Improved Shear Strength Prediction Model of Steel Fiber Reinforced Concrete Beams by Adopting Gene Expression Programming

Abstract

In this study, an artificial intelligence tool called gene expression programming (GEP) has been successfully applied to develop an empirical model that can predict the shear strength of steel fiber reinforced concrete beams. The proposed genetic model incorporates all the influencing parameters such as the geometric properties of the beam, the concrete compressive strength, the shear span-to-depth ratio, and the mechanical and material properties of steel fiber. Existing empirical models ignore the tensile strength of steel fibers, which exercise a strong influence on the crack propagation of concrete matrix, thereby affecting the beam shear strength. To overcome this limitation, an improved and robust empirical model is proposed herein that incorporates the fiber tensile strength along with the other influencing factors. For this purpose, an extensive experimental database subjected to four-point loading is constructed comprising results of 488 tests drawn from the literature. The data are divided based on different shapes (hooked or straight fiber) and the tensile strength of steel fiber. The empirical model is developed using this experimental database and statistically compared with previously established empirical equations. This comparison indicates that the proposed model shows significant improvement in predicting the shear strength of steel fiber reinforced concrete beams, thus substantiating the important role of fiber tensile strength.

Gene Expression Programming for Estimating Shear Strength of RC Squat Wall

“…Machine learning (ML) algorithms have been widely used to tackle real-world problems in the last ten years, particularly in civil engineering. ML algorithms have been successfully used in a variety of real situations, paving the way for several promising opportunities in civil engineering and other domains such as geotechnical, and structural engineering [65][66][67][68][69][70][71][72][73][74].…”

Shear Strength Model for Reinforced Concrete Corner Joints Based on Soft Computing Techniques

“…An interesting feature of having more genes and chromosomes is that the function can be more complex, but the results can still be exact. Because of this tradeoff, it is possible to achieve a simplified mathematical model and control the number of genes/chromosomes, as well as achieve the desired level of accuracy [76].…”

Predicting the Response of RC Beam from a Drop-Weight Using Gene Expression Programming

“…In terms of chromosome representation, GEP differs from the genetic algorithms GAs and the genetic programming GP. In GAs, chromosomes are linear strings of fixed length, whereas in GPs, they are nonlinear entities of varying sizes and shapes [ 43 , 44 , 45 ]. GEP, on the other hand, encompasses both a fixed-length linear string and a ramified structure of various sizes and shapes.…”

“…Notably, increasing the number of genes and chromosomes can result in a complicated function but the function can precisely fit the results. There is a trade-off between achieving a simplified mathematical model by controlling the number of genes and chromosomes and achieving the desired level of accuracy [ 43 , 44 , 45 , 46 , 47 ].…”

“…The accuracy of any empirical model is inextricably linked to the training and validation of these models using the available dataset [ 45 ]. In the case of the current model, a random sample of 60% from the designated RC beam experiments is used as training data (for model creation), while a random sample of 40% from each category is used for validation.…”