Long-gauge Fiber Bragg Grating Sensors Research Articles

Experimental study on combined effect of mechanical loads and corrosion using tube-packaged long-gauge fiber Bragg grating sensors

This study presents an experimental investigation on the combined effect of mechanical loads and corrosion using the designed polytetrafluoroethylene tube-packaged fiber Bragg grating (FBG) sensors, as to implement long-gauge FBG (LFBG) sensors in corrosion detection practices for structural health monitoring. A simplified LFBG-based sensing model was proposed for strain measurement in terms of the Bragg wavelength change. Correspondingly, a systematic corrosion assessment strategy was developed to estimate corrosion severity and average corrosion rate. Upon this, the experimental study was performed on epoxy-coated steel specimens embedded with LFBG sensors, where the loading, corrosion, and combined loading–corrosion tests were used to explore the effect of mechanical loads on corrosion behavior. Test results revealed that the specimens subjected to combined conditions exhibited more severe corrosion damage. The maximum mass loss was observed to be 1.82 and 2.43 in percentage under individual corrosion and combined loading–corrosion conditions, respectively. Also, the pit depth under combined conditions was found to develop rapidly in the early stage. The pit depth severity ratio was around 0.2–0.8 during the 67 days of exposure, indicating an evident impact of loading on corrosion severity. Furthermore, the maximum average corrosion rate under combined conditions was found to be 5.66 times that under individual corrosion conditions.

In-situ resin flow monitoring in VaRTM process by using optical frequency domain reflectometry and long-gauge FBG sensors

The vacuum-assisted resin transfer molding (VaRTM) process has attracted increased attention because of its ability to provide fast production times for large structures while maintaining a high quality. However, the occurrence of process-induced defects during the VaRTM process significantly affects the mechanical properties of the composites, resulting in a decrease in the reliability of the process. Therefore, flow monitoring should be performed during the manufacturing process to eliminate process-induced defects, such as dry spots and unimpregnated areas in the preform.Herein, we report a facile method for monitoring resin flow using a distributed strain sensing method during the VaRTM process. For the distributed strain sensing method, optical frequency domain reflectometry (OFDR) and a long-gauge fiber Bragg grating (FBG) sensor were used simultaneously. To analyze the change in the sensor signal according to the resin impregnation state, the strain along the in-plane direction of the preform during the VaRTM process was investigated through a numerical simulation and compared with the strain change measured by the OFDR system. The strain rate from the FBG sensor showed a positive value in the impregnated region but a negative value in the unimpregnated region, which means that the point at which the reversal of the strain rate occurs is the same as the position of the flow front. Thus, not only the flow front over the entire gauge length but also the approximate position and size of the unimpregnated area were accurately monitored.

Convergence deformation monitoring of a shield tunnel based on flexible long-gauge FBG sensors

For subway safety, it is important to monitor the convergence deformation of shield tunnels in the long term. However, traditional monitoring methods are constrained by high cost and technical difficulties. Herein, improvements achieved through use of a new type of optical fiber (OF) sensor are reported. The new type of optical fiber sensor, namely, the flexible long-gauge Fiber Bragg Grating (FBG) sensor, is proposed with descriptions of design, preparation and strain sensing performance. Then, a method of assessing convergence deformation is proposed based on distributed long-gauge strain measurements. Finally, the proposed method is validated with measurements made on two full size specimens of shield segments. It is also concluded that the seam change shows a strong influence on convergence deformation. From static experiments, convergence deformation can be accurately evaluated to satisfy the demands of tunnel management. Considering the additional advantages of distributed sensing, such as sensing distance, stability and durability, the proposed method exhibits broad application prospects.

Damage identification using deep learning and long-gauge fiber Bragg grating sensors.

This study proposes an innovative structural damage identification method using dynamic response measured by long-gauge fiber Bragg grating (FBG) sensors and accelerometers to train deep convolutional neural networks (DCNNs). At the same time, the pre-trained model is applied to another structure through transfer learning (TL) technology. To verify this method, the I-shaped steel beam vibration test was conducted at first. Three types of data, acceleration, wavelength, and the fusion of the former, are utilized to train convolutional neural network (CNN) models, and the models are then tested and compared. In TL, another CNN is pre-trained using FBG data of steel beam. Next, the data of T-shaped reinforced concrete (RC) beam are employed to train the pre-trained model. The performance of the CNN is evaluated by training history and the confusion matrix. The results show the CNN-based damage identification method can classify the damage pattern accurately. The CNN trained by fusion data has both high classification accuracy and faster training speed. TL technology can greatly reduce the training time of other target tasks in the same fields.

