Automated optimization of steel reinforcement in RC building frames using building information modeling and hybrid genetic algorithm

Design of steel reinforcement is an important and necessary task for designing reinforced concrete (RC) building structures. Currently, steel reinforcement design is performed manually or semi-automatically using computer software such as ETABS, with reference to building codes. These approaches are time consuming and sometimes error-prone. Recent advances in building information modeling (BIM) technology allow digital 3D BIM models to be leveraged for supporting different types of engineering analyses such as structural engineering design. With the aid of BIM technology, steel reinforcement design could be automated for fast, economical and error-free procedures. This paper presents a BIM-based framework using the developed three-stage hybrid genetic algorithm (GA) for automated optimization of steel reinforcement in RC frames. The methodology framework determines the selection and alignment of steel reinforcement bars in an RC building frame for the minimum steel reinforcement area, considering longitudinal tensile, longitudinal compressive and shear steel reinforcement. The first two stages optimize the longitudinal tensile and longitudinal compressive steel reinforcement while the third stage optimizes the shear steel reinforcement. International design code (BS8110) and buildability constraints are considered in the developed optimization framework. A BIM model in Industry Foundation Classes (IFC) is then automatically created to visualize the optimized steel reinforcement design results in 3D thereby facilitating design communication and generation of construction detailing drawings. A three-storey RC building frame is analyzed to check the applicability of the developed framework and its improvement over current design approaches. The results show that the developed methodology framework can minimize the steel reinforcement area quickly and accurately.