Introduction

In recent years, there has been much interest in the aerospace, mechanical and civil engineering research communities in developing structural health monitoring systems that can detect, locate and assess progressive deterioration in structures or abrupt damage induced by extreme loading events such as those that may occur in earthquakes, hurricanes/typhoons or explosions. In the case of civil structures, such as buildings, bridges, offshore platforms or dams, the most commonly utilized approach to locate damage relies on visual inspections, which are labor intensive, often not timely and may miss hidden damage. In the 1994 Northridge Earthquake, for example, fractures were induced in welded beam-column connections in some steel buildings in the Los Angeles area and these fractures were not discovered until several months after the earthquake because the damage was not apparent from visual inspections. As an alternative to visual inspections and localized testing using ultra-sonics, acoustic emissions, x-rays, etc., an SHM (structural health monitoring) system based on measured structural response may be employed. A typical SHM system consists of a distributed set of motion sensors (e.g. accelerometers, velocimeters or fiber-optic strain gauges) connected to a data acquisition system and a conveniently located central processing unit such as a personal computer. In many applications to civil structures, it is desirable to have remote real-time monitoring of a structure and so the processed data must be available on-line through a dedicated communication channel or via an Internet link. The basic idea is to infer damageinduced changes in the physical properties of a structure from changes in the identified dynamic characteristics by applying suitable algorithms to measured structural response quantities. In theory, this allows induced damage to be detected, localized and assessed through structural response monitoring. In practice, implementation of this concept has proved to be very challenging because the inverse problem that must be solved is ill-conditioned for typical operating conditions, that is, results are sensitive to measurement noise and modeling errors. This special issue of the CACAIE journal is devoted to recent research in SHM. It follows another special issue of the journal on the same topic that was published in Vol. 16, No. 1, 2001, with Prof. James T.P. Yao as Guest Editor. The call for papers for the current special issue on SHM was first issued in May 2004 and twenty-six papers were subsequently submitted for possible publication. Each paper was rigorously reviewed anonymously for quality and originality by at least three reviewers. The review process for papers co-authored by a Guest Editor was handled independently by the journal’s Editorin-Chief, Prof. Hojjat Adeli. Seven papers that met the high standards of the journal were finally approved for publication in the special issue. It is hoped that the reader interested in SHM will find these papers informative and useful. We sincerely thank the many reviewers of the submitted papers for their thoughtful reviews and all the authors of the submitted papers for their interest and effort in the special issue. We are also grateful to the Editor-in-Chief, Prof. Hojjat Adeli, for his encouragement and assistance in producing this issue.