Synchronous Identification of Damage and Vehicle Load on Simply Supported Bridges Based on Long-Gauge Fiber Bragg Grating Sensors

Bridge vehicle-load and damage-condition information plays a vital role in the structural analysis and assessment of bridges. This information can be derived from bridge responses (such as displacement, acceleration, and strains). Most existing bridge damage and load identification methods are based on different measurements from both the bridge damage and load subsystems (different sensors and methods) in the literature. Accordingly, more sensors and measuring activities are required; therefore, the costs associated with structural health monitoring tend to rise. This paper proposes a new method for the synchronous identification of damage and vehicle loads on simply supported bridges, which uses just one set of long-gauge fiber Bragg grating (FBG) sensors and reduces the use of sensing devices, sensors, and costs. In the proposed method, the correlations among the peak values of static macrostrain curves, bending stiffness of bridge cross sections, and vehicle loads are established based on the macrostrain influence line theory. A numerical case study of a 30-m simply supported bridge, subject to moving vehicles, is performed to preliminarily validate the effectiveness of the proposed method. In this case, the damage identification error is below 6%, and the load identification error is less than 4%. In addition, a laboratory experiment including a model bridge and car is conducted to further examine the feasibility of this method in engineering practice, and the results show that the model bridge damage and load can be identified synchronously with good accuracies.

Damage Detection and Evaluation for an In-Service Shield Tunnel Based on the Monitored Increment of Neutral Axis Depth Using Long-Gauge Fiber Bragg Grating Sensors.

It is difficult to detect and evaluate the structural damage in a shield tunnel during operation because many traditional techniques based on the observation of vibrations are limited in daily monitoring in tunnels. Thus, the curvature radius of a static longitudinal settlement curve is used to identify the residual health and safety of an in-service shield tunnel. However, there are still two problems. The curvature radius is suitable for a qualitative judgment rather than a quantitative evaluation for longitudinal damage detection. Moreover, the curvature radius, which is calculated from the measured settlements of three neighboring points, gives an average damage degree in a wide scope only and is difficult to use to identify the damage’s precise location. By means of the analysis of three kinds of longitudinal failure modes in a shield tunnel, this paper proposes: (1) a damage detection method based on the monitored increment of the neutral axis depth; and (2) an index to evaluate longitudinal damage. The index is composed of the residual ratios of the equivalent flexural stiffness (HFM1) and the equivalent shear stiffness (HFM3). The neutral axis position and the proposed damage index can be determined using long-gauge Fiber Bragg Grating sensors. Results from numerical simulations show that the deviation between the HFM1 and the true value residual ratio of the equivalent flexural stiffness is no more than 1.7%. The HFM3 is equal to its true value in the entire damage process. A loading experiment for a scaled-down model of a shield tunnel using long-gauge Fiber Bragg Grating sensors indicated that the errors in the HFM1 were no more than 5.0% in the case of early damage development (HFM1 ≥ 0.5). The maximum error did not exceed 9.0% even under severe damage conditions in the model. Meanwhile, the HFM3 also coincided with its true value in the entire testing process.

Application of a Novel Long-Gauge Fiber BraggGrating Sensor for Corrosion Detection via aTwo-level Strategy.

Corrosion of main steel reinforcement is one of the most significant causes of structural deterioration and durability reduction. This research proposes a two-level detection strategy to locate and quantify corrosion damage via a new kind of long-gauge fiber Bragg grating (FBG) sensor. Compared with the traditional point strain gauges, this new sensor has been developed for both local and global structural monitoring by measuring the averaged strain within a long gauge length. Based on the dynamic macrostrain responses of FBG sensors, the strain flexibility of structures are identified for corrosion locating (Level 1), and then the corrosion is quantified (Level 2) in terms of reduction of sectional stiffness of reinforcement through the sensitivity analysis of strain flexibility. The two-level strategy has the merit of reducing the number of unknown structural parameters through corrosion damage location (Level 1), which guarantees that the corrosion quantification (Level 2) can be performed efficiently in a reduced domain. Both numerical and experimental examples have been studied to reveal the ability of distributed long-gauge FBG sensors for corrosion localization and quantification.

Evaluation of Prestress Loss Distribution during Pre-Tensioning and Post-Tensioning Using Long-Gauge Fiber Bragg Grating Sensors.

Prestress loss evaluation in prestressed strands is essential for prestressed structures. However, the sensors installed outside the duct can only measure the total prestress loss. The sensors attached on strands inside the duct also have several problems, such as inadequate durability in an aggressive environment and vulnerability to damage during tensioning. This paper proposes a new installation method for long-gauge fiber Bragg grating (LFBG) sensors to prevent accidental damage. Then the itemized prestress losses were determined in each stage of the pre-tensioning and post-tensioning according to the LFBG measurements. We verified the applicability of the LFBG sensors for prestress monitoring and the accuracy of the proposed prestress loss calculation method during pre-tensioning and post-tensioning. In the pre-tensioning case, the calculated prestress losses had less deviation from the true losses than those obtained from foil-strain gauges, and the durability of the LFBG sensors was better than foil-strain gauges, whereas in post-tensioning case, the calculated prestress losses were close to those derived from theoretical predictions. Finally, we monitored prestress variation in the strand for 90 days. The itemized prestress losses at each stages of post-tensioning were obtained by the proposed calculation method to show the prospect of the LFBG sensors in practical evaluation.

