Computational Improvement for Expected Sliding Distance of a Caisson-Type Breakwater by Introduction of a Doubly-Truncated Normal Distribution

Computational Improvement for Expected Sliding Distance of a Caisson-Type Breakwater by Introduction of a Doubly-Truncated Normal Distribution
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通过引入双截断正态分布改进沉箱式防波堤的预期滑动距离的计算

DOI:
10.1142/s0578563403000816
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发表时间:
2003
影响因子:
2.4
通讯作者:
T. Takayama
T. Takayama
中科院分区:
工程技术3区
文献类型:
--
作者:
Tae;T. Takayama

文献摘要

被引文献

相似文献

本文的目的是研究沉箱型防波堤预期滑动距离的传统评估方法的有效性,并提出更准确计算预期滑动距离的替代方法。重点讨论了设计过程中出现的不确定因素的考虑方法,以及随机变量种子对期望滑动距离的影响。本文表明,在原正态分布下的模拟结果,由于在实验值或观测值有效的区域之外取了一些随机变量,导致滑动距离异常。在计算过程中,随机变量的种子对期望滑动距离有很大的影响。因此,为了精确计算滑动距离,我们采用了双截尾正态分布代替原来的正态分布,并引入了平均期望滑动距离的概念,作为各种不同种子计算的期望滑动距离的平均值。本文证明了双截尾正态分布和平均期望滑动距离的概念对于精确估计期望滑动距离是非常有效的。
The objectives of this paper are to investigate the validity of conventional evaluation methods for expected sliding distance of a caisson-type breakwater, and to propose alternatives for more accurate computation of the expected sliding distance. In particular, the method for considering uncertain factors, which appear in the design process, and the influence due to the seed of random variable on the expected sliding distance are discussed. The present paper shows that simulated result under the original normal distribution causes abnormal sliding distance because some random variables are taken outside the region where experimental or observed values are valid. It also points out that the seed of random variable largely affects the expected sliding distance in the computation process. Therefore, we have employed a doubly-truncated normal distribution instead of the original one for accurate computation of sliding distance, and we have introduced the concept of average expected sliding distance as the mean value of expected ones computed for various different seeds. The present paper has proven that the employment of the doubly-truncated normal distribution and the concept of average expected sliding distance are very effective on accurate estimation of the expected sliding distance.