile loadings.

The outcomes of this study will provide valuable insights into the behavior of hybrid fiber systems and contribute to the development of efficient, intelligent concrete elements with integrated self-sensing capabilities.

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.