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problem. One reason is the sensitivity to stress corrosion cracking of the prestressing steel. Failure of prestressing wires can result in collapse of the entire structure without premature indication by transverse bending cracks or considerable deformation. The response of a structure to significant number of prestressing wire breaks was studied on a bridge in Brandenburg, Germany. Two thirds of the prestressing wires in concentrated tendons of two girders were cut before the bridge was demolished. Acoustic Emission Analysis was used to detect the wire breaks. Thus, the number of wire breaks was correlated with results of other measurement techniques, in particular strain measurements on the girders. In preparation of the measurements, the acoustic properties of the bridge were determined and the suitability of Schmidt hammer impacts as an acoustic reference source was validated.
Nowadays, civil engineers have applied prestressing technique to long span bridges to enhance the structural strength and to improve the structural performance in their life time.
However, it‘s not easy to avoid prestress loss during the construction or in service time. This problem will influence the function of the structures and worse make our life in danger.
Therefore, it‘s important to focus on the topic „bridge monitoring“ to make sure the structural property is "health". This presentation introduces the work which fulfills the research goal - birdge monitoring. A large-scale prestressed concrete bridge model was considered in this work. We obteined various damaged condition due to the different prestressed levels which we can change every time in the experiment.
The experimental measurement in two scales were demonstrated by ultrasonics and natural vibration of the bridge.
For the last decades vibration based identification of damage on civil Engineering structures has become an important issue for maintenance operations on transport infrastructure.
Research in that field has been rapidly expanding from classic modal Parameter estimation using measured excitation to modern operational monitoring. Here the difficulty is to regard to the specific environmental and operational influence to the structure under observation. In this paper, two methods accounting for statistical and/or operational uncertainties are applied to measurement data of a progressive damage test on a prestressed concrete bridge. On the base of covariance driven Stochastic Subspace Identification (SSI) an algorithm is developed to monitor and automatically compute confidence intervals of the obtained modal parameters. Furthermore, a null space based non-parametric damage detection method, utilizing a statistical χ2 type test is applied to the measurement data. It can be shown that for concrete bridges the proposed methodology is able to clearly indicate the presence of structural damage, if the damage leads to a change of the structural system.
Structural health monitoring with statistical methods during progressive damage test of S101 Bridge
(2014)
For the last decades vibration based damage detection of engineering structures has become an important issue for maintenance operations on transport infrastructure. Research in vibration based structural damage detection has been rapidly expanding from classic modal parameter estimation to modern operational monitoring. Since structures are subject to unknown ambient excitation in operation conditions, all estimates from the finite data measurements are of statistical nature. The intrinsic uncertainty due to finite data length, colored noise, non-stationary excitations, model order reduction or other operational influences needs to be considered for robust and automated structural health monitoring methods. In this paper, two subspace-based methods are considered that take these statistical uncertainties into account, first modal parameter and their confidence interval estimation for a direct comparison of the structural states, and second a statistical null space based damage detection test that completely avoids the identification step. The performance of both methods is evaluated on a large scale progressive damage test of a prestressed concrete road bridge, the S101 Bridge in Austria. In an on-site test, ambient vibration data of the S101 Bridge was recorded while different damage scenarios were introduced on the bridge as a benchmark for damage identification. It is shown that the proposed damage detection methodology is able to clearly indicate the presence of structural damage, if the damage leads to a change of the structural system.