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 non-linear and non-stationary signals into basis functions called intrinsic
mode functions (IMF). IMFs are monocomponent functions that have well defined instantaneous
frequencies. EMD is a sifting process that is non-parametric 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 and speedier edge detection with the Sobel method.
Geosynthetic materials have found useful applications when unbound aggregates have been placed on cohesive soil with very weak subgrade. They have also been successfully used in retarding reflective cracking in both flexible and composite pavements. There are many applications of geosynthetics in pavement engineering yet there is considerable lack of understanding in the behavior of the material. Geosynthetic materials exhibit very peculiar properties in the area of tensile strength and reinforcement. MEMS are miniature sensing or actuating devices that can interact with other environments (provided no adverse reaction occurs) to either obtain information or alter it. With remote query capability, it appears such devices can be embedded in pavement systems as testing and monitoring tools. The aim of this paper is to propose both field and laboratory methods for monitoring geotextile performance using MEMS.
Rigid pavement structures have uncertainties and variability in their structural layers and components. These variations and uncertainties are seldomly included in performance assessment and evaluation in pavement systems. This paper proposes to use Stochastic Finite Element Method (SFEM) in rigid pavement faulting and load transfer efficiency. The SFEM uses random parameters, as stochastic process namely random fields. These random fields are characterized, quantitatively by spatial functions of statistical moment like the mean, variance and covariance.
A major objective of a Pavement Management System (PMS) is to assist highway managers and engineers in making consistent and cost effective decisions related to maintenance and rehabilitation of pavements. But in many circumstances in PMS decision making, pavement engineers find themselves in a state of uncertainty. In this paper, the application of valuation- based systems and networks is used to provide a framework for representing, solving, and drawing inferences under a PMS decision-making environment. The valuation-based system is a new representation of decision-making and problem solving. The graphical depiction of a valuation-based system is called a valuation network. Valuation networks provide a compact representation emphasizing qualitative features of decision making. A valuation network in PMS decision making is constructed and the key algorithm in solving and making inferences in the network is illustrated.