High-Rise Morphological Design in Dense Urban Contexts: A Parametric Modeling and Evolutionary Optimization Approach

Le résumé fourni par la source

As cities grow increasingly complex, the demand for adaptive, performance-driven urban design strategies has intensified. While computational design approaches have shown considerable promise in multiple design disciplines, their application to large-scale urban morphology, especially high-rise development, remains limited. To this end, this study introduces an advanced morphological engine that integrates parametric modeling, environmental simulation, and evolutionary optimization to generate and evaluate high-rise design alternatives in dense metropolitan contexts. Using Beirut as a case study, the framework systematically varies key design parameters, such as rotation, setbacks, and vertical segmentation, producing a broad spectrum of feasible configurations. Each alternative is evaluated against performance indicators addressing solar radiation, thermal gain, and visual integration with the urban fabric. Multi-objective optimization is achieved through the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), yielding a Pareto front of non-dominated solutions that capture trade-offs among competing objectives. Results highlight that optimized morphological adjustments can significantly enhance both environmental performance and contextual livability, with the top-ranked solution demonstrating a 15% performance improvement over the weakest configuration. The findings underscore the value of optimization-driven design as a decision-support tool for balancing sustainability, aesthetics, and contextual integration in high-rise development. The proposed framework advances evidence-based design practices and supports the creation of resilient, livable, and contextually responsive urban forms.

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