The main aim of this study is to introduce a two-step method for damage identification in moment frame connections using a support vector machine (SVM) and differential evolution algorithm (DEA). In the first step, the possibility location of damage in connections is determined through SVM leading to reducing the dimension of the search space. Then, the accurate location and precise amount of damage in connections are determined in the second step via DEA with a high speed. In order to simulate damage in connections, a moment frame is modeled through semi-rigid beam to column connections and the analytical model is used to randomly generate structures with damaged connections as data. Then, SVM is trained and tested using this data, to facilitate natural frequencies are considered as input data and the characteristic of damage in beam to column connections are considered as output data of the network. Now, the possible location of the damage in connections can be determined using the SVM trained. The accurate location and severity of damage are determined by DEA based on the prediction of SVM in the first step. In order to assess the efficiency of the proposed method, two numerical examples are considered with different damage cases and considering noise. A comparative study is also made to judge the performance of the method with that of a work available in the literature. The outcome shows the high efficiency of the proposed method to identify the location and severity of the damage in moment frame connections.

Introduction

Occurrence of damage in structural systems such as buildings, bridges, oil platforms and so on is inevitable in their lifetime. There are many examples of damage in structures that have been led to an overall failure. In order to prevent from spreading the local damage to overall one, it is important to identify and repair damage by inspecting the current status of structures. Damage identification methods are categorized into destructive and non-destructive methods. The destructive methods are not a suitable method for most structures because of their cost and inefficiency, hence, researchers turned to non-destructive methods. One of the most important non-destructive identification methods is based on observing the change in structural responses such as dynamic and static responses. The changes in structures due to damage are shown better by dynamic responses, made the dynamic based methods more popular. The damage identification in structures should be in some way that the location and severity of damage in structures are accurately determined. Over the last few years, various methods have been proposed to identify damage in structural members, however, damage identification in connections has been less studied. This issue in earthquake-zone areas that a localized damage in connections may be led to an overall failure of structure, increases the importance of damage identification in connections. In 2001, a research was carried out by Yun et al. for estimating the joint damage of a steel structure from modal data using a neural network technique.