Measuring the displacement of flexible bridges directly is difficult particularly on monumental suspension bridges. Since these bridges cross over sea channels or large rivers, installation of conventional devices for displacement measurement is technically not easy and costly, if not impossible. In this study, real-time displacement measurement of bridges was carried out by means of digital image processing techniques. This is innovative, highly cost-effective and easy to implement, and yet maintains the advantages of dynamic measurement and high resolution. First, the measurement point is marked on the bridge with a target panel of known geometry. A commercially available digital video camcorder is installed on a fixed point some distance from the bridge (e.g. on the coast) or on a pier (abutment) of the bridge which can be regarded as a fixed point. The camcorder with a telescopic device installed takes a motion picture of the target marked. Meanwhile, the displacement of the target is calculated using an image processing technique, which requires a target recognition algorithm, projection of the captured image, and calculation of the actual displacement using target geometry and the number of pixels moved. To measure the displacement at multiple locations on the bridge, an effective synchronized vision-based system was developed using master/slave system and wireless data communication. For the purpose of verification, the measured displacement by synchronized vision-based system was compared with the data measured by a contact-type sensor, a linear variable differential transformer (LVDT) from laboratory tests. The displacement measured by the proposed method showed a good agreement with the data from the conventional sensors. A field test on a pedestrian suspension bridge was also carried out to check the feasibility of the proposed system.
Proc. SPIE. 5765, Smart Structures and Materials 2005: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems
KEYWORDS: Signal to noise ratio, Sensors, Interference (communication), Structural health monitoring, Damage detection, Neural networks, Bridges, Chemical elements, Signal detection, Evolutionary algorithms
Two-step identification approach for effective bridge health monitoring is proposed to alleviate the issues associated with many unknown parameters faced in the real structures and to improve the accuracy in the estimate results. It is suitable for on-line monitoring scheme, since the rigorous damage assessment is not always needed to be performed whereas the alarming for potential damage occurrence is to be continuously carried out. In this study, two-step identification approach is incorporated. In the first step for screening potential damaged members, three different methods were utilized: (1) Damage Indicator Method based on the Modal Strain Energy (DIM-MSE), (2) Probabilistic Neural Networks (PNNs), and (3) Neural Networks using Grouping technique (NNs-Gr). Then, in the second step, the conventional neural networks technique is utilized for damage assessment on the screened members. The proposed methods are verified through a field test on the northern-most span of old Hannam Grand Bridge over Han River in Seoul, Korea. The issues on measurement noise, modeling errors and multiple damages are addressed.