Damage Identification Results Research Articles

Damage detection in plate-like structures using High-Order mode shape derivatives

This paper investigates the application and reliability of using high-order mode shape derivatives especially, the fourth derivative in damage detection of plate-like structures. Numerical analyses have been carried out for low- and high-order mode shapes of simply supported and cantilever steel plate models. Six scenarios of damages are studied for plate models to represent different damage characteristics. The influence of artificial noise on the damage identification using changes in fourth derivative of mode shapes has been investigated. Based on the numerical studies, it is shown that the fourth derivative of mode shape is promising in detecting and locating structural damage in plate-like structures since it is localized at the damage locations even for a small amount of damage using only one of the mode shapes. Both low- and high-order mode shapes give successful results. Also, damage indices of the fourth derivatives give smoother localization and consequently better damage identification than those of curvature of mode shapes. Furthermore, using high-order modes (which can be measured by advanced sensors) does not improve the results of damage identification using the fourth derivative. Unfortunately, damage detection using changes in fourth derivative of mode shapes is sensitive to measurement noise.

Damage Identification of Offshore Platform Based on D-S Evidence Theory

In order to improve the accuracy of damage identification of offshore platform, structural damage identification method based on D-S evidence theory is proposed. Firstly, the stiffness matrix damage factor iterative method and modal strain energy method are used to identify the structural damage, and give the decision vectors respectively. Then, the decision vectors are imported into the D-S evidence theory formula, the damage probability of every element would be obtained and the damage location would be determined finally. Offshore platform model experiments show that the results of structural damage identification are more accurate and reliable after D-S evidence theory is used.

Damage identification study of a seven-story full-scale building slice tested on the UCSD-NEES shake table

A full-scale seven-story reinforced concrete building section was tested on the UCSD-NEES shake table during the period October 2005–January 2006. The shake table tests were designed to damage the building progressively through four historical earthquake records. At various levels of damage, ambient vibration tests and low-amplitude white noise base excitations with root-mean-square accelerations of 0.03 g and 0.05 g were applied to the building, which responded as a quasi-linear system with parameters evolving as a function of structural damage. Modal parameters (natural frequencies, damping ratios and mode shapes) of the building were identified at different damage levels based on the response of the building to ambient as well as low-amplitude white noise base excitations, measured using DC coupled accelerometers. This paper focuses on damage identification of this building based on changes in identified modal parameters. A sensitivity-based finite element model updating strategy is used to detect, localize and quantify damage at each damage state considered. Three sets of damage identification results are obtained using modal parameters identified based on ambient, 0.03 g, and 0.05 g RMS white noise test data, respectively. The damage identification results obtained in all three cases do not exactly coincide, but they are consistent with the concentration of structural damage observed at the bottom two stories of the building. The difference in the identified damage results is mainly due to the significant difference in the identified modal parameters used in the three cases. The assumption of a quasi-linear dynamic system is progressively violated with increasing level of excitation. Therefore, application of nonlinear FE model updating strategies is recommended in future studies to resolve the errors caused by structural response nonlinearity.

Uncertainty and Sensitivity Analysis of Damage Identification Results Obtained Using Finite Element Model Updating

Abstract: A full‐scale seven‐story reinforced concrete shear wall building structure was tested on the UCSD‐NEES shake table in the period October 2005–January 2006. The shake table tests were designed so as to damage the building progressively through several historical seismic motions reproduced on the shake table. A sensitivity‐based finite element (FE) model updating method was used to identify damage in the building. The estimation uncertainty in the damage identification results was observed to be significant, which motivated the authors to perform, through numerical simulation, an uncertainty analysis on a set of damage identification results. This study investigates systematically the performance of FE model updating for damage identification. The damaged structure is simulated numerically through a change in stiffness in selected regions of a FE model of the shear wall test structure. The uncertainty of the identified damage (location and extent) due to variability of five input factors is quantified through analysis‐of‐variance (ANOVA) and meta‐modeling. These five input factors are: (1–3) level of uncertainty in the (identified) modal parameters of each of the first three longitudinal modes, (4) spatial density of measurements (number of sensors), and (5) mesh size in the FE model used in the FE model updating procedure (a type of modeling error). A full factorial design of experiments is considered for these five input factors. In addition to ANOVA and meta‐modeling, this study investigates the one‐at‐a‐time sensitivity analysis of the identified damage to the level of uncertainty in the identified modal parameters of the first three longitudinal modes. The results of this investigation demonstrate that the level of confidence in the damage identification results obtained through FE model updating, is a function of not only the level of uncertainty in the identified modal parameters, but also choices made in the design of experiments (e.g., spatial density of measurements) and modeling errors (e.g., mesh size). Therefore, the experiments can be designed so that the more influential input factors (to the total uncertainty/variability of the damage identification results) are set at optimum levels so as to yield more accurate damage identification results.

Damage identification of a large-span concrete cable-stayed bridge based on genetic algorithm

The global stability of a structure, the stiffness of its main girder and concrete tower, and the variation of the forces of its stay cables are key issues to the safety assessment of an in-service cable-stayed bridge. The efficiency and rationality of local elaborate non-damage-identification could be enhanced by the primary damage identification of cable-stayed bridges on the basis of periodic detection of the cable force and strain monitor in key sections of the main girder. The genetic algorithms of damage identification for cable-stayed bridges were investigated in this paper on the basis of the monitor data of the cable force and strain in a key section of the main girder. A damage detection program for complex civil structure was generated to implement the identification of damage location and extent. The deterioration of the structure was calculated according to the variation of monitor data. It is demonstrated that the results of damage identification from the parametric finite element method are accurate. The method had been verified using a long-span concrete cable-stayed bridge in Ningbo, which has been in use for the past four years.

Reliability-Based Assessment of Bridges Using Long-Term Monitoring Data

In this paper a method for safety and condition assessment of bridge components based on long-term monitoring data and reliability analysis is proposed with the target to provide quantitative information to bridge managers for decision making on optimizing and prioritizing bridge inspection and maintenance. In the proposed method, the probability density function (PDF) of the load effect is obtained directly from continuous strain measurement, while the PDF of the resistance is determined using the material strength and its variation coefficient prescribed in provisions or obtained by material tests, and further revised with the damage identification results. Then the safety indices of structural components are estimated using the first-order reliability method (FORM), which can be readily used to decide bridge inspection/maintenance strategy because the correspondence between the safety index and the required maintenance action has been established. This method can incorporate the identified structural damage in a probabilistic manner into the safety index evaluation. Case studies of two truss bridge structures have been provided to illustrate the proposed method.

Statistical damage identification of structures with frequency changes

Model updating methods based on structural vibration data have being rapidly developed and applied to detect structural damage in civil engineering. But uncertainties existing in the structural model and measured vibration data might lead to unreliable damage detection. In this paper a statistical damage identification algorithm based on frequency changes is developed to account for the effects of random noise in both the vibration data and finite element model. The structural stiffness parameters in the intact state and damaged state are, respectively, derived with a two-stage model updating process. The statistics of the parameters are estimated by the perturbation method and verified by Monte Carlo technique. The probability of damage existence is then estimated based on the probability density functions of the parameters in the two states. A higher probability statistically implies a more likelihood of damage occurrence. The presented technique is applied to detect damages in a numerical cantilever beam and a laboratory tested steel cantilever plate. The effects of using different number of modal frequencies, noise level and damage level on damage identification results are also discussed.