Quantifying Uncertainties in Cargo Deck Deflections from Distributed Fiber Optic Strain Data

Abstract

A variety of sensing modalities and sensor technologies are used to monitor the health, assess the condition, and quantify the life span of civil infrastructure. Among the more recent technologies utilized for health monitoring applications are fiber optic sensors, which are emerging as a popular choice for monitoring non-standard structures such as pipelines, off-shore platforms, and marine structures. The marine applications, in particular, have arisen because fiber optics are impervious to corrosion and electro-magnetic interference, and are also shock tolerant. The most current fiber optic technologies can embed numerous sensing points along a single, continuous fiber, creating a so-called distributed sensing system that can contain hundreds to thousands of points, depending on the desired resolution. These distributed sensing systems are typically deployed to measure strain wherein disturbances along the length of the fiber are registered wavelength shifts that can be related back to increments of microstrain.

A naval hovercraft was outfitted with a distributed fiber optic sensing system in which two fibers were placed along the longitudinal and transverse axis of the cargo deck, i.e., the fibers were oriented perpendicular to each other. After the fibers were in place, a series of trucks were loaded onto the cargo deck and the resulting strain profiles were measured by the distributed fiber optic sensing system. The aim of this study is to quantify the deck deflections that correspond to the measured strains. This is accomplished by idealizing the cargo deck as a rectangular plate and using the data to estimate the associated plate model parameters. However, in order to account for measurement noise, as well as the inherent variability of inferences made with different data sets, a hierarchical Bayesian scheme that considers both the model parameter uncertainty and the prediction error variance. Through this hierarchical Bayesian formulation, the posterior distributions of the model parameters are found, and from these distributions, deck deflection envelopes can be created, which allow for further quantification of asset service life.