Abstract
Composite steel beams are commonly used element in multistorey steel buildings to enhance floor economy and serviceability and provide more clear height. Due to the low level of stress in the webs of such beams, hybrid sections are used where the flanges have higher strength than the webs. A lot of earlier research was carried out to optimize the design of the hybrid and nonhybrid composite steel beams under both static loading and dynamic behavior. However, there is still a need to develop a more practical optimization method. The aim of this research is to develop simple and practical equations to determine the optimum cross section dimensions for both shored and unshored, simply supported, hybrid and nonhybrid, composite steel beam under static loads. To achieve that goal, a research program of two phases was carried out. The first phase was generating a database of 504 composite beams with different steel grades for flanges and webs, subjected to different values of bending moment. The cross section of each beam in the database was optimized using GRG technique to minimize the cost considering the unit price of each steel grade. In the second phase, the generated database was divided into training and validation subsets and used to develop two predictive models using Nonlinear Regression (NLR) technique and Artificial Neural Network (ANN) technique to predict the optimum cross section dimensions and hence the optimum weight and cost. The accuracies of the developed models were measured in terms of average error percent. NLR and ANN models showed average error percent of 16% and 11%, respectively.
Highlights
Connecting steel beams to concrete decks to form composite floors is commonly used in multistorey steel buildings. e use of this system reduces both weight and cost of floors and increases their stiffness and severability
On the other hand, unshored floor is casted without supporting, and weights of steel and concrete are supported by steel section only while the rest of loads are supported by the composite section
Methodology is research program is divided into two main phases. e first phase is concerned with generating a database of optimized composite beams, and the second phase is concerned with developing predictive models to predict the optimum cross section dimensions of the composite beam based on its configurations using the generated optimized dataset from the first phase. e detailed descriptions of each phase are provided
Summary
Connecting steel beams to concrete decks to form composite floors is commonly used in multistorey steel buildings. e use of this system reduces both weight and cost of floors and increases their stiffness and severability. Silih [5] used the well-known Nonlinear Programming (NLP) approach to optimize the design of both trusses and beams composite floors according to Eurocode. Uros Klansek and Stojan Kravanja [9], in the 2nd part of their research, optimized the design of composite floors using Nonlinear Programming (NLP) approach. Klansek [10] suggested an optimization technique for both truss and beam composite floors according to Eurocode using Nonlinear Programming (NLP). Al-Ansari [11] presented a Genetic Algorithm (GA) model to optimize the design of composite beams according to the LRFD of the AISC considering the effect of span and loads. Rosca et al [14] presented an optimization technique for composite beams design according to EN-1994-1-1/2006 using Nonlinear Programming (NLP) approach. Gohari et al [29] suggested a new analytical solution for elastic flexure of thick multilayered composite hybrid plates resting on Winkler elastic foundation in air and water
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