Application of relational database principles for rating bituminous coarse aggregates with respect to frictional performance
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The design approach that is commonly employed to ensure satisfactory skid resistance of bituminous pavement surfaces is to control the quality of coarse aggregates used in pavement construction. Traditionally, state and federal highway agencies have relied on the results of laboratory tests for this purpose. Among the laboratory tests, those commonly used are Polish Value Test, Acid Insoluble Residue Test and Petrographic Analysis. The findings from many research studies indicate that the reliability that can be achieved by using a single laboratory test is poor. In the current research study at Texas Tech, a comprehensive laboratory and field test program was undertaken with the objective of developing an improved procedure for predicting field skid resistance performance of bituminous aggregates. The field test program included monitoring of 55 pavement test sections that were located in various climatic zones within the state of Texas over a 3-year study period. As a part of this monitoring program skid resistance of the pavement at 64 km/h, British pendulum number, and pavement macrotexture were measured. The laboratory test program consisted of complete characterization of the pavement coarse aggregates using the following test methods: Polish Values Test, Magnesium Sulfate Test, LA Abrasion Test, Acid Insoluble Residue Test and Petrographic Analysis. The skid resistance data collected over the 3-year study period was then used to develop a "Skid Performance Rating" for each pavement section. Subsequently appropriate statistical analyses were conducted to develop regression models that related skid performance rating to various laboratory test parameters. The findings revealed that a better correlation is obtained when aggregates are categorized into sub-groups that contain aggregates with similar mineralogical makeup. Accordingly, aggregates were categorized based on percent carbonate minerals and the Acid Insoluble Residue. Statistical regression models were then developed for each aggregate category. As an alternative means, historical data on skid resistance of pavements constructed with a given aggregate can be used in the evaluation of aggregates. This alternative procedure is used by TxDOT to overcome the shortcomings of using laboratory test data. Highway agencies may use either of the above mentioned procedures to evaluate the performance of an aggregate source for use in constructing pavement test sections. A combination of the two approaches mentioned above may result in predicting field skid resistance on pavement surface courses with a greater degree of reliability. However, an approach that uses these two methods involves dealing with a large amount of laboratory and field test data. A user may find it extremely difficult and cumbersome to maintain and use this information to reliably predict the skid resistance of pavement test sections. In order to achieve the objective of faster reliable prediction of aggregate field skid performance, a application tool was developed. This application, 'SKIDRATE', was specifically designed to address the problem of predicting skid resistance on Hot Mix Asphalt Concrete (HMAC) pavement surfaces. SKIDRATE combines Relational Database Management Systems (RDBMS) principles and statistical regression techniques to evaluate aggregate sources to be used in the construction of HMAC pavement surfaces. An Entity-Relationship Data model was used to analyze and design the RDBMS. Important entities and association among the entities were identified along with the respective cardinalities of the association. Primary and foreign keys were determined for the relations in the RDBMS. The relations were normalized to 3NF in most of the situations. The application enables the storage of data about the aggregate source, results of laboratory tests and details of field skid testing. Users of the application can retrieve the required information on any given aggregate source and process the data using the results of a comprehensive statistical regression analysis that is integrated within the application. This integration of database technology and statistical regression analyses facilitates fast, easy and reliable interpretation of the field and laboratory test results. The application can be used as a convenient tool by engineers in transportation departments to evaluate the suitability of an aggregate for use in pavement surface courses.