A reliable prognostics framework is essential to prevent catastrophic failure of bridges due to scour. In the U.S., scour accounts for almost 60% of bridge failures. Currently available techniques in the literature for predicting scour are mostly based on empirical equations and deterministic regression models, like Neural Networks and Support Vector Machines, and do not predict the evolution of scour over time. In this paper, we will discuss a Gaussian process model, which includes Bayesian uncertainty for prediction of time-dependent scour evolution. We will validate the model on the experimental data conducted in four different flumes in different conditions. The robustness of the algorithm will also be demonstrated under different scenarios, like lack of training data and equilibrium scour conditions. The results indicate that the algorithm is able to predict the scour evolution with an error of less than 20% for most of the time, and 5% or less given enough training data.
The development of structural health monitoring techniques leads to the integration of sensing capability within
engineering structures. This study investigates the application of multi walled carbon nanotubes in polymer matrix
composites for autonomous damage detection through changes in electrical resistance. The autonomous sensing
capabilities of fiber reinforced nanocomposites are studied under multiple loading conditions including tension loads.
Single-lap joints with different joint lengths are tested. Acoustic emission sensing is used to validate the matrix
crack propagation. A digital image correlation system is used to measure the shear strain field of the joint area. The
joints with 1.5 inch length have better autonomous sensing capabilities than those with 0.5 inch length. The
autonomous sensing capabilities of nanocomposites are found to be sensitive to crack propagation and can
revolutionize the research on composite structural health management in the near future.
A methodology based on Lamb wave analysis and time-frequency signal processing has been developed for
damage detection and structural health monitoring of composite structures. Because the Lamb wave signals
are complex in nature, robust signal processing techniques are required to extract damage features. In this
paper, Lamb wave mode conversion is used to detect the damage in composite structures. Matching pursuit decomposition algorithm is used to represent each Lamb wave mode in the time-frequency domain. Results from numerical Lamb wave propagation simulations and experiments using orthotropic composite plate structures are presented. The capability of the proposed algorithm is demonstrated by detecting seeded delaminations in the composite plate samples. The advantages of the methodology include accurate time-frequency resolution, robustness to noise, high computational efficiency and ease of post-processing.