Data-driven method of damage detection using sparse sensors installation by SEREPa

Abstract

This paper presents a model-based method of damage detection and severity identification in structural elements. The model-based method is performed with a finite element model. One of the important challenges in damage identification problems is lack of measured degrees of freedom and limitations of installed sensors on the structures. The new approach of this study is the use of expanded mode shapes data to train artificial neural network (ANN). In this study, the measured mode shapes are expanded by SEREPa. SEREPa expansion is developed based on the System-Equivalent Reduction and Expansion Process (SEREP), which is a non-smooth method and protects the measured data. ANN was then trained through the expanded data as inputs, location and severity of damage as outputs. The algorithm used to train ANN is scaled conjugate gradient. The advantage of this algorithm is that less data storage space is used and lower computation costs are needed. To show SEREPa’s efficiency in estimating unmeasured mode shapes, an experimental example containing a truss tower was presented. Two numerical examples including a plane truss and a space truss were presented to illustrate efficiency of damage detection method. Finally, the proposed method was verified by an experimental example. Damage prediction results for both numerical and experimental examples indicated an acceptable accuracy of the proposed method.