Crack evaluation is essential for effective classification of pavement cracks. Digital images of pavement cracks have been analyzed using techniques such as fuzzy set theory and neural networks. Bidimensional empirical mode decomposition (BEMD), a new image analysis method recently developed, can potentially be used for pavement crack evaluation. BEMD is an extension of the empirical mode decomposition (EMD), which can decompose nonlinear and nonstationary signals into basis functions called intrinsic mode functions (IMFs). IMFs are monocomponent functions that have well-defined instantaneous frequencies. EMD is a sifting process that is nonparametric and data driven; it does not depend on an a priori basis set. It is able to remove noise from signals without complicated convolution processes. BEMD decomposes an image into two-dimensional IMFs. The present paper explores pavement crack detection using BEMD together with the Sobel edge detector. A number of images are filtered with BEMD to remove noise, and the residual image analyzed with the Sobel edge detector for crack detection. The results are compared with results from the Canny edge detector, which uses a Gaussian filter for image smoothing before performing edge detection. The objective is to qualitatively explore how well BEMD is able to smooth an image for more effective edge detection with the Sobel method.

A major challenge in sewer and water pipe infrastructure management is determining structural condition scores for pipe assets. The methodology used to determine the condition of pipes should be relevant, repeatable, and validated. Additionally, to be appropriate for use in risk-analysis models, the scale selected to represent the structural condition should be linear. The methodology developed in this paper enables previously developed pipe statistical failure models to be translated into a mean time to failure (MTF) for individual pipes. By using the economic concept of discounting, the MTF is then used to score the structural condition of the pipe, providing an appropriate structural condition score for use in risk-based asset management models. This methodology for determining the structural condition score has several advantages over previously developed models: the results can be easily interpreted and validated, and the resulting scale is linear. Additionally, the system can be implemented for pipe...

Assessing and maintaining track geometry within acceptable limits are key components of railroad infrastructure maintenance operations. Track geometry conditions have a significant influence on rider comfort and safety. To maintain the ride quality and safety of the track, maintenance activities pertaining to track geometry, such as tamping, are performed. Tamping enhances the track geometry quality but fails to return the track geometry to an as-good-as-new condition. Majority of studies have evaluated tamping recovery using deterministic techniques, which assume that tamping recovery is dependent on the track geometry quality prior to tamping. However, they fail to capture the uncertainty of the recovery values. Probabilistic approaches are increasingly being used to account for the uncertainty but fail to model the underlying dependence between the variables, which may exhibit nonlinear dependences such as tail or asymmetric dependence. To accurately model the tamping recovery phenomenon, this research employs the copula models in combining arbitrary marginal distributions to form a joint multivariate distribution with the underlying dependence. Copula models are used to estimate the tamping recovery of track geometry parameters such as surface (longitudinal level), alignment, cross level, gage, and warp.

Transverse cracks are distresses whose initiation and propagation affect the stability and structural integrity of the highway pavement. They are caused by load, moisture, temperature, construction defects, or a combination of these. Cracking leads to inherent pavement defects that are exacerbated by moisture infiltration as well as the formation of other pavement distresses like roughness with their attendant problems to roadway agencies and users. One feature of transverse cracks is the spontaneity of their initiation and the uncertainty of their propagation. As a result, modeling the formation and spread of transverse cracks requires a framework within which uncertainty can be expressed explicitly. This research seeks to use a statistical approach to model the propagation of transverse cracks on road pavements in Delaware. The aim is to investigate how hierarchical Markov-chain Monte Carlo (MCMC) simulation performs in estimating and predicting the spread of transverse cracks without neglecting...