Explicit expression for effective moment of inertia of RC beams

Abstract

Deflection is an important design parameter for structures subjected to service load. This paper provides an explicit expression for effective moment of inertia considering cracking, for uniformly distributed loaded reinforced concrete (RC) beams. The proposed explicit expression can be used for rapid prediction of short-term deflection at service load. The explicit expression has been obtained from the trained neural network considering concrete cracking, tension stiffening and entire practical range of reinforcement. Three significant structural parameters have been identified that govern the change in effective moment of inertia and therefore deflection. These three parameters are chosen as inputs to train neural network. The training data sets for neural network are generated using finite element software ABAQUS. The explicit expression has been validated for a number of simply supported and continuous beams and it is shown that the predicted deflections have reasonable accuracy for practical purpose. A sensitivity analysis has been performed, which indicates substantial dependence of effective moment of inertia on the selected input parameters.

Cracked span length beam element for service load analysis of steel concrete composite bridges

“…To consider cracking, the smeared crack model is used [24][25][26][27]. The concrete is considered to be an elastic material in tension before the cracking (curve OA, Fig.…”

“…3), r un and M x in Eq. (27) are substituted by Àf t and R A x À M A À wx 2 /2 (w = uniformly distributed load), respectively, to yield Àf t ¼ E c S xy ðR A x À M A À wx 2 =2Þ ð A:1Þ…”

Rapid prediction of moments in highrise composite frames considering cracking and time-effects

“…The trained neural networks may be used to develop the required closed form expressions. Since the sigmoid function has been used as the activation function, the outputs O 1 (= [20][21] where, q is the number of input parameters; r is the number of hidden neurons; bias k is the bias of k th hidden neuron (h k ); bias o1 ; bias o2 are the bias of output neurons; The inelastic moments at left and right side of joints are then given as…”

“…Many researchers have proposed the closed form expressions using the weight matrices and activated function of the trained neural network. Some of the closed form expressions obtained from the trained neural networks include determination of distortional buckling stress in cold-formed steel members [16], estimation of ultimate pure bending of fabricated and cold-formed steel circular tubes [17], prediction of base shear of steel frame structures [18], estimation of distortional buckling stress in elliptical hollow section tubes [19], evaluation of deflections in composite bridges considering flexibility of shear connectors, concrete cracking and shear lag effect [20] and prediction of effective moment of inertia in reinforced concrete beams [21]. Such closed form expressions are useful to predict the parameters in every day design with acceptable accuracy.…”

Effect of an Opening on Reinforced Concrete Hollow Beam Web Under Torsional, Flexural, and Cyclic Loadings

“…Rather than hollow beams, the RC sections with web openings are also frequently used in construction for passing electrical and mechanical utilities as shown in Figure 1. Flexure is considered an important parameter for structural design service load (Patel et al, 2014). Hafiz et al (2014) evaluated the effects of openings on the behavior of reinforced concrete beams without special reinforcement on the opening zone.…”