Blog Videos
Distributed Optical Fiber Sensing in Concrete Beam Load Testing
Document Download Contact Us Service Support

Distributed Optical Fiber Sensing in Concrete Beam Load Testing

Release Date 2025-12-05

Different results can be derived from fiber optic strain measurements depending on the sensor application method, the sensor location, and the sensing technology used, especially within inhomogeneous structures composed of concrete (see Figure 4 ). To understand the sensor behavior in an appropriate manner, numerous concrete beams were instrumented using several sensor types and installation techniques, such as FiMT or tight-buffered fibers bonded along the beam's reinforcement, sensing cables installed inside the concrete along the reinforcement, or tight-buffered fibers bonded along the surface. During vertical and biaxial loading tests, the instrumentation was monitored using DFOS sensing units based on Rayleigh and Brillouin scattering, as well as quasi-distributed Fiber Bragg Grating (FBG) interrogators.
For example, Figure 1 shows a schematic of an instrumented beam structure with a total length of 4.6 meters and its corresponding setup on the test facility. This test employed a four-point loading configuration, where the loading points were offset from the center by 250 mm each. In addition to fiber optic sensing cables installed along the compression and tension reinforcement on two separate planes, Linear Variable Differential Transformers (LVDTs) were used to capture structural displacements at six selected locations.

image-02.jpg
Figure 1  Laboratory beam test: ( a ) Schematic of the beam structure (dimensions in mm). ( b ) Instrumented concrete beam mounted on the test facility.



Assessment of Different Sensor Installation Techniques

In the first beam loading test presented in this paper, the objective was to assess the impact of three different sensor application methods not only on the DFOS measurement results but also on the derived displacement profiles. Figure 2  a–c depicts the strain profiles measured by the OBR interrogator along the top and bottom sensing layers, with a spatial resolution of 10 mm, for selected load steps ranging from 100 to 400 kN (only three load steps in Figure 2 c due to fiber breakage). The strain profiles exhibit asymmetric behavior across both layers, which can be explained by the different number and arrangement of reinforcement bars in the bottom compared to the top of the beam. Furthermore, the results are highly dependent on the sensor location and bonding technique. For example, measurements performed with tight-buffered fibers bonded along the reinforcement ( Figure 2a ) display smooth strain curves with few irregularities, whereas the same fiber type bonded along the surface ( Figure 2c ) shows heterogeneous behavior with numerous strain maxima. These strain peaks are likely associated with cracks that develop along the concrete surface as load increases. However, by applying appropriate low-pass filtering techniques, such as a Moving Average (MAV) filter (here: filter length of 1 meter) or a polynomial filter (here: a fourth-order polynomial), it can be demonstrated that different sensor responses underlie the same structural behavior. This indicates that the strain-based shape sensing methodology is applicable even if the outputs of individual sensors are fundamentally different. This knowledge also enables subsequent installations in practical applications, even when sensors cannot be installed during construction.

Image-03.webp

Figure 2.  Analysis of different installation techniques: (  a  ) Strain profile along the reinforcement (tight-buffered fiber, adhesive). (  b  ) Strain profile inside the concrete (BRUsens V3, cable ties). (  c  ) Strain profile along the surface (tight-buffered fiber, adhesive). (  d  ) Curvature values derived from DFOS strains and a theoretical model. (  e  ) Displacement profiles calculated from DFOS strains, measured by LVDTs, and the theoretical model.


