A Rolling Dynamic Deflectometer (RDD) is a nondestructive testing device for determining continuous deflection profiles of pavements. Unlike discrete testing methods, the RDD performs continuous measurements. The ability to perform continuous measurements makes the RDD a powerful screening/evaluation tool for quickly characterizing large sections of pavement, with little danger of missing critical pavement features. RDD testing applications have involved pavement forensic investigations, delineations of areas to be repaired, selection of rehabilitation treatments, measurements of relative improvements due to the rehabilitation, and monitoring of changes with time (trafficking and environmental loading). However, the speed of RDD testing with the current rolling sensors is between 1 and 2 mph (1.6 to 3.2 km/hr). Improvements in testing speed and data analysis procedures would increase its usefulness in project-level studies as well as permit its used in some pavement network-level studies. A three-part study was carried out to further improve the RDD. The first part involved the development of speed-improved rolling sensors (referred as the third-generation rolling sensor). Key benefits of this new rolling sensor are: (1) increased testing speed up to 5 mph (8.0 km/hr), and (2) reduced level of rolling noise during RDD measurements. With this rolling sensor, the RDD can collect more deflection measurements at a speed of 3 to 5 mph (4.8 to 8.0 km/hr). Field trials using the first- and third-generation rolling sensors on both flexible and rigid pavements were performed to evaluate the performance of the third-generation rolling sensor. The second part of this study involved enhancements to the RDD data analysis procedure. An alternative data analysis method was developed for the third-generation rolling sensor. This new analysis method produces results at higher speeds that are comparable to the existing analysis method used for testing at 1 to 2 mph (1.6 to 3.2 km/hr). Key benefits of this analysis method that were not previously available are: (1) distance-based deflection profiles (report RDD deflections based on a selected distance interval), (2) improved-spatial resolution without sacrificing the filtering performance, and (3) analysis of the rolling noise characteristics and signal-to-noise and distortion ratios better characterize the deflection profiles and their accuracy. The third part of this study involved investigating the effects of parameters affecting RDD deflection measurements which include: (1) force level and operating frequency, (2) in-situ sensor calibration, (3) load-displacement curve, and (4) pavement temperature variations. These parameters need to be considered in testing and data analysis procedures of the RDD because small errors from these parameters can adversely influence calculations of the RDD deflections. Criteria are presented for selecting the best operating parameters for testing. / text

We discuss two-sample problems and the implementation of a new two-sample data analysis procedure. The proposed procedure is based on the concepts of mid-distribution, design of score functions, components, comparison distribution, comparison density and exponential model. Assume that we have a random sample X1, . . . ,Xm from a continuous distribution F(y) = P(Xi y), i = 1, . . . ,m and a random sample Y1, . . . ,Yn from a continuous distribution G(y) = P(Yi y), i = 1, . . . ,n. Also assume independence of the two samples. The two-sample problem tests homogeneity of two samples and formally can be stated as H0 : F = G. To solve the two-sample problem, a number of tests have been proposed by statisticians in various contexts. Two typical tests are the two-sample t?test and the Wilcoxon's rank sum test. However, since they are testing differences in locations, they do not extract more information from the data as well as a test of the homogeneity of the distribution functions. Even though the Kolmogorov-Smirnov test statistic or Anderson-Darling tests can be used for the test of H0 : F = G, those statistics give no indication of the actual relation of F to G when H0 : F = G is rejected. Our goal is to learn why it was rejected. Our approach gives an answer using graphical tools which is a main property of our approach. Our approach is functional in the sense that the parameters to be estimated are probability density functions. Compared with other statistical tools for two-sample problems such as the t-test or the Wilcoxon rank-sum test, density estimation makes us understand the data more fully, which is essential in data analysis. Our approach to density estimation works with small sample sizes, too. Also our methodology makes almost no assumptions on two continuous distributions F and G. In that sense, our approach is nonparametric. Our approach gives graphical elements in two-sample problem where exist not many graphical elements typically. Furthermore, our procedure will help researchers to make a conclusion as to why two populations are different when H0 is rejected and to give an explanation to describe the relation between F and G in a graphical way.