Model‐free data interpretation for continuous monitoring of complex structures

The algorithms are designed to learn characteristics of time series generated by sensor data during a period called the initialization phase where the structure is assumed to behave normally. This phase subsequently helps identify those behaviors which can be classified as anomalous. In this way the new methodologies can effectively identify anomalous behaviors without explicit (and costly) knowledge of structural characteristics such as geometry and models of behavior. The methodologies have been tested on numerically simulated elements with sensors at a range of damage severities. A comparative study with wavelet and other statistical analyses demonstrates superior performance for identifying the presence of damage. Civil engineering structures are difficult to model accurately and this challenge is compounded when structures are built in uncertain environments. As consequence, their real behavior is hard to predict; such difficulties have important effects on the reliability of damage detection. Such situations encourage the enhancement of traditional approximate structural assessments through in‐service measurements and interpretation of monitoring data. While some proposals have recently been made, in general, no current methodology for detection of anomalous behavior from measurement data can be reliably applied to complex structures in practical situations. This paper presents two new methodologies for model‐free data interpretation to identify and localize anomalous behavior in civil engineering structures. Two statistical methods (i) moving principal component analysis and (ii) moving correlation analysis have been demonstrated to be useful for damage detection during continuous static monitoring of civil structures.