• Structural Engineering and Mechanics
• /
• v.65 no.2
• /
• pp.129-139
• /
• 2018

Post-tensioning (PT) tendons are commonly used for the assembly of modularized concrete members, and tension is applied to the tendons during construction to facilitate the integrated behavior of the members. However, the tension in a PT tendon decreases over time due to steel corrosion and concrete creep, and consequently, the stress on the anchor head that secures the PT tendon also diminishes. This study proposes an automatic detection system to identify tension reduction in a PT tendon using pulsed-eddy-current (PEC) measurement. An eddy-current sensor is installed on the surface of the steel anchor head. The sensor creates a pulsed excitation to the driving coil and measures the resulting PEC response using the pick-up coil. The basic premise is that the tension reduction of a PT tendon results in stress reduction on the anchor head surface and a change in the PEC intensity measured by the pick-up coil. Thus, PEC measurement is used to detect the reduction of the anchor head stress and consequently the reduction of the PT tendon force below a certain threshold value. The advantages of the proposed PEC-based tension-reduction-detection (PTRD) system are (1) a low-cost (< $ 30), low-power (< 2 Watts) sensor, (2) a short inspection time (< 10 seconds), (3) high reliability and (4) the potential for embedded sensing. A 3.3 m long full-scale monostrand PT tendon was used to evaluate the performance of the proposed PTRD system. The PT tendon was tensioned to 180 kN using a custom universal tensile machine, and the tension was decreased to 0 kN at 20 kN intervals. At each tension, the PEC responses were measured, and tension reduction was successfully detected.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.17-28
• /
• 2022

The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge.

• Proceedings of the Korean Society For Composite Materials Conference
• /
• 2001.05a
• /
• pp.250-253
• /
• 2001

Recently, based on the smart structure concept, optical fiber sensors have been increasingly applied to monitor the various engineering and civil structural components. Repairs based on adhesively bonded fiber reinforce composite patches are more structurally efficient and much less damaging to the parent structure than standard repairs based on mechanically fastened metallic patches. As a result of the high reinforcing efficiency of bonded patches fatigue cracks can be successfully repaired. However, when such repairs are applied to primary structures, it is needed to demonstrate that its loss can be immediately detected. This approach is based on the "smart patch" concept in which the patch system monitors its own health. The objective of this study is to evaluate the potentiality of application of transmission-type extrinsic Fabry-Perot optical fiber sensor (TEFPI) to the monitoring of crack growth behavior of composite patch repaired structures. The sensing system of TEFPI and the data reduction principle for the detection of crack detection are presented. Finally, experimental results from the tests of center-cracked-tension aluminum specimens repaired with bonded composite patch is presented and discussed.