Fiber Bragg grating (FBG) sensors have attracted growing interest in road health monitoring due to their high sensitivity, accuracy, and resilience to harsh environmental conditions. Continuous monitoring is essential for identifying patterns in the collected data and FBG sensors meet this need by continuously measuring strain across multiple pavement layers at high sampling frequencies, creating an extensive, high-resolution dataset. However, such large data volumes present substantial challenges for transmission, storage, processing, and analysis in structural health monitoring (SHM). To address these challenges, this study introduces a data reduction approach grounded in probability theory. The proposed method utilizes a relative damage assessment framework to minimize the need for full data storage and processing. Instead of analyzing each strain measurement, this approach leverages the distribution of strain events to estimate potential structural changes. By focusing on cumulative strain event counts at specific threshold levels, it identifies shifts in strain distribution patterns that can be an indication of structural changes. Then, the effectiveness of this approach was validated through real-world data from an in-situ (field) monitoring campaign. This streamlined data interpretation process significantly reduces the volume of data for storage and processing, thereby enabling efficient real-time damage assessment of complex infrastructure systems. Overall, the probability-based data reduction method proposed provides a scalable and responsive solution for SHM systems utilizing FBG sensors, particularly in applications requiring dense sensor networks and continuous monitoring. This approach holds promise for enhancing the scalability and efficiency of SHM systems, especially in large-scale infrastructure projects.

Managing and extracting insights from the large volumes of data generated by optical fiber sensor networks is a major challenge. This paper presents an intelligent, scalable framework for real-time road health monitoring using fiber Bragg grating (FBG) sensor data. The proposed framework reduces reliance on manual data handling and cuts storage needs by over 99 % by constructing a compact health indicator (HI). Data preprocessing and fusion reduce volume and variability, while a wavelet scattering network (WSN) extracts damage-sensitive features that are encoded via a long short-term memory (LSTM) autoencoder to represent the health state. Temperature data is integrated to distinguish structural damage from environmental effects. The approach is evaluated through laboratory fatigue tests and synthetic damage data generated from healthy-state field measurements. Results demonstrate accurate, efficient monitoring with potential for edge deployment, enabling low-cost, real-time, long-term structural health management and representing a significant step toward automated, resource-efficient infrastructure maintenance.

Structural health monitoring (SHM) of infrastructure using sensor networks presents significant challenges, particularly for linear structures that require extensive coverage of critical hotspots. Among the various sensing technologies, optical fiber sensors have recently gained attention as a promising solution for distributed and long-span monitoring due to their ability to provide a high density of sensing points. However, the vast amounts of data generated by these sensors create substantial challenges in data handling, processing, management, and analysis. These challenges are further intensified under random loading and unknown conditions, where discerning patterns becomes particularly difficult. To address these issues, this study proposes a probabilistic-based framework for generating a health indicator (HI) through cumulative loading-time analysis of sensor data. The method reduces data dimensionality by calculating cumulative loading time within predefined windows and strain levels, thereby extracting meaningful features by fitting a cumulative distribution function. These features are then used to construct sensor-specific distributions, and Kullback–Leibler divergence is employed to monitor shifts between a trained baseline distribution and the current distribution. This produces the HI, enabling quantitative tracking of distribution shifts caused by structural changes or long-term anomalies. The proposed approach was validated through experimental fatigue tests, in which strain sensors monitored responses during fatigue progression. Results demonstrated the method’s effectiveness in detecting and localizing damage in two scenarios: when damage occurred directly at sensor locations and when it occurred nearby. Furthermore, the method was evaluated using both healthy-state field data and synthetic damage data generated from a fiber Bragg grating sensor network embedded in a roadway. This real-world scenario, characterized by random and unknown-magnitude loading, further validated the method’s robustness and applicability. Overall, the results demonstrate the potential of the proposed framework for practical deployment in SHM systems, offering efficient monitoring using large-scale sensor networks.

The ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients’ readiness, there is still around 15–20% of predictive failure rate. This work is a proof of concept focused on adding new value to the prediction of the weaning outcome. Heart Rate Variability (HRV) and Cardiopulmonary Coupling (CPC) methods are evaluated as new complementary estimates to assess weaning readiness. The CPC is related to how the mechanisms regulating respiration and cardiac pumping are working simultaneously, and it is defined from HRV in combination with respiratory information. Three different techniques are used to estimate the CPC, including Time-Frequency Coherence, Dynamic Mutual Information and Orthogonal Subspace Projections. The cohort study includes 22 patients in pressure support ventilation, ready to undergo the SBT, analysed in the 24 h previous to the SBT. Of these, 13 had a successful weaning and 9 failed the SBT or needed reintubation –being both considered as failed weaning. Results illustrate that traditional variables such as heart rate, respiratory frequency, and the parameters derived from HRV do not differ in patients with successful or failed weaning. Results revealed that HRV parameters can vary considerably depending on the time at which they are measured. This fact could be attributed to circadian rhythms, having a strong influence on HRV values. On the contrary, significant statistical differences are found in the proposed CPC parameters when comparing the values of the two groups, and throughout the whole recordings. In addition, differences are greater at night, probably because patients with failed weaning might be experiencing more respiratory episodes, e.g. apneas during the night, which is directly related to a reduced respiratory sinus arrhythmia. Therefore, results suggest that the traditional measures could be used in combination with the proposed CPC biomarkers to improve weaning readiness.