Modeling asphalt pavement overlay transverse cracks using the genetic operation tree and Levenberg-Marquardt Method

[[abstract]]The Artificial Neural Network (ANN) and the nonlinear regression method are commonly used to build models from experimental data. However, the ANN has been criticized for incapable of providing clear relationships and physical meanings, and is usually regarded as a black box. The nonlinear regression method needs predefined and correct formula structures to process parameter search in terms of the minimal sum of square errors. Unfortunately, the formula structures of these models are often unclear and cannot be defined in advance. To overcome these challenges, this study proposes a novel approach, called ââLMGOT,ââ that integrates two optimization techniques: the LevenbergâMarquardt (LM) Method and the genetic operation tree (GOT). The GOT borrows the concept from the genetic algorithm, a famous algorithm for solving discrete optimization problems, to generate operation trees (OTs), which represent the structures of the formulas. Meanwhile, the LM takes advantage of its merit for solving nonlinear continuous optimization problems, and determines the coefficients in the GOTs that best fit the experimental data. This paper uses the LMGOT to investigate the data sets of pavement cracks from a 15-year experiment conducted by the Texas Departments of Transportation. Results show a concise formula for predicting the length of pavement transverse cracking, and indicate that the LMGOT is an efficient approach to building an accurate crack model.[[incitationindex]]SCI[[booktype]]紙

A numerical investigation of wind speed effects on lake-effect storms

Observations of lake-effect storms that occur over the Great Lakes region during late autumn and winter indicate a high sensitivity to ambient wind speed and direction. In this paper, a two-dimensional version of the Penn State University/National Center for Atmospheric Research (PSU/NCAR) model is used to investigate the wind speed effects on lake-effect snowstorms that occur over the Great Lakes region.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42510/1/10546_2004_Article_BF00708966.pd

Implementation of the Developed Quality Acceptance System for Steel Bridge Painting Construction

The purpose of this research is to implement the steel bridge painting quality acceptance system developed in the Joint Highway Research Project HPR-2029-89-27 and to computerize the developed system into inspectors’ daily practice. A training program was conducted. Field experiment of the developed inspection system was initiated immediately right after the inspector training program. The new acceptance system was manually tested. Feedback from INDOT’s inspectors and other personnel were adopted to refine the inspection system. Meanwhile, an interactive computer graphic program was developed to assist the INDOT in designing the double sampling plan and deciding the sample size with controlled risks for both the INDO and painting contractors. In addition, a pen-computer system for painting quality acceptance has been developed. It reduces both the need for inspectors’ statistical background, and tedious manual paper work. The collected data will be more accurate, timely and providing the INDOT with more information to make better decisions. The electronic network system to transfer data between construction sites and the INDOT’s central office was also tested. Once the data are collected in the field, the data inside pen-computers can be transferred to PCs in the central office. The communication between field and office was enhanced. The pen-based computer system developed in this project can be applied to many other highway inspection areas, such as bridges and pavement inspections. Many current inspection works are very time-consuming and may cause errors when the data is re-typed into computers. If the existing inspection forms can be computerized through hand writing recognition technology, the required efforts and paper shuffling can be largely reduced