Development of a Bridge Weigh-in-Motion System Based on Long-Gauge Fiber Bragg Grating Sensors

Monitoring the weight of vehicles is essential for policymakers to maintain the condition of bridges. In the group of methods for weighing vehicles, bridge weigh-in-motion (B-WIM) systems are among the most prominent and have several advantages, such as convenience and no interruption to traffic. However, they still have one drawback in that, for most systems, a vehicle’s velocity and wheelbase need to be given in advance. Currently, this problem is usually solved by adding an axle detector, which leads to higher costs. Under such circumstances, this paper presents an alternative B-WIM system to measure a vehicle’s velocity, wheelbase, and axial and gross weight merely based on a single set of long-gauge fiber Bragg grating (FBG) sensors without additional devices. To test the effectiveness of this method under various conditions, a vehicle-bridge interaction (VBI) system simulation was performed based on the result of an indoor experiment. Also, an in situ test on an expressway bridge was performed to initially verify its feasibility. The results showed that this method can achieve its function with good accuracy under various conditions.

Structural health monitoring of maglev guideway PC girders with distributed long-gauge FBG sensors

Summary Health monitoring for maglev guideway PC girders is critical to ensure the safe operation of a high-speed maglev train. A long-gauge fiber Bragg grating (FBG) sensor has been recently developed to achieve the high-precision distributed macro-strain measurements and the capability of dynamic testing. This paper presents an integrated strategy for health monitoring of maglev PC girders equipped with the developed FBG sensors. Using the quasi-static measurements under the maglev train load that is uniformly distributed over the span, the deflection and the flexural stiffness of the PC girder can be evaluated. Using the dynamic measurements of the free vibration after the quick passing of the maglev train, the modal parameters of the girder is determined, based on which the flexural stiffness can be calculated in a different way. Using the long-term quasi-static measurements with no maglev train passing by, the deflection can be estimated due to the other effects such as temperature variation, concrete creep, or prestress loss. If assuming that the prestress loss is the only cause of the long-term deflection, the prestress loss can be determined according to its quantitative relation with the strain variation. Through analytical investigations and field testing on a typical maglev PC girder, the ability of the FBG sensors to evaluate the deflection, stiffness, modal parameters and prestress loss is validated.

Dynamic Method of Neutral Axis Position Determination and Damage Identification with Distributed Long-Gauge FBG Sensors.

The neutral axis position (NAP) is a key parameter of a flexural member for structure design and safety evaluation. The accuracy of NAP measurement based on traditional methods does not satisfy the demands of structural performance assessment especially under live traffic loads. In this paper, a new method to determine NAP is developed by using modal macro-strain (MMS). In the proposed method, macro-strain is first measured with long-gauge Fiber Bragg Grating (FBG) sensors; then the MMS is generated from the measured macro-strain with Fourier transform; and finally the neutral axis position coefficient (NAPC) is determined from the MMS and the neutral axis depth is calculated with NAPC. To verify the effectiveness of the proposed method, some experiments on FE models, steel beam and reinforced concrete (RC) beam were conducted. From the results, the plane section was first verified with MMS of the first bending mode. Then the results confirmed the high accuracy and stability for assessing NAP. The results also proved that the NAPC was a good indicator of local damage. In summary, with the proposed method, accurate assessment of flexural structures can be facilitated.

IDENTIFICATION OF DAMAGE IN REINFORCED CONCRETE COLUMNS UNDER PROGRESSIVE SEISMIC EXCITATION STAGES

Prompt and accurate detection of realistic damage in constructed facilities is critical for effective condition assessment and structural health monitoring. This paper reports the experimental investigations of eccentric reinforced concrete columns mounted onto a shaking table and subject to progressively increasing seismic excitations. The investigation was aimed at studying the changes in the dynamic parameters in order to assess the structural conditions of the concrete columns after each post-seismic stage. The dynamic response of the structure was measured using accelerometers, traditional foil-strain gauges, and long-gauge fiber Bragg grating (FBG) sensors. The post-seismic conditions of the columns were evaluated via vibration-based damage identification methods. Results from this study demonstrate the applicability of specially packaged surface-mounted long-gauge FBG sensors for detecting the initiation and the progression of cracks due to reverse dynamic loads. The concept of modal macrostrain analysis was also introduced to identify and localize mild damage due to the applied seismic excitations of increasing intensities. The performance of the sensors for structural identification is also discussed.