Evaluation of the Strength of Slab-Column Connections with FRPs Using Machine Learning Algorithms

Abstract

Slab–column connections with FRPs fail suddenly without warning. Machine learning (ML) models can model the behavior with high precision and reliability. Nineteen ML algorithms were examined and compared. The comparisons showed that the ensembled boosted tree model showed the best, most precise prediction with the highest coefficient of determination (R2) (0.98), the lowest Root Mean Square Error (RMSE) (44.12 kN), and the lowest Mean Absolute Error (MAE) (35.95 kN). The ensembled boosted model had an average of 0.99, a coefficient of variation of 12%, and a lower 95% of 0.97, respectively, in terms of the measured strength. Thus, it was found to be more accurate and consistent compared to all implemented machine learning models and selected traditional models. In addition, the significance of various parameters with respect to the predicted strength was identified, where the effective depth was the most significant by a factor of 0.9, and the concrete compressive strength was the lowest by a factor of 0.3.

Reliable Machine Learning Model for Shear Strength of Reinforced Concrete Beams Strengthened Using FRP Jackets

“…Relatively limited detailed studies have investigated the shear strength of RC members; in addition, many questions regarding the strength mechanism, especially the usage of anchorage devices, are yet to be resolved. Many researcher studies have idealized the EB-FRP jacket in a manner similar to that of steel FRP intermediate de-bonding FRP end de-bonding shear reinforcments, assuming that the contribution of EB-FRP to shear capacity arises from the EB-FRP capacity to resist tensile stresses at a strain, which is less than or equal to the FRP ultimate tensile strain [16][17][18]. Numerous experimental tests was conducted out in this field of research, and existing design codes and guidelines are available for designing EB-FRP elements [18][19][20][21][22].…”

“…It has been used for new structural elements in recent years, particularly in aggressive environments including but not limited to chemical plants, which is because its being corrosion free [12][13][14]. FRP can be used in situations where the usage of steel would be impossible or impractical [15][16]. For instance, it can be formed on-site to fit any irregular shape.…”

Compressive Strength of Steel Fiber-Reinforced Concrete Employing Supervised Machine Learning Techniques

“…These practices can be used to predict outcomes with a high degree of precision. Many studies have been undertaken using machine learning approaches to forecast the strength properties of concrete and its structural elements as well [10][11][12][13][14][15][16][17][18][19][20][21][22][23]. Two machine learning approaches, the individual and ensemble approaches, were employed by Ahmad et al to forecast concrete compressive strength [24].…”

Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence

“…Several studies have attempted to predict concrete strength characteristics [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. Machine learning methods are employed to forecast concrete strength [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ] and the durability of concrete [ 50 , 51 , 52 ]. Bagging regression (BR) and gradient boosting (GB) models based on a variation of the bootstrap aggregation decision tree (DT) method have been shown in several studies to outperform other stand-alone ML models in terms of concrete strength prediction accuracy [ 53 , 54 , 55 , 56 ].…”