Browsing by Advisor Le, Tien-Thinh
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Buried pipes suffer from various natural and human-related phenomena leading to the bending forces on such structures. The analytical models face obstacles such as the complications in modeling material behavior and the local stress concentration due to the appearance of defects are combined. This causes the accumulative over and under-estimation of pipe capacity due to the idealizations of full plastic stress distribution and location of defects at the most dangerous area, respectively. Consequently, such models are not appropriate in the case that defects are not located on the bending plane. The Finite Element, FE, approach is used to overcome these difficulties with the appearance... |
This study investigates the performance and robustness of regression machine-learning models in the presence of variability in the experimental database. The main objective of this work is to predict the ultimate load of circular concrete-filled steel tubes. The simulations were designed by combining size of the learning dataset, random realizations and prediction models. The variability (i.e. probability density function of each variable) is propagated to the output response through the regression machine-learning models. Results show that such variability must be considered when training and testing regression machine-learning models. The performance and robustness of the prediction... |
