Abstract

This paper presents a study showing the optimization of the mass, direct (self-manufacturing) costs, and energy life-cycle costs of composite floor structures composed of a reinforced concrete slab and steel I-beams. In a multi-parametric study, mixed-integer non-linear programming (MINLP) optimizations are carried out for different design parameters, such as different loads, spans, concrete and steel classes, welded, IPE and HEA steel profiles, and different energy consumption cases. Different objective functions of the composite structure are defined for optimization, such as mass, direct cost, and energy life-cycle cost objective functions. Moreover, three different energy consumption cases are proposed for the energy life-cycle cost objective: an energy efficient case (50 kWh/m2), an energy inefficient case (100 kWh/m2), and a high energy consumption case (200 kWh/m2). In each optimization, the objective function of the structure is subjected to the design, load, resistance, and deflection (in)equality constraints defined in accordance with Eurocode specifications. The optimal results calculated with different criteria are then compared to obtain competitive composite designs. Comparative diagrams have been developed to determine the competitive spans of composite floor structures with three different types of steel I beam: those made of welded sections and those made of IPE or HEA sections, respectively. The paper also answers the question of how different objective functions affect the amount of the calculated costs and masses of the structures. It has been established that the higher (more wasteful) the energy consumption case is, the lower the obtained masses of the composite floor structures are. In cases with higher energy consumption, the energy life-cycle costs are several times higher than the costs determined in direct cost optimization. At the end of the paper, a recommended optimal design for a composite floor system is presented that has been developed on the multi-parametric energy life-cycle cost optimization, where the energy efficient case is considered. An engineer or researcher can use the recommendations presented here to find a suitable optimal composite structure design for a desired span and uniformly imposed load.

Highlights

  • The optimization of steel–concrete composite structures can generally be conducted under several relevant criteria

  • This paper presents a study showing the optimization of the mass, direct costs, and energy life-cycle costs of composite floor structures composed of a reinforced concrete slab and steel I-beams

  • The direct costs calculated in the energy life-cycle cost optimization and in the mass optimization are 20% and 30% higher, respectively, than the optimal direct costs calculated in the direct cost optimization

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Summary

Introduction

The optimization of steel–concrete composite structures can generally be conducted under several relevant criteria. The single-objective MINLP approach was used to perform the separate optimizations of the addressed structural system under the same design constraints but by considering different objectives: the minimization of mass, minimization of direct costs, and minimization of energy life-cycle costs. Define three different objective functions of CFS to be considered when performing structural optimizations and design comparisons: Mass objective function; Direct cost objective function, as originally introduced in references Klanšek and Kravanja [4,5] and later extended in the reference written by Kravanja et al [14]; Energy life-cycle cost objective function, which includes direct cost items, as stated in the previous three references, and the energy operating costs required for an adequate structure use;. Where MASS denotes the mass of the structure per unit of its useable surface, ρc and Vc represent the concrete density and its associated volume, ρs is the steel density, while Vs

Mass Objective Function
Direct Cost Objective Function
Energy Life-Cycle Cost Objective Function
Multi-Parametric MINLP Optimization
Objective
Comparisons between the Obtained Costs and Mases of Different Objective F
Findings
Conclusions
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