New prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approach. (August 2017)

• Record Type: Journal Article
• Authors: Babanajad, Saeed K.; Gandomi, Amir H.; Alavi, Amir H.
• Is Part Of: Advances in engineering software. Volume 110(2017)
• Page Start: 55
• Page End: 68
• Publication Date: 2017-08
• Subjects: Artificial intelligence -- Gene expression programming -- Triaxial -- Machine learning -- Computer-aided -- Strength model; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2017.03.011
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: New AI-based prediction models have been developed to use the concrete mix design proportions as well as the confining pressures in order to calculate the concrete ultimate strength under true-triaixial stress states. The proposed formulations have employed the distinctive concept of Genetic Programming, specifically Gene Expression Programming, to derive computer-aided prediction models for the multiaxial strength of concrete under true-triaxial loading. The results of the proposed models showed that they can be used as proper alternatives of existing empirical and analytical formulations used for strength calculation of concrete specimens. Abstract: 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 (σ1 ) to mix design parameters and principal stresses (σ2, σ3 ), 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.