Privacy-responsive optimization of double-layer mashrabiya façades using visibility metrics

This study presents a computational framework for optimizing privacy-responsive façade systems in residential environments. The research operationalizes visual exposure and visual access as quantifiable performance metrics to evaluate the balance between privacy and outward visibility in housing design. A parametric double-layer façade configuration inspired by the traditional mashrabiya is developed and evaluated using ray-based visibility analysis and evolutionary multi-objective optimization. The workflow evaluates 1000 design instances, which collapse to 89 unique objective-space points. Non-dominated filtering identifies 5 unique Pareto-optimal points within the evaluated optimization population. Comparison with three benchmark façade configurations extends the combined non-dominated set to 7 unique points, of which two are benchmark-derived and one benchmark case is dominated. A representative solution is selected from the optimization-derived Pareto front using a knee-point criterion based on normalized distance to the utopia point. The results demonstrate how façade geometry can be systematically tuned to support balanced privacy-responsive design in dense residential contexts.