%0 Journal Article
%T Application of Artificial Immune System in Structural Health Monitoring
%A Jiachen Zhang
%A Zhikun Hou
%J Journal of Structures
%D 2014
%R 10.1155/2014/709127
%X A large number of methods have been proposed in the area of structural health monitoring (SHM). However, many of them rely on the prior knowledge of structural-parameter-values or the assumption that the structural-parameter-values do not change without damage. This dependence on specific parameter values limits these methods¡¯ applicability. This paper proposes an artificial immune system- (AIS-) based approach for the civil structural health monitoring, which does not require specific parameter values to work. A linear three-floor structure model and a number of single-damage scenarios were used to evaluate the proposed method¡¯s performance. The high success rate showed this approach¡¯s great potential for the SHM tasks. This approach has merits of less dependence on the structural-parameter-values and low demand on the training conditions. 1. Introduction Structural health monitoring (SHM) refers to the process of implementing a strategy to identify the damage in engineering infrastructures. The damage here can be changes to the material and/or geometric properties, boundary conditions, and system connectivity [1]. The specific objectives of SHM systems are to acquire the information about the damage existence, the damage location, the damage type, and the damage severity [2]. An effective SHM system could detect the structural damage in its early stage, well before a catastrophic structural failure, which threatens the safety of people¡¯s life and property. Also, SHM systems provide valuable information for postevent damage assessments and help to develop a condition-based repair priority. Various schemes have been developed to extract the structural health information from the measured structural dynamic response. These techniques include, but are not limited to, modal-based system identification techniques, wavelet-based approaches, neural network-based schemes, Kalman filter methods, statistical approach, and computer-based machine learning techniques [3]. As one of the machine learning techniques, the recently developed artificial immune system (AIS) is an interdisciplinary area, which relates to immunology, computer science, and engineering [4]. The AIS learns from the principles and mechanisms in the biological immune system (BIS) and takes advantage of the tremendous computation power of modern computers. Many algorithms of the AIS have been developed, such as the negative selection algorithm [5], the positive selection algorithm [6], and the clonal selection algorithm [7]. These methods have been successfully applied to many engineering areas
%U http://www.hindawi.com/journals/jstruc/2014/709127/