ANN-based response surface method and its application to ultimate strength of plates

被引：0
|
作者：
Pu, YC [1 ]
Mesbahi, E [1 ]
ElHewy, AH [1 ]
机构：
[1] Newcastle Univ, Sch Marine Sci & Technol, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England
来源：
PROCEEDINGS OF THE FIFTEENTH (2005) INTERNATIONAL OFFSHORE AND POLAR ENGINEERING CONFERENCE, VOL 4 | 2005年
关键词：
ANN-based response surface method; artificial neural networks; response surface method; ultimate strength of plates; design equations of plates;
D O I：
暂无
中图分类号：
U6 [水路运输]; P75 [海洋工程];
学科分类号：
0814 ; 081505 ; 0824 ; 082401 ;
摘要：
In this paper, artificial neural networks (ANN)-based response surface method (RSM) is presented. This method is compared with conventional polynomial-based RSM in the context of structural reliability analysis. ANN-based RSM is then applied to predict ultimate strength of unstiffened plates. It is found out that the ANN-based RSM is more accurate and efficient than polynomial-based RSM in structural reliability analysis. ANN-based RSM can more accurately predict ultimate strength of unstiffened plates than the existing empirical formulae.
引用
收藏
页码：752 / 758
页数：7
相关论文
共 50 条
  • [41] Simple method for ultimate strength calculation and reliability analysis of stiffened plates
    Feng, Liang
    Dong, Sheng
    Wang, Baosen
    Zhen, Chunbo
    [J]. Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition), 2016, 44 (09): : 71 - 76
  • [42] ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength
    Jalal, Fazal E.
    Iqbal, Mudassir
    Khan, Waseem Akhtar
    Jamal, Arshad
    Onyelowe, Kennedy
    Lekhraj
    [J]. SCIENTIFIC REPORTS, 2024, 14 (01):
  • [43] An ANN-based failure pressure prediction method for buried high-strength pipes with stray current corrosion defect
    Liu, Xiaoben
    Xia, Mengying
    Bolati, Dinaer
    Liu, Jianping
    Zheng, Qian
    Zhang, Hong
    [J]. ENERGY SCIENCE & ENGINEERING, 2020, 8 (01) : 248 - 259
  • [44] Application of Recent Developments in Deep Learning to ANN-based Automatic Berthing Systems
    Lee, Daesoo
    Lee, Seung-Jae
    Seo, Yu-Jeong
    [J]. INTERNATIONAL JOURNAL OF ENGINEERING AND TECHNOLOGY INNOVATION, 2020, 10 (01) : 75 - 90
  • [45] Application of ANN-based failure criteria to rocks under polyaxial stress conditions
    Rafiai, H.
    Jafari, A.
    Mahmoudi, A.
    [J]. INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES, 2013, 59 : 42 - 49
  • [46] An ANN-based model for learning individual customer behavior in response to electricity prices
    Nakabi, Taha Abdelhalim
    Toivanen, Pekka
    [J]. SUSTAINABLE ENERGY GRIDS & NETWORKS, 2019, 18
  • [47] Application of Meta-Evolutionary Programming in ANN-Based Lightning Prediction System
    Johari, D.
    Rahman, T. K. A.
    Musirin, I.
    Aminudin, N.
    [J]. INTERNATIONAL REVIEW OF ELECTRICAL ENGINEERING-IREE, 2010, 5 (04): : 1824 - 1832
  • [48] Application of response surface method for reliability analysis of stiffened laminated plates
    Yang, Nana
    Das, Purnendu K.
    Yao, Xiongliang
    [J]. SHIPS AND OFFSHORE STRUCTURES, 2015, 10 (06) : 653 - 659
  • [49] Adaptation of an ANN-Based Air Quality Forecasting Model to a New Application Area
    Orlowski, Cezary
    Sarzynski, Arkadiusz
    Karatzas, Kostas
    Katsifarakis, Nikos
    Nazarko, Joanicjusz
    [J]. ADVANCED TOPICS IN INTELLIGENT INFORMATION AND DATABASE SYSTEMS, 2017, 710 : 479 - 488
  • [50] An ANN-based method for predicting Zhundong and other Chinese coal slagging potential
    Yang, Haoran
    Jin, Jing
    Hou, Fengxiao
    He, Xiang
    Hang, Yixuan
    [J]. FUEL, 2021, 293
12345