Development and field application of a multivariate statistical process control framework for health-monitoring of transportation infrastructure

•Multivariate statistical process control to monitor transportation infrastructure.•Control charts for outlier detection, i.e., possible indications extraordinary events.•T2T2 charts to aggregate and jointly monitor evolution of multiple properties.•Sophisticated decomposition techniques used to identify outlier sources.•Empirical analysis and validation using field data from bridge in Hurley, Wisconsin.

We present a two-part multivariate statistical process control framework to support health-monitoring of transportation infrastructure. The first part consists of estimation of regression and ARIMA–GARCH models to explain, predict, and control for common-cause variation in the data, i.e., changes that can be attributed to usual operating conditions, including traffic loads, environmental effects, and damage when present throughout the data. The second part of the framework consists of using multivariate control charts to simultaneously analyze the standardized innovations of the aforementioned models in order to detect possible special-cause or extraordinary events, such as unique/infrequent traffic, weather, or the onset of damage. The proposed approach revolves around construction of T2T2 control charts as a framework to jointly monitor the evolution and contemporaneous correlation of a set of measurements. The approach provides significant practical/computational advantages over individual analysis of multiple structural properties, and addresses technical problems stemming from ignoring the relationships among them.To illustrate the framework, we analyze strain and displacement data from the monitoring system on the Hurley Bridge (Wisconsin Structure B-26-7). Data were collected from 1 April 2010 to 28 June 2012. Analysis of 7 measurement sequences collected over the 27 month planning horizon revealed 6 possible special-cause events. In terms of outlier interpretation, we use Mason–Young–Tracy Decomposition to establish the contribution of (subsets of) the measurements. Also, we link the most significant special-cause events, in terms of magnitude and duration, to unusual changes in weather and traffic. To conclude, we compare the proposed approach and benchmark the empirical results with Principal Component Analysis, perhaps, the most common alternative appearing in the literature.