Prediction of undrained failure envelopes of skirted circular foundations using gradient boosting machine algorithm

椭圆 包络线(雷达) 算法 嵌入 Boosting(机器学习) 数学 正交性 航程(航空) 几何学 应用数学
作者
Hongzhen Chen,Zhichao Shen,Le Wang,Chongchong Qi,Yinghui Tian
出处
期刊:Ocean Engineering [Elsevier]
卷期号:258: 111767-111767
标识
DOI:10.1016/j.oceaneng.2022.111767
摘要

Skirted circular foundations have been widely used in offshore engineering and are subjected to combined vertical ( V ), horizontal ( H ) and moment ( M ) loading. Undrained load-carrying capacities of skirted circular foundations under combined V–H-M loading were derived from the finite element limit analysis (FELA) in this paper considering a wide range of embedment ratios and soil strength heterogeneity indices. A double-ellipse fitting strategy was proposed and employed to fit numerical results with a unified failure envelope expression. Due to complex interactions between different variables, gradient boosting machine (GBM) algorithm was introduced to learn the relationship between fitting parameters in the failure envelope expression and their influencing variables based on the database constructed by FELA. The results in this study show that the double-ellipse fitting strategy provides comparably accurate and more conservative predictions of failure envelopes compared with existing fitting strategies. The GBM model developed in this study has a good performance in predicting fitting parameters of failure envelopes. The importance of influencing variables and the effect of database size and data orthogonality on the performance of GBM model were discussed. The method based on double-ellipse fitting strategy and GBM algorithm can be implemented in a program to generate failure envelopes conveniently. • A double-ellipse fitting strategy was proposed for failure envelopes fitting. • Gradient boosting machine (GBM) was used to predict failure envelopes under a wide range of boundary conditions. • The failure envelopes were well predicted and the embedment was the most important influencing variables. • Effect of database size and data orthogonality on the performance of GBM model were discussed.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
及禾发布了新的文献求助10
刚刚
orixero应助i1采纳,获得10
1秒前
殷勤的涵梅完成签到 ,获得积分10
1秒前
1秒前
LIKO发布了新的文献求助10
1秒前
weiye1992完成签到,获得积分10
1秒前
英姑应助汤一德采纳,获得30
2秒前
科研通AI6应助肉song小贝采纳,获得10
2秒前
2秒前
2秒前
大眼睛的草莓完成签到,获得积分10
2秒前
2秒前
2秒前
skmksd完成签到,获得积分10
2秒前
弈心完成签到 ,获得积分10
3秒前
脑洞疼应助鲤鱼天奇采纳,获得30
3秒前
季博常完成签到,获得积分10
4秒前
SciGPT应助朝气采纳,获得10
4秒前
科研通AI6应助yuyu采纳,获得10
4秒前
跳跳虎完成签到,获得积分10
4秒前
风之子完成签到,获得积分10
5秒前
aliu完成签到,获得积分10
5秒前
开心夜云完成签到,获得积分10
5秒前
努力学习完成签到,获得积分10
6秒前
淡定傲儿完成签到,获得积分20
7秒前
7秒前
wz0330发布了新的文献求助10
7秒前
Anyemzl完成签到,获得积分10
7秒前
micro然发布了新的文献求助10
7秒前
贱小贱完成签到,获得积分10
7秒前
踢踢踢踢踢死你完成签到,获得积分10
7秒前
迷人的翠绿完成签到,获得积分10
8秒前
风之子发布了新的文献求助10
8秒前
Lu完成签到,获得积分10
8秒前
9秒前
彳亍完成签到,获得积分10
9秒前
盟主完成签到 ,获得积分10
9秒前
10秒前
10秒前
bkagyin应助MS903采纳,获得10
11秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Fermented Coffee Market 2000
Methoden des Rechts 600
Constitutional and Administrative Law 500
PARLOC2001: The update of loss containment data for offshore pipelines 500
Critical Thinking: Tools for Taking Charge of Your Learning and Your Life 4th Edition 500
Vertebrate Palaeontology, 5th Edition 380
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 遗传学 催化作用 冶金 量子力学 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 5282573
求助须知:如何正确求助?哪些是违规求助? 4436555
关于积分的说明 13809715
捐赠科研通 4317159
什么是DOI,文献DOI怎么找? 2369613
邀请新用户注册赠送积分活动 1364999
关于科研通互助平台的介绍 1328450