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Fly ash, river sand, and ordinary gravel, which are used in concrete as the admixture, fine aggregate, and coarse aggregate, respectively, were replaced by iron tailing powder, sand, and gravel. The influence of the iron tailings on the bond properties between concrete and steel bar was studied by conducting pull‐out tests on 16 groups and 48 specimens. At a curing age of 90 days, the splitting tensile strength of the iron tailing powder concrete (ITPC) for strength grades C25 and C30 was the lowest and was only 71.8% and 66.6% of the splitting tensile strength for the corresponding strength grade of conventional concrete (CC). The lower splitting tensile strength of ITPC caused the bond strength between the concrete and steel bar to be lower than that between the CC and steel bar, with a maximum difference of 14.67%. The bond strength between the iron tailing powder–sand concrete (ITPSC) and steel bar was slightly worse than that between the CC and steel bar, with a maximum difference of 6.88%, and better than that between the ITPC and steel bar. The bond strength between the full iron tailing concrete and steel bar was better than those between the CC and steel bar and between the ITPSC and steel bar. In addition, the bond slip characteristics were investigated. [ABSTRACT FROM AUTHOR]

Reinforced concrete structures can experience various harsh environments during their service life, among which chloride ion exposure, especially in marine environments, can cause the durability reduction and deterioration of concrete structures. Artificial intelligence (AI)‐based modeling of the non‐steady‐state apparent chloride diffusion coefficient (DC) of concrete for a long exposure time using the experimental field results can assist in identifying the influential factors and better estimating the service life of a concrete structure. In this study, two novel extensions of ensemble AI algorithms, including genetic programming forest (GPF) and linear genetic programming forest (LGPF) algorithms, were proposed to model the DC of concrete. The experimental field data were gathered from the literature. Different structures of the proposed ensemble methods were developed and examined, and the best‐developed model was selected for further analysis, including sensitivity analysis and parametric study. In addition, the random forest (RF) method was used as the control ensemble technique to have a comparison. The results show that the best LGPF model possesses superior performance than the best‐developed GPF and RF models. In addition, the results show that silica fume‐to‐binder ratio, exposure time, and exposure conditions have the most significant impacts on the DC of concrete. This study contributes to the civil engineering practice by developing a new tool to model the DC of concrete that facilitates the durability assessment of concrete structures. [ABSTRACT FROM AUTHOR]

Global warming triggered efforts on sustainable materials in any area of production. Thus, the construction materials were also at the edge of a sharp change to achieve less harm to the environment by reducing the carbon print. Consequently, researchers have been trying to replace the cement from the mixtures of reinforced concrete as cement production causes large amounts of carbon dioxide emissions. Alkali‐activated binders are proved to be versatile alternatives to cementitious binders; however, accurate mathematical models to predict the capacities of these structural elements are limited in number. Therefore, in this study, a new stress–strain model has been developed to estimate the flexural capacity of geopolymer structural elements originated from construction and demolition waste. The study was initiated by formulating a novel stress–strain model applicable for defining the compressive behavior of geopolymer concrete. The formulation was based on recent experimental findings on flexural behavior of geopolymer beam specimens. Then, the performance of the proposed stress–strain model in estimating the ultimate moment capacities was investigated by using 36 bending tests from the literature. The results showed that the proposed model had very little deviations and enhanced accuracy compared with the literature available estimation method recommended by the ACI318. In addition, a soft database composed of 50 different beam specimens with varying mechanical properties and reinforcement patterns was formed. Numerical models corresponding to all possible combinations of each parameter were generated, and their load capacities were determined. After that, the estimation performances of the proposed method and the stress–strain models from the literature, and also the ACI318 procedure were examined. The conclusion of this check also yielded promising results with an absolute mean percentage error of 5.13% regardless of the mode of failures. [ABSTRACT FROM AUTHOR]

In this contribution, an additive strain model is used to describe the deformation behavior of concrete. The model assumes that elastic, viscous, thermal, and damage‐induced strains occur in concrete. The strain components develop independently of each other as a function of the number of load cycles, test duration, and characteristic parameters of the stress cycles. The focus of this paper is on the determination of a creep‐relevant stress level to estimate the magnitude of the viscous deformation component as a function of the fatigue stress. Experimentally measured strains of fatigue‐loaded concrete specimens are divided into the strain components assumed by the additive strain model and analyzed. The investigations yield new insights into the strain and material behavior of fatigue‐loaded concrete. [ABSTRACT FROM AUTHOR]