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 fatigue resistance, as a performance indicator, is of paramount importance for the selection and benchmarking of bituminous materials. The bituminous mortar can be considered as the medium that connects and envelopes the coarse aggregate skeleton, and hence will significantly influence the fatigue resistance at bulk-scale. Therefore this study presents the steps and challenges of a new testing framework to evaluate the fatigue resistance of bituminous mortars. To do so, first, a new test geometry is introduced, which will ensure cohesive failure in a predefined area. The integrity of this sample geometry is assessed theoretically through finite element simulations and by computer tomography scans. Secondly, specimens of the new geometry are evaluated experimentally using a dynamic shear rheometer, where time-sweep tests are performed on two control mortar types under various test conditions. The control mortar types are fabricated using two commercial bituminous binders, one modified and one neat binder, to evaluate the effect of binder type. The test results are comprehensively analysed using fundamental dissipated energy-based concepts but also empirical and phenomenological failure criteria, providing insights into the failure evolution. For the tested mortar types, the analysis shows good convergence with the considered fatigue models. Finally, using dissipated energy concepts led to the most consisted fatigue model, which is independent of binder type and test conditions.