Structural Health Monitoring Activities at Los Alamos National Laboratory

Throughout the workshop, the importance of instrumentation for bridges and dams had been pointed out numerous times. Many workshop participants seemed to agree that the recorded data provide invaluable information to assess the integrity and life safety of civil infrastructure, and help to improve the scientific understanding about the dynamic response of structures under extreme events such as earthquakes. The advances in sensing and communication technologies are making the instrumentation of densely spaced sensors not only feasible but also practical. Furthermore, the workshop participants have put recommends to encourage building owners and bridge operators to install more instrumentation. There was, however, very little discussion on how to utilize these immense amounts of data collected. Taking into account the increasing demand for real or near-real time damage assessment, the issues of data mining and interrogation of the expected huge amount of measurement data becomes a very critical issue. This need for quantitative damage detection and assessment has led Los Alamos National Laboratory (LANL) to research into Structural Health Monitoring (SHM) methods that can be applied to complex structures. The SHM process involves the observation of a structure over a period of time using periodically spaced measurements, the extraction of features from these measurements, and the analysis of these features to determine the current state of health of the system. The output of this process is periodically updated information regarding the ability of the structure to continue to perform its desired function in light of the inevitable aging and degradation resulting from the operational environments. Vibration-based damage detection is a tool that is receiving considerable attention from the research community for such monitoring. The basic premise of vibration-based damage detection is that the damage will significantly alter the stiffness, mass or energy dissipation properties of a system, which, in turn, will alter the measured dynamic response of that system. Staff at Los Alamos National Laboratory cast the process of vibration-based structural health monitoring into a statistical pattern recognition paradigm, and this statistical process is composed of four portions: (1) Operational evaluation; (2) Data acquisition and cleansing; (3) Feature selection and data compression, and (4) Statistical model development. More detailed discussion of the statistical pattern recognition paradigm can be found in Farrar et al, 2000. The presentation focuses on the feature extraction and statistical model development aspects of the statistical paradigm. A novel time series analysis procedure is presented to identify the presence …