Abstract
Castellated steel beams (CSB) are an attractive option for the steel construction industry thanks to outstanding advantages, such as the ability to exceed large span, lightweight, and allowing flexible arrangement of the technical pipes through beams. In addition, the complex localized and global failures characterizing these structural members have led researchers to focus on the development of efficient design guidelines. This paper aims to propose an artificial neural network (ANN) model with optimal architecture to predict the load-carrying capacity of CSB with a scheme of the simple beam bearing load located at the center of the beam. The ANN model is built with 9 input variables, which are essential parameters equivalent to the geometrical properties and mechanical properties of the material, such as the overall depth of the castellated beam, the vertical projection of the inclined side of the opening, the web thickness, the flange width, the flange thickness, the width of web post at middepth, the horizontal projection of inclined side of the opening, the minimum web yield stress, and the minimum flange yield stress. The output variable is the load-carrying capacity of the CSB. With the optimal ANN architecture [9-1-1] containing one hidden layer, the performance of the ANN model is evaluated based on statistical criteria such as R2, RMSE, and MAE. The results show that the optimal ANN model is a highly effective predictor of the load-carrying capacity of the CSB with the best value of R2 = 0.989, RMSE = 3.328, and MAE = 2.620 for the testing part. The ANN model seems to be the best algorithm of machine learning for predicting the CSB load-carrying capacity.
Highlights
In modern construction, steel structures are used for abundant structures, including heavy industrial buildings, high-rise buildings, equipment support systems, infrastructure, bridges, towers, and racking systems [1]. e steel structure has numerous advantages such as large bearing capacity thanks to the high strength steel material, high reliability thanks to the uniform material, and the elastic and ductile capacity of steel, making it easy to transport and assemble [2]
Castellated steel beams (CSB) with web openings are among the first suggestions of these solutions [5]. is type of beam is made from wide flange I-beams, cutting the belly plate in a zigzag line, welding the two halves on top of each other, and welding by vertical welding seams along the beam [3, 4, 6, 7]
artificial neural network (ANN) does not attempt to simulate the delicate workings of the brain, but they try to replicate the logical activity of the brain by gathering a lot of input in the form of neurons to perform computational or cognitive processes. e purpose of ANN is to define the relationship between the input parameters and the output parameters of the model
Summary
Steel structures are used for abundant structures, including heavy industrial buildings, high-rise buildings, equipment support systems, infrastructure, bridges, towers, and racking systems [1]. e steel structure has numerous advantages such as large bearing capacity thanks to the high strength steel material, high reliability thanks to the uniform material, and the elastic and ductile capacity of steel, making it easy to transport and assemble [2]. Erefore, several solutions have been proposed to increase the rigidity or load capacity of the structure without increasing the weight of steel. Castellated steel beams (CSB) with web openings are among the first suggestions of these solutions [5]. Is increases the section height but does not increase weight compared to the original solid beam; bending resistance characteristics such as the moment of inertia, section modulus, and the radius of inertia are higher, and the beam stiffness and flexural resistance of sections are enhanced [3, 7, 8]. Erefore, the beam bearing capacity increases, the deflection is small, and the beam is able to exceed the large aperture [7]. The web openings will change the stress distribution on the bending sections [4]. The stress distribution is quite complex, and stress concentration occurs.
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