Exploiting and Enhancing (Back)Scattering Fingerprints in Optical Frequency Domain Reflectometry sensing: A potential
strategy for dealing with future smart cities diverse monitoring demands
Name: LEANDRO CASSA MACEDO
Publication date: 16/04/2026
Examining board:
Name |
Role |
|---|---|
| ANSELMO FRIZERA NETO | Coorientador |
| ARNALDO GOMES LEAL JUNIOR | Presidente |
| CARLOS MARQUES | Examinador Externo |
| MARIA JOSE PONTES | Examinador Interno |
| MOISES RENATO NUNES RIBEIRO | Examinador Interno |
Pages
Summary: In recent years, Smart Cities technologies have been used as strategic approaches to decentralized decision-making through the integration of the digital and physical worlds. Structural Health Monitoring and environmental sensing are essential appli-
cations that aim to maintain the safety, efficiency, and operational integrity of modern urban infrastructures. Sensor devices are deployed to collect mechanical, chemical, and physical data to monitor structural conditions, track material degradation, and indicate anomalies. Moreover, the combination of advanced sensing techniques and artificial intelligence algorithms in the engineering sector has high potential to enable intelligent, data-driven decisions in infrastructure management.
The need for comprehensive spatial monitoring has driven the development of continuous-sensing technologies to extract useful features from complex environments. Conventional approaches often rely on discrete point sensors, such as electrical strain gauges and conventional transducers, for structural and physical analysis. However, simultaneous monitoring of extensive structures or multiparameter gradients requires deploying numerous individual devices, which leads to cabling
complexity, data synchronization issues, and high connection density, as well as spatial gaps in the acquired data. Distributed sensing techniques are attractive solutions to overcome these drawbacks. These systems offer advantages in sensor compactness and continuous spatial profiling, enabling accurate monitoring of localized events without omitting intermediate structural behavior. Furthermore, distributed optical fibers can be embedded or externally bonded to structures, providing advantages in scalability and usability.Optical fiber sensors (OFS) offer attractive features for smart infrastructure, including compactness, light weight, and immunity to electromagnetic interference.
Distributed optical fiber sensors based on Optical Frequency Domain Reflectometry (OFDR) offer additional capabilities, utilizing Rayleigh backscattering to achieve sub-millimeter spatial resolution. This doctoral thesis presents a multiparameter distributed sensing framework that combines OFDR techniques with machine learning algorithms and physical models, integrated across diverse measurement scenarios.
Such an approach leads to reliable optical fiber-based solutions that accurately quantify structural deformation, modal dynamics, chemical variations, and biomechanical parameters. The approaches proposed in this work include three-dimensional shape reconstruction using multicore fibers, simultaneous strain and temperature monitoring in concrete, and Singular Value Decomposition for dynamic modal analysis. In addition, this thesis presents the development of specialized transducers, including non-adiabatic tapered fibers for pH sensing, elastomeric encapsulations for liquid level monitoring, and a smart carpet for gait analysis. This broad application spectrum demonstrates the feasibility of high-resolution distributed sensing for continuous monitoring, indicating an important advancement in Smart City ecosystems and enabling the evelopment of multifunctional diagnostic tools.
