Multi-Objective Performance Evaluation Framework for Integrated Fiber-Reinforced Shading Systems

Shading systems are associated by their ability to control various factors such as energy consumption, visual comfort, and natural ventilation. To fulfill such economic, environmental, and social requirements, the use of integrated modular fiber-Reinforced Concrete (FRC) shading systems has become popular in recent years. While being more environmentally friendly, Fiber-reinforced concrete panels are also associated with lower costs. However, designing such systems are often associated with onerous time-consuming processes which has impacted their market uptake. To support designers since the early design phase, this research proposes an integrated systemic framework. The proposed framework benefits from integrating Multi-objective Evolutionary Algorithms (MOEAs). Such algorithms have proven to address conflicting parameters effectively while providing a range of different solutions to problems. Furthermore, the proposed framework incorporates the use of Pareto-front and Ranking method to better support decision making. Moreover, case study validates the efficacy of the framework in daylighting performance evaluation of an integrated modular FRC shading device for an office room. Finally, the results indicate that the proposed framework can provide a variety of optimal solutions to conflicting goals and further support performance evaluation of an integrated modular fiber-reinforced shading system.