An improved understanding of the performance of civil structures under static and dynamic loading, and through accurate and reliable spatio-temporal modeling with adequate resolution is of great importance in structural health monitoring (SHM). In this study, a test rig for scaled load tests was developed, consisting of a simply supported beam, which is deformed by hydraulic loading. The rig allows forces to be applied at different positions along the beam. Potential extensions include simulating multi-span bridge behavior or applying dynamic vertical loads. During quasi-static hydraulic loading, temperature and strain were continuously monitored using distributed fiber-optic sensing (DFOS) based on Rayleigh and Brillouin backscattering. In case of Rayleigh backscattering, the optical frequency domain reflectometry (OFDR) technique is used to interpret the signal from the fiber-optic cable with a spatial resolution in 1 mm level. Whereas, for the Brillouin backscattering, the Brillouin optical frequency domain analysis (BOFDA) technique is used to interpret the signal from the fiber-optic cable with a spatial resolution in the range of 200 mm. Strain measurements were obtained utilizing a Fibrasens strain cable. A Solifos BRUsens temperature cable was used to separate temperature-induced effects from the Fibrasens measurements, thereby enabling correction of the strain values. The beam deflection was measured by a terrestrial laser scanner (TLS) of type Zoller+Fröhlich IMAGER 5016A in profile mode. This results in 2D profile data of the vertical deflection with a high spatio-temporal resolution of up to 55 Hz in 1 mm range. A B-spline curve approximation was applied to the 2D profile data to derive a continuous 2D deflection profile. For validation, a laser tracker (LT) was used to perform high-accuracy measurements at the point of maximum deflection. It further supports an in-depth analysis and statistical judgement of the DFOS-based strain measurements. Moreover, the deflection curves derived from DFOS-based strain measurements were compared with deflection curves estimated from PLS using TLS and laser tracker measurements. This integrated monitoring approach provides a comprehensive assessment of structural responses under load conditions, supporting detailed evaluation of the beam behavior and validation of experimental and numerical models. The proposed multimodal monitoring framework enables the establishment of baseline deflection–strain relationships under controlled loading, providing a foundation for detecting abnormal structural responses in future civil engineering applications.

In this paper, we use distributed fiber optic sensing (DFOS) for traffic load monitoring on a newly constructed bridge instrumented with a network of embedded optical fibers. Specifically, the optical fibers are embedded longitudinally in all bridge girders at two depths, forming a looped sensing configuration that enables the measurement of different stress states when a vehicle passes. The measurements presented here were conducted before the bridge was opened to traffic, using a mobile crane weighing approximately 36 metric tons, with the load distributed evenly across three axles. We show that our DFOS system can effectively capture the bridge structural response to the moving mobile crane and provide estimates of axle weight distribution and spacing. Finally, the results indicate that, although the fibers located in the girders beneath the loaded lane are strongly affected by the traffic, the fibers beneath the unloaded lane show only a negligible response, highlighting the ability to separate the effects of vehicles traveling in neighboring lanes.

Rockfall hazards have increasingly drawn global attention due to their devastating impacts on human lives, infrastructure, and the environment. Traditional monitoring approaches for these events typically rely on ground-based radar, video surveillance, and point sensors such as accelerometers and strain gauges. Although these methods provide valuable insights into ground motion and event characteristics, they are often constrained by sparse spatial coverage, limited resolution, susceptibility to environmental interference, and high installation and maintenance expenses. Distributed Acoustic Sensing (DAS) technology mitigates these issues by converting optical fiber cables, extending tens of kilometers, into a continuous array of virtual sensors with meter spatial resolution. Previous studies have demonstrated that DAS can capture high-frequency seismic signals generated by artificially triggered rockfalls, exhibiting strong correlation with conventional seismic records. In this study, we conducted an extensive DAS data collection campaign that captured routine operational signals from various highway segments, diverse vehicle types, and different time periods. The testing section, approximately 1 km long, is situated along an highway in Quzhou, Zhejiang Province, China. Subsequently, we manually simulated rockfall events by allowing wooden stakes of varying weights to fall naturally at different locations, thereby emulating the characteristics of real rockfall incidents. Given the substantial daily sample size and the rigorous requirements for a low false alarm rate, a single model proved insufficient. Therefore, by integrating features from the time-frequency domain and spatial resolution, we fused an audio classification model- PANNs with Self-Attention and Convolution to develop a DAS-based long-range rockfall monitoring and early warning system. This model delivers accurate, real-time monitoring over extensive distances, achieving a recall rate exceeding 92% while maintaining a false alarm rate of only 0.058%. Moreover, the system can provide timely alerts regarding incidents, offer recommendations to road maintenance departments, and significantly mitigate secondary disasters such as traffic accidents and congestion triggered by rockfalls.

Structural health monitoring has traditionally been applied primarily to existing structures that already exhibit damage or load-bearing deficits. In such cases, a customized monitoring concept is typically developed to address the specific condition of the structure. This reactive approach, however, contrasts with the objectives of predictive infrastructure management, which aim to detect changes at an early stage and support long-term planning. For new bridge constructions, a shift toward proactive and integrated monitoring is therefore highly desirable.

Key requirements for such an approach include the permanent availability of pre-installed sensing technology, reliable power supply and data transmission, measurement procedures without traffic disruptions, on-demand data retrieval, and the implementation of a reference or baseline measurement immediately after construction. This baseline allows any subsequent structural changes throughout the lifecycle of the bridge to be identified with high accuracy. Moreover, comprehensive monitoring supports efficient maintenance planning by combining sensor data with traditional inspection results. New bridge structures offer ideal conditions for embedded monitoring systems, as modern, robust, and cost-effective sensors can be installed permanently and non-invasively in all relevant components, including those that will later become difficult to access.

Continuous monitoring provides several key benefits as early detection of damage such as corrosion or cracking, improved planning of maintenance interventions, optimization of inspection intervals, and reduction of costs and traffic restrictions. Furthermore, real structural loads resulting from variable influences such as traffic, temperature, or wind can be recorded and analyzed in detail, supporting fatigue assessments, remaining service life predictions, and the development of digital twins. Additional advantages include the detection of overloads and unauthorized heavy transports, as well as faster structural assessment following exceptional events such as fires or slope failures.

This paper presents the instrumentation of a newly constructed prestressed concrete bridge using distributed fiber optic sensing (DFOS). The fiber optic sensors were integrated into all major components of the structure, including the deck slab, abutments, and all precast girders. The fiber optic sensing cables were designed to allow flexible configuration of measurement sections. During the construction phase, verification measurements and a load test were performed to evaluate installation quality, sensor performance, and data reliability. The results demonstrate that DFOS is a powerful and practical technology for structural health monitoring of new bridge constructions and represents a key enabler for predictive maintenance management.