A forest fire algorithm for predicting the percolation threshold of ITZs in concrete

被引：0
|
作者：
Zhou, Xinzhu [1 ]
Zheng, Jianjun [1 ]
Xia, Caian [1 ]
机构：
[1] Zhejiang Univ Technol, Sch Civil Engn & Architect, Hangzhou 310014, Peoples R China
来源：
ICNC 2007: THIRD INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 4, PROCEEDINGS | 2007年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Interfacial transition zone (ITZ) in concrete is characterized typically by its high porosity and therefore has a significant influence, particularly, on the transport properties of concrete. To study the transport mechanism of ions and fluids in concrete, it is essential to determine the percolation threshold of ITZs in concrete and to evaluate the effects of attributing factors on it. This paper presents a forest fire algorithm for the prediction of the percolation threshold of ITZs in concrete. Based on the simulated mesostructure of concrete with periodic boundary conditions, the ITZ percolation probability is determined numerically. The effect of the element size on the ITZ percolation probability is then discussed. Finally, the proposed numerical algorithm is verified with experimental results obtained from the research literature.
引用
收藏
页码：805 / +
页数：2
相关论文
共 50 条
  • [41] Predicting Forest Fire Using Remote Sensing Data And Machine Learning
    Yang, Suwei
    Lupascu, Massimo
    Meel, Kuldeep S.
    THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2021, 35 : 14983 - 14990
  • [42] Predicting the Occurrence of Forest Fire in the Central-South Region of China
    Hai, Quansheng
    Han, Xiufeng
    Vandansambuu, Battsengel
    Bao, Yuhai
    Gantumur, Byambakhuu
    Bayarsaikhan, Sainbuyan
    Chantsal, Narantsetseg
    Sun, Hailian
    FORESTS, 2024, 15 (05):
  • [43] Predicting snag fall in an old-growth forest after fire
    Becker, Kendall M. L.
    Lutz, James A.
    FIRE ECOLOGY, 2023, 19 (01)
  • [44] Predicting snag fall in an old-growth forest after fire
    Kendall M. L. Becker
    James A. Lutz
    Fire Ecology, 19
  • [45] OPTIMIZING THE ROTHERMEL MODEL FOR EASILY PREDICTING SPREAD RATE OF FOREST FIRE
    Hua, J.
    Zhang, S.
    Gao, H.
    Chen, X.
    Li, X.
    Liu, J.
    MATHEMATICAL AND COMPUTATIONAL FORESTRY & NATURAL-RESOURCE SCIENCES, 2020, 12 (02): : 62 - 71
  • [46] OPTIMIZING THE ROTHERMEL MODEL FOR EASILY PREDICTING SPREAD RATE OF FOREST FIRE
    Hua, J.
    Zhang, S.
    Gao, H.
    Chen, X.
    Li, X.
    Liu, J.
    Zhang, S., 1600, Contemporary Journal Concept Press (12): : 62 - 71
  • [47] RANDOM FOREST ALGORITHM FOR PREDICTING CHRONIC DIABETES DISEASE
    Subbaiah, S.
    Kavitha, M.
    INTERNATIONAL JOURNAL OF LIFE SCIENCE AND PHARMA RESEARCH, 2020, : 4 - 8
  • [48] Predicting Video Website DAU by Random Forest Algorithm
    Dai, Jianhua
    Guo, Xiao
    Jin, Libo
    2020 IEEE CONFERENCE ON TELECOMMUNICATIONS, OPTICS AND COMPUTER SCIENCE (TOCS), 2020, : 89 - 93
  • [49] Self-Destructive Percolation as a Limit of Forest-Fire Models on Regular Rooted Trees
    Graf, Robert
    RANDOM STRUCTURES & ALGORITHMS, 2017, 50 (01) : 86 - 113
  • [50] An Improved Random Forest Algorithm for Predicting Employee Turnover
    Gao, Xiang
    Wen, Junhao
    Zhang, Cheng
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2019, 2019
12345