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

random data analysis and measurement procedures is available in our digital library an online access to it is set as public so you can get it instantly. Our book servers spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the random data analysis and measurement procedures is universally compatible with any devices to read.

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

This report presents models for estimating the effects of pavement condition on vehicle operating costs. These models address fuel consumption, tire wear, and repair and maintenance costs and are presented as computational software on the accompanying CD-ROM, CRP-CD-111, to facilitate use. The material contained in the report should be of immediate interest to state pavement, construction, and maintenance engineers; vehicle fleet managers; and those involved in pavement investment decision processes and financial aspects of highway transportation.

Estimating the Effects of Pavement Condition on Vehicle Operating Costs

Fuel consumption costs are an essential part of life-cycle cost analysis. These costs are influenced by vehicle technology, pavement condition, roadway geometrics, environment, speed, and other factors. Many models for the effects of pavement condition on fuel consumption were developed on the basis of data generated years ago in other countries for vehicles that vary substantially from those used currently in the United States. Therefore, new information is needed to help in refining and developing models that would better apply to U. S. conditions. The mechanistic model developed as part of the Highway Development and Management software (HDM-4) is recommended after calibration for predicting fuel consumption. The results of the calibration exercise for U. S. conditions, with field data collected as part of the NCHRP Project 1-45, are presented. The calibrated HDM-4 fuel consumption model was able to predict very adequately the fuel consumption of five different vehicle classes under different operating...

Calibration of HDM-4 Models for Estimating the Effect of Pavement Roughness on Fuel Consumption for U. S. Conditions

Continuous health monitoring of pavement systems using smart sensing technology

Truck axle configurations and weights have changed significantly since the AASHO road study was conducted in the late 1950s and early 1960s. Emerging concerns about the effects of new axle configurations on pavement damage, which is unaccounted for in the AASHTO procedure, have prompted several researchers to investigate the impacts of different axle and truck configurations on pavement performance. However, there is still a need to strengthen the mechanistic findings using field data. In this paper, actual in-service traffic and pavement performance data for flexible pavements in the state of Michigan are considered. Monitored truck traffic data for different truck configurations are used to identify their relative damaging effects on flexible pavements in terms of cracking, rutting, and roughness. The analysis included simple, multiple, and stepwise regression. The results indicated that trucks with multiple axles (tridem or more) appear to produce more rutting damage than those with only single and tandem axles. On the other hand, trucks with single and tandem axles tend to cause more cracking. Pavement roughness results did not show enough evidence to draw a firm conclusion.

/pdf/effect-of-heavy-multiple-axle-trucks-on-flexible-pavement-59xi0ei537.pdf

Karim Chatti

Papers

Estimating the Effects of Pavement Condition on Vehicle Operating Costs

Calibration of HDM-4 Models for Estimating the Effect of Pavement Roughness on Fuel Consumption for U. S. Conditions

Continuous health monitoring of pavement systems using smart sensing technology

Effect of Heavy Multiple Axle Trucks on Flexible Pavement Damage Using In-Service Pavement Performance Data

An intelligent structural damage detection approach based on self-powered wireless sensor data