Steel plates are used in the construction of various structures in civil engineering, aerospace, and shipbuilding. One of the main failure modes of plate members is buckling. Openings are provided in plates to accommodate various additional facilities and make the structure more serviceable. The present study examined the critical buckling load of rectangular steel plates with centrally placed circular openings and different support conditions. Various datasets were compiled from the literature and integrated into artificial intelligence techniques like Gene Expression Programming (GEP), Artificial Neural Network (ANN) and Evolutionary Polynomial Regression (EPR) to predict the critical buckling loads of the steel plates. The comparison of the developed models was conducted by determining various statistical parameters. The assessment revealed that the ANN model, with an R² of 98.6% with an average error of 10.4%, outperformed the other two models showing its superiority in terms of better precision and less error. Thus, artificial intelligence techniques can be adopted as a successful technique for the prediction of the buckling load, and it is a sustainable method that can be used to solve practical problems encountered in the field of civil engineering, especially in steel structures.

In this paper, an extensive literature search has been employed to extract multiple data on the confined compressive strength of carbon fiber reinforced polymer (CFRP) concrete columns with noncircular cross-sections. The values collected are related to width (b), length (h), radius of corner (r), thickness of fiber (t), unconfined concrete strength (f’co), tensile strength of fiber (ftf), elastic modulus of fiber (Ef) and the confined strength of the CFRP-jacketed concrete columns (f’cc). The database was used to propose predictive models by artificial neural network (ANN-BP, -GA & -GRG), genetic programming (GP) and the evolutionary polynomial regression (EPR) techniques. The sum of squares errors (SSE), root mean square errors (RMSE) and coefficient determination (R²) performance indices were used to evaluate the performance accuracy and efficiency of the models. At the end of the exercise, the GP and EPR produced closed form equation with performance indices of 0.623 (28%) and 0.815 (20.9%), respectively, and these did not come close to the performance of ANN-BP, -GRG and GA which performed in that order with 0.967 (9.4%), 0.960 (10.3%) and 0.957 (10.6%), respectively. Last, the relative importance of the parameters conducted showed that f’co has the greatest influence on the f’cc of the concrete structure.

The wrapping of concrete structures with fiber polymers has been an essential part of concrete technology aimed at the improvement of concrete performance indices during the construction and lifelong usage of the structures. In this paper, a universal representative database was collected from multiple literature materials on the effect of different fiber-reinforced polymers on the confined compressive strength of wrapped concrete columns (Fcc). The collected data show that the Fcc value depends on the FRP thickness (t), tensile strength (Ftf), and elastic modulus (Ef), in addition to the column diameter (d) and the confined compressive strength of concrete (Fco). Five AI techniques were applied on the collected database, namely genetic programming (GP), three artificial neural networks (ANN) trained using three different algorithms, “back Propagation BP, gradually reduced gradient GRG and genetic algorithm GA”, and evolutionary polynomial regression (EPR). The results of the five developed predictive models show that (t) and Ftf have a major impact on the Fcc value, which presents the effect of confinement stress (t. Ftf/d) on the confined compressive strength (Fcc). Comparing the predicted values with the experimental ones showed that the GP model is the least accurate one, and the EPR model is the next least accurate, while the three ANN models have almost the same level of high accuracy, with an average error percentage of 5.8% and a coefficient of determination R² of 0.961. The ANN model is more accurate than the EPR and GP predictive models, but they are suitable for manual calculation because they are closed-form equations.