Machine learning algorithms operating in an unsupervised fashion has emerged as promising tools for detecting structural damage in an automated fashion.Its essence relies on selecting appropriate features to train the...Machine learning algorithms operating in an unsupervised fashion has emerged as promising tools for detecting structural damage in an automated fashion.Its essence relies on selecting appropriate features to train the model using the reference data set collected from the healthy structure and employing the trained model to identify outlier conditions representing the damaged state.In this paper,the coefficients and the residuals of the autoregressive model with exogenous input created using only the measured output signals are extracted as damage features.These features obtained at the baseline state for each sensor cluster are then utilized to train the one class support vector machine,an unsupervised classifier generating a decision function using only patterns belonging to this baseline state.Structural damage,once detected by the trained machine,a damage index based on comparison of the residuals between the trained class and the outlier state is implemented for localizing damage.The two-step damage assessment framework is first implemented on an eight degree-of-freedom numerical model with the effects of measurement noise integrated.Subsequently,vibration data collected from a one-story one-bay reinforced concrete frame inflicted with progressive levels of damage have been utilized to verify the accuracy and robustness of the proposed methodology.展开更多
Students in South African Universities come from different socio-cultural backgrounds, countries and high schools. This suggests that these students have different experiences which impact on their levels of grasping ...Students in South African Universities come from different socio-cultural backgrounds, countries and high schools. This suggests that these students have different experiences which impact on their levels of grasping information in class as they potentially use different lenses on tuition. The current practice in Universities in contributing to the academic performance of students includes the use of tutors, the use of mobile devices for first year students, use of student assistants and the use of different feedback measures. What is problematic about the current practice is that students are quitting university in high numbers. In this study, knowledge has been drawn from data through the use of machine learning algorithms. Bayesian networks, support vector machines （SVMs） and decision trees algorithms were used individually in this work to construct predictive models for the academic performance of students. The best model was constructed using SVM and it gave a prediction of 72.87% and a prediction cost of 139. The model does predict the performance of students in advance of the year-end examinations outcome. The results suggest that South African Universities must recognize the diversity in student population and thus provide students with better support and equip them with the necessary knowledge that will enable them to tap into their full potential and thus enhance their skills.展开更多
基金funding provided by the Scientific and Technological Research Council of Türkiye(TÜBİTAK).
文摘Machine learning algorithms operating in an unsupervised fashion has emerged as promising tools for detecting structural damage in an automated fashion.Its essence relies on selecting appropriate features to train the model using the reference data set collected from the healthy structure and employing the trained model to identify outlier conditions representing the damaged state.In this paper,the coefficients and the residuals of the autoregressive model with exogenous input created using only the measured output signals are extracted as damage features.These features obtained at the baseline state for each sensor cluster are then utilized to train the one class support vector machine,an unsupervised classifier generating a decision function using only patterns belonging to this baseline state.Structural damage,once detected by the trained machine,a damage index based on comparison of the residuals between the trained class and the outlier state is implemented for localizing damage.The two-step damage assessment framework is first implemented on an eight degree-of-freedom numerical model with the effects of measurement noise integrated.Subsequently,vibration data collected from a one-story one-bay reinforced concrete frame inflicted with progressive levels of damage have been utilized to verify the accuracy and robustness of the proposed methodology.
文摘Students in South African Universities come from different socio-cultural backgrounds, countries and high schools. This suggests that these students have different experiences which impact on their levels of grasping information in class as they potentially use different lenses on tuition. The current practice in Universities in contributing to the academic performance of students includes the use of tutors, the use of mobile devices for first year students, use of student assistants and the use of different feedback measures. What is problematic about the current practice is that students are quitting university in high numbers. In this study, knowledge has been drawn from data through the use of machine learning algorithms. Bayesian networks, support vector machines （SVMs） and decision trees algorithms were used individually in this work to construct predictive models for the academic performance of students. The best model was constructed using SVM and it gave a prediction of 72.87% and a prediction cost of 139. The model does predict the performance of students in advance of the year-end examinations outcome. The results suggest that South African Universities must recognize the diversity in student population and thus provide students with better support and equip them with the necessary knowledge that will enable them to tap into their full potential and thus enhance their skills.
