In this paper, Endurance Time Analysis （ETA） method which is a new time-history based dynamic pushover procedure is introduced and its application in linear analysis of concrete arch dams is investigated. In this me...In this paper, Endurance Time Analysis （ETA） method which is a new time-history based dynamic pushover procedure is introduced and its application in linear analysis of concrete arch dams is investigated. In this method the structure is subjected to gradually intensifying acceleration functions and its performance is evaluated based on the length of the time duration that can satisfy required performance criteria. For this purpose Dez arch dam is selected as case study, fluid-structure interaction is taken into account and F.E. model of the system is excited in three performance levels. ETA method gives an approximation of maximum response at the equivalent target time, resulted from analyzing the system based on natural records. Extracted results are displacement, velocity and acceleration of the crest at crown cantilever. Results show using of ETA method can reduce at least 50% in number of analyses and 70% in total time of analyses at the current case. Furthermore, it is found that although the results of the ETA are not exactly consistent with the results of time-history analyses using real ground motions, errors are reasonable and ETA can identify performance levels of the dam with acceptable accuracy.展开更多
We start with a description of the statistical inferential framework and the duality between observed data and the true state of nature that underlies it. We demonstrate here that the usual testing of dueling hypothes...We start with a description of the statistical inferential framework and the duality between observed data and the true state of nature that underlies it. We demonstrate here that the usual testing of dueling hypotheses and the acceptance of one and the rejection of the other is a framework which can often be faulty when such inferences are applied to individual subjects. This follows from noting that the statistical inferential framework is predominantly based on conclusions drawn for aggregates and noting that what is true in the aggregate frequently does not hold for individuals, an ecological fallacy. Such a fallacy is usually seen as problematic when each data record represents aggregate statistics for counties or districts and not data for individuals. Here we demonstrate strong ecological fallacies even when using subject data. Inverted simulations, of trials rightly sized to detect meaningful differences, yielding a statistically significant p-value of 0.000001 （1 in a million） and associated with clinically meaningful differences between a hypothetical new therapy and a standard therapy, had a proportion of instances of subjects with standard therapy effect better than new therapy effects close to 30%. A ―winner take all‖ choice between two hypotheses may not be supported by statistically significant differences based on stochastic data. We also argue the incorrectness across many individuals of other summaries such as correlations, density estimates, standard deviations and predictions based on machine learning models. Despite artifacts we support the use of prospective clinical trials and careful unbiased model building as necessary first steps. In health care, high touch personalized care based on patient level data will remain relevant even as we adopt more high tech data-intensive personalized therapeutic strategies based on aggregates.展开更多
文摘In this paper, Endurance Time Analysis （ETA） method which is a new time-history based dynamic pushover procedure is introduced and its application in linear analysis of concrete arch dams is investigated. In this method the structure is subjected to gradually intensifying acceleration functions and its performance is evaluated based on the length of the time duration that can satisfy required performance criteria. For this purpose Dez arch dam is selected as case study, fluid-structure interaction is taken into account and F.E. model of the system is excited in three performance levels. ETA method gives an approximation of maximum response at the equivalent target time, resulted from analyzing the system based on natural records. Extracted results are displacement, velocity and acceleration of the crest at crown cantilever. Results show using of ETA method can reduce at least 50% in number of analyses and 70% in total time of analyses at the current case. Furthermore, it is found that although the results of the ETA are not exactly consistent with the results of time-history analyses using real ground motions, errors are reasonable and ETA can identify performance levels of the dam with acceptable accuracy.
文摘We start with a description of the statistical inferential framework and the duality between observed data and the true state of nature that underlies it. We demonstrate here that the usual testing of dueling hypotheses and the acceptance of one and the rejection of the other is a framework which can often be faulty when such inferences are applied to individual subjects. This follows from noting that the statistical inferential framework is predominantly based on conclusions drawn for aggregates and noting that what is true in the aggregate frequently does not hold for individuals, an ecological fallacy. Such a fallacy is usually seen as problematic when each data record represents aggregate statistics for counties or districts and not data for individuals. Here we demonstrate strong ecological fallacies even when using subject data. Inverted simulations, of trials rightly sized to detect meaningful differences, yielding a statistically significant p-value of 0.000001 （1 in a million） and associated with clinically meaningful differences between a hypothetical new therapy and a standard therapy, had a proportion of instances of subjects with standard therapy effect better than new therapy effects close to 30%. A ―winner take all‖ choice between two hypotheses may not be supported by statistically significant differences based on stochastic data. We also argue the incorrectness across many individuals of other summaries such as correlations, density estimates, standard deviations and predictions based on machine learning models. Despite artifacts we support the use of prospective clinical trials and careful unbiased model building as necessary first steps. In health care, high touch personalized care based on patient level data will remain relevant even as we adopt more high tech data-intensive personalized therapeutic strategies based on aggregates.
