1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Dynamics of structures

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Random Data Analysis And Measurement Procedures

Emerging artificial intelligence methods in structural engineering

This report presents information on the characterization of cementitiously stabilized layers and the properties that influence pavement performance. It also contains recommended performance-related procedures for characterizing these layers and performance-prediction models for incorporation into the mechanistic–empirical pavement analysis methods. Individual chapters highlight pavement distresses of hot-mix asphalt pavements and concrete pavements, laboratory tests and model development, and model calibration. The material contained in the report will be of immediate interest to state materials, pavement, and construction engineers and others involved in the different aspects of pavement design and construction.

Characterization of Cementitiously Stabilized Layers for Use in Pavement Design and Analysis

Few studies have investigated pavement damage resulting from different axle and truck configurations, and they were generally limited to single and tandem axles. The unconfined compression cyclic load test with loading cycles that simulate different axle and truck configurations was used to examine their relative effect on permanent deformation of an asphalt mixture. The laboratory investigation indicates that the rutting damage due to different axle configurations is approximately proportional to the number of axles. However, the effect of rest period between axles groups within a truck configuration is significant. Calculating truck rutting damage by simply summing the vertical permanent deformation corresponding to its constituent axle groups results in erroneous predictions. The application of Miner's rule results in improved truck rutting damage predictions for a wider range of truck configurations, although it does not account for the effect of rest periods within a truck configuration. Fin...

A laboratory investigation of the effect of multiple axle and truck configurations on HMA mixture rutting

It has been widely recognized by both pavement and vehicle engineers that heavy trucks play a major role regarding road damage in the US and Canada. Technical professionals from the PACCAR Technical Center have been members of a team of research scientists and engineers studying the interactions between heavy trucks and pavements since 1988. This research team included members from industry, government, and universities. Represented institutions included the PACCAR Technical Center, the University of Washington, Washington State University, Michigan State University, the University of California at Berkeley, and the Washington State Department of Transportation.

A Demonstration Of The Theory Of Spatial Repeatability

The collection of distress data from video imaging of the pavement surface can provide the location and type of many distresses However, it cannot provide useful information about some distress features such as the magnitude of faulting, breaks and curling in concrete pavements This report describes a simple tool to extract such information through the use of the raw profile data Different methods were used in the study, including wavelets, time frequency and discrete methods The discrete elevation difference method (DED) was selected for faulting and breaks detection, and the discrete slope method (DS) was selected for curling detection To test the validity of the analysis, surveys were conducted on different sites in Michigan The new tool was able to detect the magnitude of faulting, breaks and curling with an R2 of 097 and standard error (SE) of 025 mm The localization was also highly accurate (R2 =099) The results indicate that the methods described above can capture relevant information about these roughness features with reasonable accuracy

https://michigan.gov/documents/mdot/MDOT_Research_Report_RC1505_272122_7.pdf

Development Of A Simple Diagnosis Tool For Detecting Localized Roughness Features

Effect of Sample Size and Methods on Percent within Limits for Quality Control and Assurance

In this paper the writers demonstrate the inclusion of the Long-Term Pavement Performance (LTPP-DATAPAVE 3.0) data within the pavement engineering curriculum at Michigan State University (MSU). The paper presents two examples, one from an undergraduate course on pavement rehabilitation and one from a graduate course on pavement analysis and design. These examples illustrate the use of LTPP data in computing responses, predicting traffic, developing rehabilitation strategies, and predicting performance of both rigid and flexible pavements.

Karim Chatti

Papers

A laboratory investigation of the effect of multiple axle and truck configurations on HMA mixture rutting

A Demonstration Of The Theory Of Spatial Repeatability

Development Of A Simple Diagnosis Tool For Detecting Localized Roughness Features

Effect of Sample Size and Methods on Percent within Limits for Quality Control and Assurance

Use of the Long-Term Pavement Performance Database in the Pavement Engineering Curriculum