New prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approach

被引:75
作者
Babanajad, Saeed K. [1 ]
Gandomi, Amir H. [2 ,3 ]
Alavi, Amir H. [4 ]
机构
[1] Rutgers State Univ, CAIT, New Brunswick, NJ 08854 USA
[2] Stevens Inst Technol, Hoboken, NJ 07030 USA
[3] Michigan State Univ, BEACON Ctr Study Evolut Act, E Lansing, MI 48824 USA
[4] Michigan State Univ, Dept Civil & Environm Engn, E Lansing, MI 48824 USA
关键词
Artificial intelligence; Gene expression programming; Triaxial; Machine learning; Computer-aided; Strength model; HIGH-PERFORMANCE CONCRETE; COMPRESSIVE STRENGTH; FAILURE CRITERION; NEURAL-NETWORK; BEHAVIOR; SELECTION; SURFACE; DAMAGE;
D O I
10.1016/j.advengsoft.2017.03.011
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The complexity associated with the in-homogeneous nature of concrete suggests the necessity of conducting more in-depth behavioral analysis of this material in terms of different loading configurations. Distinctive feature of Gene Expression Programming (GEP) has been employed to derive computer-aided prediction models for the multiaxial strength of concrete under true-triaxial loading. The proposed models correlate the concrete true-triaxial strength (sigma(1)) to mix design parameters and principal stresses (sigma(2),sigma(3)), needless of conducting any time-consuming laboratory experiments. A comprehensive true-triaxial database is obtained from the literature to build the proposed models, subsequently implemented for the verification purposes. External validations as well as sensitivity analysis are further carried out using several statistical criteria recommended by researchers. More, they demonstrate superior performance to the other existing empirical and analytical models. The proposed design equations can readily be used for pre-design purposes or may be used as a fast check on deterministic solutions. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:55 / 68
页数:14
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