An overview of vibration-based damage detection research at Los Alamos National Laboratory

Abstract

The ability to monitor the structural health of our aging infrastructure is becoming increasingly important. A wide variety of highly effective local nondestructive evaluation tools are available. However, damage identification based upon changes in vibration characteristics is one of the few methods that monitors changes in the structure on a global basis. The material presented herein will summarize the structural health monitoring research that has been conducted at Los Alamos National Laboratory over the last 8 years. First, the process of vibration-based damage detection will be described as a problem in statistical pattern recognition. This process has three portions: (1) data acquisition and cleansing; (2) feature selection and data compression; and (3) statistical model development. Current research regarding feature selection and statistical model development will be emphasized with the application of this technology to large-scale, in situ bridge structures and to bridge columns that were tested in a laboratory. In particular, the ability to quantify uncertainties in measured dynamic properties caused by variable environmental and operational conditions is addressed. The paper will conclude by summarizing some of the underlying technical issues associated with vibration-based global damage detection that continues to make this area of technology a challenging research topic.