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

In this paper, some 1,437 pavement sections from ninety-seven projects in Michigan were analyzed to investigate the interaction between pavement swface roughness and distress. The main hypothesis of this research is that an increase in roughness leads to higher dynamic axle loads, which in tum can lead to a tangible acceleration in pavement distress. If this relationship is established, then it will be possible to plan a preventive maintenance (PM) action to smooth the pavement surface. Such a PM action is bound to extend the service life of the pavement by several years. The objectives of this research were to: 1) test the above hypothesis; 2) develop roughness thresholds; and 3) determine the optimal timing of the PM action. The selected projects include all pavement types. The Ride Quality Index (RQJ) and Distress Index (DJ) were used as measures of surface roughness and distress, respectively. The analysis showed good relationships between DI and RQI for rigid and composite pavements; however for flexible pavements there was significant scatter. A logistic function was used to fit the data. Roughness thresholds were determined as the RQI-values corresponding to peak acceleration in distress. In addition, actual surface profiles of 335 in-service pavement sections from thirty-seven projects were used to generate dynamic axle load using the TruckSim® truck simulation program. Good correlations between dynamic axle load and RQI were obtained. Based on these relationships, roughness threshold values were determined for all pavement types. Rigid pavements had higher RQI threshold values than flexible and composite pavements. The results agreed reasonably with those obtained using MDOT PMS distress data.

Effect of surface roughness on truck dynamic loading and pavement damage

Guidelines for Developing Performance-Related Specifications for Rigid Pavement Preservation Treatments: Diamond Grinding

When a pavement section is subjected to preservation or rehabilitation treatments, its surface conditions transfer from one condition state before treatment to another after treatment. This transformation is a function of the condition state before treatment, the treatment type and timing, the project boundaries, and the quality of construction. The pavement surface conditions along a project vary substantially from one point to the next. Hence, a typical pavement project consists of several condition states that will be transferred to another set of condition states due to the treatment. The probability of such transformation of each pavement segment from one condition state to another populates the cells of the treatment transition matrix. In this study, the before and after treatment distributions of 0.1 mile pavement segments along flexible pavement projects in the State of Louisiana that were subjected to asphalt concrete overlay, mill and fill, and single and double chip seals were studied. Treatment transition matrices were formed and analyzed. The results are presented and discussed in this paper. It is shown that treatment transition matrices can be used to study the health of the pavement network, construction quality, the differences in the state-of-the-practice between the various regions within the state, the benefits of the treatments, and to establish the parameters for design-build warranties. It is also shown that the state-of-the-practice for the selection of treatment type is not dependent on the before treatment distribution of the condition states of the 0.1 mile pavement segments.

Pavement Treatment Transition Matrices: Reality Check on Treatment Practices

Mechanistic-Based Parametric Model for Predicting Rolling Resistance of Flexible Pavements

The surface roughness in terms of International Roughness Index (IRI) captures variation in vertical surface elevations along the pavement length. Diamond grinding is a rigid pavement preservation treatment which is typically utilized to increase pavement smoothness by eliminating the surface undulations. The focus of this study is to evaluate the impact of diamond grinding on rigid pavement performance by using the mechanistic-empirical procedure. The effectiveness of a diamond grinding was assessed by evaluating the IRI before and after the treatment. Generally, smoother pavements will have lower IRI, which will result in less vehicle body and axle bounce. Consequently, the pavement will be subjected to reduced dynamic loads which may cause less pavement damage or vice versa. The longitudinal profile and axle load data from the Long-Term Pavement Performance (LTPP) in the SPS-6 experiment were utilized to evaluate the impact of diamond grinding on pavement performance. These experiments were designed to evaluate the effectiveness of rigid pavement rehabilitation treatments. The data for pavement sections from two climatic zones were evaluated before and after diamond grinding. The axle distributions for each pavement section were utilized to generate truck gross-vehicle weight (GVW) distributions. Subsequently, the truck GVW distributions were used to generate truck response on different pavement surface profiles by using TruckSim software. The impact of surface roughness on dynamic loads was evaluated by comparing axle load spectra before and after diamond grinding. The variations in the axle load spectra are used to explain the differences in the predicted pavement performance in terms of cracking, faulting and IRI.

Karim Chatti

Papers

Effect of surface roughness on truck dynamic loading and pavement damage

Guidelines for Developing Performance-Related Specifications for Rigid Pavement Preservation Treatments: Diamond Grinding

Pavement Treatment Transition Matrices: Reality Check on Treatment Practices

Mechanistic-Based Parametric Model for Predicting Rolling Resistance of Flexible Pavements

Impact of Diamond Grinding on Rigid Pavement Performance