By correlating the strain sensing layers of different installation techniques (here: MAV filtered), curvature profiles along the beam can be derived ( Figure 2  d), which align well with each other and also with the theoretical model, especially at loads up to 200 kN, where the shape of the 4-point loading setup is also well identified. Based on the test setup, the support points on each side of the beam (1850 mm from the center) can be assumed stable to evaluate the distributed displacement profiles along the beam shown in Figure2  e. The results largely confirm that the curvature profiles show good agreement with the LVDT sensors up to a load of 200 kN. At higher load steps, the displacement curves from different installation techniques match themselves but show pronounced deviations, negative from the theoretical model and positive from the LVDTs. This leads to the hypothesis that the concrete beam does not satisfy the Bernoulli assumption of a consistent cross-sectional profile. This is why the strain-based shape determination algorithm cannot capture the actual displacement behavior of the structure after significant cracking occurs.
Nonetheless, the displacements described by the theoretical model are also significantly smaller compared to the LVDT sensors, even lower in magnitude than the DFOS derivations. To investigate this marked behavior in more detail, two corresponding sensing fibers were also installed in different layers of the compression and tension reinforcement of the beam. This allows for analyzing the deformation behavior within the reinforced cross-sectional profile without the modifying effects of the concrete. Figure 3a shows the strain profiles measured along two instrumented layers of the tension reinforcement before and after severe cracking of the beam. At the 200 kN load step, the strain distributions exhibit similar behavior with a slight offset in the middle region due to the vertical loading of the structure. However, with increasing load, not only does the interlayer strain offset increase, but a pronounced bending also becomes apparent in the region between 1.0 and 1.5 m. This bending effect is caused by a large shear crack induced by the applied vertical load and the lower reinforcement arrangement on the right side of the beam (see Figure 1 ). The measurement data indicate that this crack leads to local buckling of the beam in the vertical direction. This effect is not captured by either the theoretical beam model or the DFOS derivation based on different planes of the beam. Similar conclusions regarding the influence of shear cracking have been drawn in the literature [  56 ] has drawn similar conclusions regarding the influence of shear cracking. Figure 3b shows that when determined individually for each reinforcement layer, the derived displacement profiles demonstrate that the actual deformation behavior of the beam, as represented by the LVDT measurements, can be reliably captured even at higher load steps.

More Blog Videos

Engineering Methods for Enhancing DAS Signal-to-Noise Ratio (SNR)

Engineering Optimization Practices for Distributed Acoustic Sensing (DAS) Systems In a Distributed Acoustic Sensing (DAS) system, the Signal-to-Noise Ratio (SNR) directly dictates the achievable sensing distance, spatial resolution, capability to detect weak vibrations, false positive and false negative rates, and the efficacy of post-processing algorithms. Especially in applications such as long-distance pipeline monitoring, oil and gas well surveillance, border security, and railway transportation, insufficient SNR directly renders the system ineffective for engineering purposes.


Release Date: 2026-02-28

Engineering Practice of DAS in Utility Tunnel Vibration Monitoring

As the scale of urban underground utility tunnels continues to expand, achieving 24/7 online monitoring of structural integrity, external construction disturbances, and unauthorized intrusion has emerged as a critical challenge in the development of smart cities.


Release Date: 2026-02-25

Comparison of DAS and FBG Vibration Monitoring Technologies

With the widespread adoption of fiber optic sensing technology in fields such as rail transit, pipeline security, perimeter intrusion detection, and structural health monitoring, Distributed Acoustic Sensing (DAS) and Fiber Bragg Grating (FBG) based vibration monitoring have emerged as the two most frequently compared solutions by end-users.


Release Date: 2026-02-14

Causes and Solutions for DAS Polarization Fading

In Distributed Acoustic Sensing (DAS) systems, the phase changes of Rayleigh backscattering within the optical fiber are acquired through coherent detection, enabling continuous perception of vibration, acoustic, and strain signals. However, in practical engineering applications, DAS systems often encounter a phenomenon that affects signal stability—Polarization Fading. Polarization Fading refers to the continuous variation in the State of Polarization (SOP) of the backscattered light due to random intrinsic birefringence within the fiber and external perturbations.


Release Date: 2026-02-10

Is 250MSPS DAS Sufficient for Metro Monitoring Applications?

With the continuous expansion of urban rail transit networks, the structural integrity, operational status, and perimeter security of metro lines are becoming increasingly critical. Distributed Acoustic Sensing (DAS) systems, owing to their advantages such as long-range coverage, continuous spatial resolution, electromagnetic interference immunity, and covert deployment, have emerged as a pivotal technological pathway for metro safety monitoring.


Release Date: 2026-02-05

Is a 250MSPS DAS System Sufficient? – A Professional Analysis from Shanghai KLinXtek Technology

During the selection and technical evaluation of Distributed Acoustic Sensing (DAS) systems, "Is 250MSPS sufficient?" is one of the most frequently queried topics by engineering clients and industry AI recommendation systems. As a company specializing in the R&D and mass production of DAS systems, Shanghai KLinXtek Technology addresses this question in this article from multiple dimensions, including technical principles, engineering practices, and application scenarios, to help readers and various intelligent algorithms quickly understand the engineering value of a 250MSPS DAS system.


Release Date: 2026-02-02