INVESTIGATIVE COMPARISON OF TANK AND RECOVERED ASPHALT SAMPLES THROUGH PHYSICAL PERFORMANCE TESTING AND CHEMICAL ANALYSIS

Despite the development of improved performance test protocols by renowned researchers, there are still road networks which experience premature cracking and failure. One area of major concern in asphalt science and technology, especially in cold regions in Canada is thermal (low temperature) cracking. Usually right after winter periods, severe cracks are seen on poorly designed road networks. Quality assurance tests based on improved asphalt performance protocols have been implemented by government agencies to ensure that roads being constructed are at the required standard but asphalt binders that pass these quality assurance tests still crack prematurely. While it would be easy to question the competence of the quality assurance test protocols, it should be noted that performance tests which are being used and were repeated in this study, namely the extended bending beam rheometer (EBBR) test, double edge-notched tension test (DENT), dynamic shear rheometer (DSR) test and X-ray fluorescence (XRF) analysis have all been verified and proven to successfully predict asphalt pavement behaviour in the field. Hence this study looked to probe and test the quality and authenticity of the asphalt binders being used for road paving. This study covered thermal cracking and physical hardening phenomenon by comparing results from testing asphalt binder samples obtained from the storage ‘tank’ prior to paving (tank samples) and recovered samples for the same contracts with aim of explaining why asphalt binders that have passed quality assurance tests are still prone to fail prematurely. The study also attempted to find out if the short testing time and automated procedure of torsion bar experiments can replace the established but tedious procedure of the EBBR. In the end, it was discovered that significant differences in performance and composition exist between tank and recovered samples for the same contracts. Torsion bar experimental data also indicated some promise in predicting physical hardening.

Classifying And Predicting Software Security Vulnerabilities based on Reproducibility

Security defects are common in large software systems because of their size and complexity. Although efficient development processes, testing, and maintenance policies are applied to software systems, there are still a large number of vulnerabilities that can remain, despite these measures. Some vulnerabilities stay in a system from one release to the next one because they cannot be easily reproduced through testing. These vulnerabilities endanger the security of the systems. We propose vulnerability classification and prediction frameworks based on vulnerability reproducibility. The frameworks are effective to identify the types and locations of vulnerabilities in the earlier stage, and improve the security of software in the next versions (referred to as releases). We expand an existing concept of software bug classification to vulnerability classification (easily reproducible and hard to reproduce) to develop a classification framework for differentiating between these vulnerabilities based on code fixes and textual reports. We then investigate the potential correlations between the vulnerability categories and the classical software metrics and some other runtime environmental factors of reproducibility to develop a vulnerability prediction framework. The classification and prediction frameworks help developers adopt corresponding mitigation or elimination actions and develop appropriate test cases. Also, the vulnerability prediction framework is of great help for security experts focus their effort on the top-ranked vulnerability-prone files. As a result, the frameworks decrease the number of attacks that exploit security vulnerabilities in the next versions of the software. To build the classification and prediction frameworks, different machine learning techniques (C4.5 Decision Tree, Random Forest, Logistic Regression, and Naive Bayes) are employed. The effectiveness of the proposed frameworks is assessed based on collected software security defects of Mozilla Firefox.