Theses and Dissertations Collection

The mitigation of Alkali-Silica Reaction (ASR) has become more urgent than ever before, due to the high demand for concrete in an increasingly industrialized world with expanding urban infrastructure. This study analyzes the efficacy of additives in cement aggregate mixtures in stopping or mitigating the Alkali-Silica Reactions that damage the structural integrity of concrete construction. While there is existing research on certain supplementary cementitious materials or SCMs and their role in stopping ASR in concrete, in this research we analyze the effectiveness of Metakaolin, waste glass powder, basalt fiber, and lithium in preventing ASR using the latest and most reliable test methods available. We utilize the ASTM C1260 14-day accelerated mortar bar test for this purpose, and all supplementary materials are tested separately according to ASTM standards. Moreover, concrete properties such as compressive strength, and concrete flow test are also evaluated in order to complement the test results. Our preliminary findings are that Metakaolin when used as a SCM at a ratio higher than 10% (per cement replacement), effectively mitigates ASR in the concrete prisms used for testing. Additionally, it was found that Metakaolin also keeps concrete expansion under the safe level of 0.10%. The test results of the other two SCMs glass powder and basalt fibers showed insignificant mitigation of ASR in concrete. Furthermore, although expansion was slowed, it was not kept under safe levels (0.10% expansion) of the test. Finally, the experimental results point positively to the simultaneous addition of multiple SCMs and additives in cement mixtures to further increase its numerous properties. The rapid development in construction industry induces a large amount of concrete data that are usually measured and analyzed everyday naming that concrete is the second usable material on earth. Concrete is made from numerous ingredients that have huge variability either at the design stage or at the testing stage. The main goal of this paper is to quantify the anomalies and outliers during the design phase of concrete mixtures. Concrete mixtures have various percentages of ingredients such as cement, slag, fly ash, water, superplasticizer, and fine and coarse aggregates. Machine learning and data mining is considered a very thriving topic in many research fields and its implementation in the construction industry still limited. Concrete community is in need for such a tool to produce efficiently designed concrete mixtures. Outliers could occur during the evaluation of samples’ measurements that might include human or system errors. The Local Outlier Factor (LOF) algorithm is the most common method used to determine outliers, however, the LOF has some challenges. In this paper, an anomaly-based outlier detection algorithm called Isolation Forest based on a Sliding window for the Local Outlier Factor (IFS-LOF) algorithm, is proposed to solve the limitations of the LOF in evaluating 1030 concrete mixtures. The proposed algorithm works without any previous knowledge of data distribution and executes the process within limited memory and with minimal computational effort. The evaluation of results proved that the IFS-LOF algorithm is more efficient in detecting the sequence of outliers and provided more efficient accuracy than other state of the art LOF algorithms.