Damage detection of civil engineering structures relies heavily on the use of outlier analysis/novelty detection analysis. Generally, data captured from a structure in its normal environmental condition are used to create a model and compute control limits to represent the normal range of variations of damage sensitive features of the structure. However, the training database used usually includes outlier measurements, which may introduce masking effect. These outlier measurements can affect the mean and standard deviation/covariance matrix of the training database, and hence, affect the model and the control limits. As a result, small damage may not be detected. Therefore, this paper proposes an approach of selecting a 'clean' training database for the construction of the baseline of the undamaged structure so as to detect damage at an earlier stage. The approach makes use of Principal Component Analysis and Median Absolute Deviation to identify outlier measurements. This approach can be applied before the application of damage detection methods to allow damage to be detected at an earlier stage. The proposed approach is applied to a numerical beam model and the Z24 Bridge, in Switzerland. The results obtained demonstrate that damage can be detected at an earlier stage using the approach proposed in this paper. The proposed method also allows the determination of the model (e.g. linear or nonlinear) to be used for damage detection.

Analyzing changes in vibration properties (e.g. natural frequencies) of structures as a result of damage has been heavily used by researchers for damage detection of civil structures. These changes, however, are not only caused by damage of the structural components, but they are also affected by the varying environmental conditions the structures are faced with, such as the temperature change, which limits the use of most damage detection methods presented in the literature that did not account for these effects. In this article, a damage detection method capable of distinguishing between the effects of damage and of the changing environmental conditions affecting damage sensitivity features is proposed. This method eliminates the need to form the baseline of the undamaged structure using damage sensitivity features obtained from a wide range of environmental conditions, as conventionally has been done, and utilizes features from two extreme and opposite environmental conditions as baselines. To allow near real-time monitoring, subsequent measurements are added one at a time to the baseline to create new data sets. Principal component analysis is then introduced for processing each data set so that patterns can be extracted and damage can be distinguished from environmental effects. The proposed method is tested using a two-dimensional truss structure and validated using measurements from the Z24 Bridge which was monitored for nearly a year, with damage scenarios applied to it near the end of the monitoring period. The results demonstrate the robustness of the proposed method for damage detection under changing environmental conditions. The method also works despite the nonlinear effects produced by environmental conditions on damage sensitivity features. Moreover, since each measurement is allowed to be analyzed one at a time, near real-time monitoring is possible. Damage progression can also be given from the method which makes it advantageous for damage evolution monitoring.

No abstract

Most damage detection methods developed in the literature cannot give the locations and extent of damages under the presence of varying temperature condition. This is because temperature condition changes the vibration properties of a structure, which are commonly analyzed for damage detection, and temperature gradient throughout the structure makes it difficult to create a baseline for the undamaged structure, as the baseline is generally constructed using features obtained under a wide range of temperature conditions. In this paper, a new insight on how to approach damage detection using only a single temperature condition to create the baseline is proposed. This approach solves the damage detection under changing temperature problem in two stages by first quantifying the change of stiffness of all the elements in a structure due to temperature and damage effects, followed by removing the temperature effect, a global effect, to give the actual damage locations and extent. Using single temperature condition allows new measurements to be compared to a benchmark so that local deviation can be obtained, thus making the damaged elements identifiable. The proposed approach is tested using a beam structure model and a shear building under different gradient temperature conditions, and the results demonstrate that the method successfully eliminates the change in elemental stiffness due to temperature effect and gives correct damage locations and extent. The approach can be implemented with other existing damage detection methods that did not consider the effect of temperature so that structures under varying temperature condition can be analyzed.