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.