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Segregation (hot-mix asphalt segregation) is one of the main problems affecting asphalt pavement performance. The early detection is important, but the tests are quite expensive and time-consuming. Th...e visual examination is the cheapest method but too varied in judgement and can rise further problems. In this experiment, we developed machine learning linear and quadratic discriminant analyses to detect/classify segregated and non-segregated pavement asphalt. Six variables were employed: SD only, IR only, MAD only, IR-mean, MAD-mean, IR-mean, MAD-SDmean and IR-SD-mean. The results showed that the complexities of information affect machine learning performance. IR-SD-mean and MAD-SD-mean parameters gave best accuracy performance for training data at 99.2% (LDA)/98.5% (QDA) and testing data at 98.33% (LDA)/95% (QDA) respectively. In general, QDA gave more accuracy performance in comparison to LDA although our data dimension is small.続きを見る
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