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The ultrafine fly ash (FA) is a hazardous material collected from coal productions, which has been proficiently employed for the manufacturing of geopolymer concrete (GPC). In this study, the three artificial intelligence (AI) techniques, namely, artificial neural network (ANN), adaptive neuro-fuzzy interface (ANFIS), and gene expression programming (GEP) are used to establish a reliable and accurate model to estimate the compressive strength (f′c) of fly ash–based geopolymer concrete (FGPC). A database of 298 instances is developed from the peer-reviewed published work. The database consists of the ten most prominent explanatory variables and f′c of FGPC as a response parameter. The statistical error checks and criteria suggested in the literature are considered for the verification of the predictive strength of the models. The statistical measures considered in this study are MAE, RSE, RMSE, RRMSE, R, and performance index (ρ). These checks verify that the ANFIS predictive model gives an outstanding performance followed by GEP and ANN predictive models. In the validation stage, the coefficient of correlation (R) for ANFIS, GEP, and ANN model is 0.9783, 0.9643, and 0.9314, respectively. All three models also fulfill the external verification criterion suggested in the literature. Generally, the GEP predictive model is ideal as it delivers a simplistic and easy mathematical equation for future use. The k-fold cross-validation (CV) of the GEP model is also conducted, which verifies the robustness of the GEP predictive model. Furthermore, the parametric study is carried via proposed GEP expression. This confirms that the GEP model accurately covers the influence of all the explanatory variables used for the prediction of f′c of FGPC. Thus, the proposed GEP equation can be used in the preliminary design of FGPC.

Fiber reinforced polymer (FRP) reinforcing bars have been used in concrete structures as an alternative to conventional steel reinforcement, in order to overcome corrosion problems. However, due to the linear behavior of the commonly used reinforcing fibers, they are not considered in structures which require ductility and damping characteristics. The use of superelastic shape memory alloy (SMA) fibers with their nonlinear elastic behavior as reinforcement in the composite could potentially provide a solution for this problem. Small diameter SMA wires are coupled with polymer matrix to produce SMA–FRP composite, which is sought in this research as reinforcing bars. SMA–FRP bars are sought in this study to enhance the seismic performance of reinforced concrete (RC) moment resisting frames (MRFs) in terms of reducing their residual inter-story drifts while still maintaining the elastic characteristics associated with conventional FRP. Three story one bay and six story two bay RC MRF prototype structures are designed with steel, SMA–FRP and glass–FRP reinforcement. The incremental dynamic analysis technique is used to investigate the behaviors of the two frames with the three different reinforcement types under a suite of ground motion records. It is found that the frames with SMA–FRP composite reinforcement exhibit higher performance levels including lower residual inter-story drifts, high energy dissipation and thus lower damage, which are important for structures in highly seismic zones.

For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation to estimate the compressive strength of GPC made by employing FA. To build a model, a consistent, extensive and reliable data base is compiled through a detailed review of the published research. The compiled data set is comprised of 298 experimental results. The utmost dominant parameters are counted as explanatory variables, in other words, the extra water added as percent FA (), the percentage of plasticizer (), the initial curing temperature (), the age of the specimen (), the curing duration (), the fine aggregate to total aggregate ratio (), the percentage of total aggregate by volume (), the percent SiO2 solids to water ratio () in sodium silicate (Na2SiO3) solution, the NaOH solution molarity (), the activator or alkali to FA ratio (), the sodium oxide (Na2O) to water ratio () for preparing Na2SiO3 solution, and the Na2SiO3 to NaOH ratio (). A GEP empirical equation is proposed to estimate the of GPC made with FA. The accuracy, generalization, and prediction capability of the proposed model was evaluated by performing parametric analysis, applying statistical checks, and then compared with non-linear and linear regression equations.

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