Acceleration‐based damage indicators for building structures using neural network emulators
In this paper we propose the use of artificial neural network (ANN) emulators for an acceleration‐based approach to evaluating building structures under earthquake excitation. The input layer of the ANN is a forced vibration, described as ground acceleration and the acceleration data of several floors. The approach is improved by using the acceleration at later time steps as the output of the neural network (NN). This time delay is optimized as a tunable band to provide the most sensitive signals.
Abnormal state detection of building structures based on symbolic time series analysis and negative selection
A symbolization‐based negative selection (SNS) algorithm that combines the advantages of symbolic time series analysis (STSA) and negative selection (NS) is proposed for detecting the abnormal states of building structures. In SNS, no prior knowledge of a structure's abnormal state is needed. Only the response of the structure in a current state is used as input data. In addition, this approach can work even with one sensor, so it is highly practical and flexible. A state sequence histogram (SSH) transformed from raw acceleration data by using STSA can capture the main features of structure dynamics and alleviate the effects of harmful noise. SSHs of the normal and abnormal states of a structure are defined as self and non‐self elements, respectively. A new detector generation strategy and matching mechanism is proposed that makes the procedure more effective, along with guidelines for appropriate parameters in SNS. Numerical simulations and experimental verifications for different abnormal state cases were conducted to demonstrate the feasibility of the proposed method. Copyright © 2013 John Wiley & Sons, Ltd.
A probabilistic framework for detecting and identifying anomalies
This paper develops a probabilistic framework to detect and identify anomalies such as damage in structures. The framework is developed by introducing new terms and definitions with their corresponding mathematical formulation. An advantage of the new framework is that ill-conditioning in the identification problem is avoided and that a clear relation between measurements and modeling is established. Special results are then obtained in the form of bounds that allow for computationally efficient applications. An example application is then presented. The application is to detect and identify part-through cracks in a plate from surface strain measurements. In this application problem, the role of strain gauge size and measurement errors are considered and discussed.