Textile Reinforced Concrete (TRC) enables the fabrication of lightweight, thin-walled and sustainable structural elements with smart self-sensing functionality. This smart sensing capability arises from the use of high-strength and electrically conductive yarns, which simultaneously serve as the main reinforcement system as well as the sensory agent. The sensory concept is based on the piezoresistive effect which enables the correlation between changes in electrical properties and structural state, usually by adopting the principles of the gauge factor (GF). Previous studies have demonstrated that the integrative GF can be used to quantitatively estimate global structural health. This study aims to correlate localized microstructural mechanism of TRC elements, such as the formation of cracks, by means of GF. The study assumes that the obtained GF will yield information on both the location and the severity of the cracks.

To answer this goal the study proposes investigating local crack phenomena using the Time Domain Reflectometry (TDR) technique. It was demonstrated that the monitoring capability is enabled due to the unique microstructural mechanism of TRC technology. Each yarn consists of thousands of filaments that are bundled together. Due to cracking, the sleeve filaments (located at the outer surface of each carbon yarn) break. The irreversible breakage of these filaments trigger changes in the impedance yielding the monitoring capabilities. However, adopting the TDR technique for carbon yarns is not a straightforward act and some adjustments and pre-procedures are required. The study will discuss these issues.

The investigation will be conducted on two levels: (i) the component level, by testing bare carbon yarns with progressively reducing the number of the active filaments, and (ii) the structural level, by exploring TRC elements. In both cases, the impedance spectrum is the measured variable.

Preliminary results are shown in Fig. 1. Two parallel carbon yarns are connected to a TDR analyzer. The measurements were taken after actively cutting some of the sleeve filaments in the yarns. It is seen that the impedance change is directly correlated to the filament breakage. These preliminary results will be further investigated and extended.

The findings of this study are expected to enhance the understanding of the microstructural mechanisms governing the behavior of TRC structures, thereby contributing to the development of advanced structural health monitoring (SHM) systems.

Fatigue cracking is one of the primary distresses in asphalt pavements, leading to reduced service life and safety risks. Conventional methods for SHM of pavements use embedded or external sensors, which can damage the asphalt and degrade over time. To overcome this limitation, the study adopts the concept of smart asphalt. Smart asphalt integrates electrically conductive carbon fibers (CFs) into conventional asphalt mixtures to create electrically conductive paths within the asphalt. It is assumed that electrical resistance and capacitance, which electrically characterize smart asphalt, reflect strain and damage by the piezoresistive effect. The volume fraction of the CFs is the governing parameter that affects both sensory capability and structural performance. The goal of this study is to explore the optimum CFs’ volume fraction that enhances both structural and sensory capabilities, and to establish a correlation between the two responses.

To answer these goals, the study develops an experimental platform that enables simultaneous mechanical and electrical measurements under indirect tensile (IDT) loading on cylindrical asphalt specimens (100 mm diameter) with varying CFs contents. The electrical setup is based on AC measurements, using two copper electrodes attached to the asphalt specimens. To explore and further characterize the electrical properties of the conductive asphalt, the impedance response spectrum is continuously measured. It is assumed that the equivalent electrical circuit of the asphalt mixture consists of a parallel connection of resistor (R) and capacitor (C). The R and C are evaluated by fitting the parallel RC model to the measured impedance data using nonlinear least-squares optimization in MATLAB.

Preliminary demonstration of the proposed concept is shown for a representative specimen with 1.5% fiber content under monotonic IDT loading, in Fig. 1. The figure presents the load, relative change in electrical resistance, and integrative strain at the center of the specimen versus time. It is observed that the electrical resistance increases due to loading, which is associated with the tensile stresses and the formation and propagation of cracks within the asphalt matrix. As cracks develop, the conductive network of carbon fibers is disrupted, leading to increased resistance. This phenomenon is indicated by rapid change in electrical resistance at specific time points. These results demonstrate the sensory capabilities of the asphalt specimen reinforced with CFs and suggest that crack formation and propagation can be correlated with the electrical measurements.

Structural Health Monitoring (SHM) is widely recognized as an important methodology able to detect damage at an early stage, preventing catastrophic loss. Self-sensing cementitious composites (SSCCs) developed by the incorporation of functionalized multi-walled carbon nanotubes (MWCNTs) into a cement-based matrix have shown significant piezoresistive behavior, making them promising candidates as sensors for SHM applications on Reinforced Concrete structures. However, the effectiveness of MWCNTs in enhancing piezoresistive performance is highly dependent on achieving homogeneous dispersion and establishing a strong interfacial bond with hydration products. The microstructure of cementitious materials has been widely studied in the literature, whereas a limited number of research studies are available that focus on cement-based materials doped with MWCNTs. This study deals with an experimental activity designed to determine the dispersion efficiency of MWCNTs within the cementitious matrix. In particular, the assessment of dispersion efficiency has been carried out by investigating microstructural characteristics and interactions between MWCNTs and hardened cement paste using high-resolution Scanning Electron Microscopy (SEM). A specific methodology has been implemented, achieving promising preliminary results towards a quality control of the composite material. The study underscores the critical role of proper dispersion protocols in tailoring the microstructure and functional performance of MWCNT-doped cementitious composites and assuring optimal quality features to the novel class of sensor devices.

Fractals are geometrical structures characterized by repeating patterns across multiple scales and by distinctive properties such as self-similarity and scale invariance. These features have motivated their use in several engineering fields, including materials science, acoustics, and energy systems, to design efficient multiscale systems. In Structural Health Monitoring (SHM), however, their use has so far been largely confined to the analysis of measured signals through fractal theory and fractal dimension metrics to extract damage-sensitive indices. To the best of the authors’ knowledge, no previous work has specifically addressed the direct design of sensors or self-sensing structural layouts that exploit fractal geometries.

In this context, this study analyzes the dynamic behavior of a fractal-inspired self-similar structure to enhance the performance of diagnostic systems for SHM. Numerical simulations were conducted to analyze its out-of-plane frequency response in nominal conditions. The results show that the structure exhibits frequency bandgaps whose characteristics depend on the order of the self-similar hierarchy. These bandgaps can be exploited to inform SHM algorithms about the presence and size of local damage, as defects introduce additional modes within bandgaps.

Overall, the hierarchical topology of the proposed design gives rise to spectral features that are highly sensitive to local variations across multiple scales, bringing evidence that fractal-inspired self-similar layouts may be leveraged to improve damage diagnosis. These findings represent a promising step toward new SHM technologies that exploit complex physical principles to enable advanced sensing functionalities.