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Wenyu Cao, Benbo Sun and Pengxiao Wang
Rapidly developed deep learning methods, widely used in various fields of civil engineering, have provided an efficient option to reduce the computational costs and improve the predictive capabilities. However, it should be acknowledged that the applicat...
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Weinan Huang, Xiaowen Zhu, Haofeng Xia and Kejian Wu
In wind resource assessment research, mixture models are gaining importance due to the complex characteristics of wind data. The precision of parameter estimations for these models is paramount, as it directly affects the reliability of wind energy forec...
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Yibo Li, Shuaihang Li, Qing Zhang, Binglin Xiao and Yuantao Sun
As more and more container terminals are becoming intelligent, different kinds of sensors are widely installed at different locations of the cranes and collect a large amount of data. In order to effectively utilize and manage these huge amounts of actua...
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Xuyong Chen, Yuanlin Peng, Zhifeng Xu and Qiaoyun Wu
A new reliability estimation method based on partial multiplicative dimensional reduction is proposed for probabilistic and non-probabilistic hybrid structural systems. The proposed method is characterized by decorrelating interval input variables from r...
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Andreas Efstratiadis, Panagiotis Dimas, George Pouliasis, Ioannis Tsoukalas, Panagiotis Kossieris, Vasilis Bellos, Georgia-Konstantina Sakki, Christos Makropoulos and Spyridon Michas
We propose a novel probabilistic approach to flood hazard assessment, aiming to address the major shortcomings of everyday deterministic engineering practices in a computationally efficient manner. In this context, the principal sources of uncertainty ar...
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