Aging infrastructure requires reliable and efficient monitoring solutions to detect damage before structural failure occurs. Traditional sensing methods are often impractical due to extensive cabling and installation complexity. Externally Bonded Reinforcement (EBR) using fiber-reinforced polymer (FRP) composites is widely applied to strengthen damaged concrete structures, creating an opportunity to integrate sensing functionality directly into the strengthening layer.

This study explores the use of the conductive carbon layer within a mechanochromic FRP composite as a damage-sensing network for FRP-retrofitted concrete beams. Electrical resistance measurements were used to relate the fractional change in resistance (FCR) to structural behavior under flexural loading. The embedded carbon fiber network acts as a distributed sensor capable of detecting strain and cracking within the composite–concrete system.

Experimental results show a clear correlation between applied load, crack development, and changes in electrical resistance. The carbon fiber network exhibits a very high sensitivity to mechanical deformation, with a gauge factor far exceeding that of conventional metal strain gauges. This high sensitivity enables the detection of early-stage damage at low strain levels. In well-bonded specimens, consistent electrical response patterns were observed, including characteristic resistance changes prior to failure, indicating the potential for early warning of critical damage. Poor bonding conditions, however, resulted in inconsistent electrical behavior and reduced reliability.

Overall, the findings demonstrate that electrical resistance measurements in conductive carbon fiber composites offer a promising, integrated approach for global structural health monitoring of FRP-strengthened concrete structures, particularly when adequate bond quality is ensured.

The rapid ageing of civil and mechanical infrastructure has intensified the demand for continuous and autonomous structural health monitoring (SHM). Conventional inspection methods, being periodic, labour-intensive, and data-limited, are increasingly replaced by sensor-based systems that can track different parameters such as dynamic strain under service loads. However, current dynamic strain monitoring systems are challenged by power limitations, calibration instability, and measurement bias under complex service loads. Addressing these challenges requires a new generation of compact, intelligent, and self-reliant sensing nodes capable of accurately detecting dynamic strain.....

With much of the Netherlands’ concrete infrastructure approaching or surpassing its design life in the coming years, reliable and efficient assessment tools are essential to ensure safety and manage maintenance costs effectively. This thesis investigates the effectiveness and reliability of non-destructive testing (NDT) methods for assessing the material condition of aging reinforced concrete viaducts, with a primary case study on the Ardeweg viaduct.
This study is a direct follow-up to Gert Wilgenburg’s 2024 report on the Sluinerweg, which is another viaduct in the ”Liggerkoppen Project”. The end product of his research was a practical methodology for large-scale NDT and DT inspection. This present research aims to further develop this practical methodology by performing another large-scale inspection on the Ardeweg viaduct using various NDT methods, including: Ground Penetrating Radar (GPR), Rebound Hammer (RH), Ultrasonic Pulse Velocity (UPV), Half-Cell Potential (HCP), resistivity, and corrosion current density. The methodology combined the NDT measurements with destructive validation through core compressive strength testing, chloride profiling, carbonation depth analysis, and thin-section microscopy.
Furthermore, two types of rebound hammers (Q- and R-type) were studied to evaluate their inter changeability and variability. Particular attention was given to the effect of surface coatings. SonReb regression models were developed using RH, UPV, and destructive strength data to evaluate their accuracy for strength estimation. The Q hammer is preferred over the older R-type hammer due to its improved accuracy and user experience. The Kristal-Cement-Graniet (KCG) coating applied to parts of the viaduct has a significant effect on the RH results, increasing the variability noticeably and decreasing the mean rebound values obtained. Furthermore, results show that RH testing correlates strongly with compressive strength (2 > 0.9), while UPV ( 2 = 0.51) offers complementary value with lower sensitivity to surface conditions, like carbonation. The SonReb method, while slightly improving accuracy, showed diminishing returns relative to rebound-only models. The implementation of a carbonation correction factor for rebound values shows promise in improving the overall accuracy of the strength estimation models in both RH and SonReb models.
Corrosion risk classification from electrochemical methods aligned well with destructive chloride pro files and visual inspection, particularly in areas identified as high-risk. Half-cell potential and resistivity methods proved consistent and reliable under most field conditions, with HCP showing more consistent readings between devices and locations. The Gecor-10 and Profometer both showed a strong correlation with each other, indicating interchangeability between potential results. The Gecor-10 A sensor showed inconsistencies and more variability in both resistivity and half-cell potential measurements when compared to the other devices, raising questions about the accuracy of its calculation. The use of both 30s and 100s polarization times during inspection could provide more insight into the true value. The chloride profiles made with the RCT method in the 2019 report show a very promising correlation with the more accurate lab titrations when they are corrected for cement content. Its usefulness should be studied further as it could save time and costs.
This study concludes that NDT methods can serve as reliable tools for material condition assessment of aging concrete. Both strength- and corrosion-focused methods showed strong correlation with their destructive counterpart. Valuable practical knowledge has been obtained from the Ardeweg research, which could help shape future maintenance strategies for similar concrete structures.

Despite stringent safety standards, concrete is prone to various forms of deterioration over time, and the occurrence of cracks is not uncommon. Therefore, the detection and monitoring of deformations in concrete are essential to mitigate the risks associated with structural failure. Implementing a real-time Structural Health Monitoring (SHM) system can play a crucial role in identifying early signs of damage, such as corrosion in reinforcement, thus enhancing the efficiency of maintenance and repair interventions and ultimately prolonging the lifespan of the structure. The data collected through SHM techniques also contribute to the validation of design practices employed in the structure and further advancements in the field. In a broader context, the integration of SHM supports the development of sustainable infrastructure, ensuring the longevity and safety of concrete structures.
Along with visual inspection, SHM techniques are deployed to conduct a thorough analysis of structural behaviour. However, many traditional methods involve tedious installation processes. Several techniques utilize a wide spectrum of radiations, such as ultraviolet pulses, infrared radiations, and X-rays, and rely on sophisticated equipment that demands trained personnel for data analysis. In
this study, a novel approach is proposed for SHM by utilizing magnetic fields for
crack monitoring. Currently, this technique is used to monitor cracks in steel structures.
The aim of this work is to explore and adapt this idea to integrate the sensor system into the field of structural health monitoring for concrete structures. The scientific contributions made in this study include the investigation of the effects of crack propagation on the magnetic field, the modeling of the behavior using analytical and numerical methods, the construction of a prototype, the validation of the structural health monitoring technique, and the demonstration of the feasibility of this method.

This dissertation endeavors to introduce a novel supervised Structural Health Monitoring (SHM) methodology for the detection of damage and the prediction of fatigue life in asphalt concrete materials. Grounded in the principles of S-N (strain-number of cycles until failure) curves, this research addresses the intricate task of proficiently monitoring asphalt pavements through the utilization of sensor data, thus optimizing maintenance schedules. The successful implementation of this methodology holds the promise of significant cost savings, primarily by facilitating timely inspections and judicious resource allocation. The comprehensive approach encompasses an extensive literature review, encompassing topics such as asphalt properties, fatigue life, and damage prediction models. It also involves the strategic deployment of piezoelectric sensors, with a specific emphasis on Lead Zirconate Titanate (PZT), as well as four-point bending (4PB) testing. This methodology is further enriched by the incorporation of supervised machine learning techniques for the precise prediction of strain levels, subsequently utilized in fatigue life prediction and damage modeling.