Berk EkiciTugce KazanasmazMichela TurrinM. Fatih TasgetirenI. Sevil SariyildizEkici, BerkTurrin, MichelaTasgetiren, M. FatihSariyildiz, I. SevilKazanasmaz, Z. Tugce2025-10-0620210038092X0038-092X1471-125710.1016/j.solener.2021.05.0832-s2.0-85107932246https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107932246&doi=10.1016%2Fj.solener.2021.05.083&partnerID=40&md5=79463eb05b810f0feb0e20f6813197fchttps://gcris.yasar.edu.tr/handle/123456789/8934https://doi.org/10.1016/j.solener.2021.05.083Designing high-rise buildings is one of the complex tasks of architecture because it involves interdisciplinary performance aspects in the conceptual phase. The necessity for sustainable high-rise buildings has increased owing to the demand for metropolises based on population growth and urbanisation trends. Although artificial intelligence (AI) techniques support swift decision-making when addressing multiple performance aspects related to sustainable buildings previous studies only examined single floors because modelling and optimising the entire building requires extensive computational time. However different floor levels require various design decisions because of the performance variances between the ground and sky levels of high-rises in dense urban districts. This paper presents a multi-zone optimisation (MUZO) methodology to support decision-making for an entire high-rise building considering multiple floor levels and performance aspects. The proposed methodology includes parametric modelling and simulations of high-rise buildings as well as machine learning and optimisation as AI methods. The specific setup focuses on the quad-grid and diagrid shading devices using two daylight metrics of LEED: spatial daylight autonomy and annual sunlight exposure. The parametric model generated samples to develop surrogate models using an artificial neural network. The results of 40 surrogate models indicated that the machine learning part of the MUZO methodology can report very high prediction accuracies for 31 models and high accuracies for six quad-grid and three diagrid models. The findings indicate that the MUZO can be an important part of designing high-rises in metropolises while predicting multiple performance aspects related to sustainable buildings during the conceptual design phase. © 2021 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/openAccessBuilding Simulation, High-rise Building, Machine Learning, Optimization, Performance-based Design, Sustainability, Architectural Design, Conceptual Design, Decision Making, Floors, Intelligent Buildings, Machine Learning, Neural Networks, Optimization, Population Statistics, Sustainable Development, Building Simulation, Decisions Makings, Floor Level, High Rise, High Rise Building, Machine-learning, Optimisations, Performance Aspects, Performance Based Design, Sustainable Building, Tall Buildings, Artificial Intelligence, Building, Detection Method, Machine Learning, Methodology, Metropolitan Area, Optimization, Perforation, Performance Assessment, Research And Methodology, Sustainability, UrbanizationArchitectural design, Conceptual design, Decision making, Floors, Intelligent buildings, Machine learning, Neural networks, Optimization, Population statistics, Sustainable development, Building simulation, Decisions makings, Floor level, High rise, High rise building, Machine-learning, Optimisations, Performance aspects, Performance based design, Sustainable building, Tall buildings, artificial intelligence, building, detection method, machine learning, methodology, metropolitan area, optimization, perforation, performance assessment, research and methodology, sustainability, urbanizationPerformance-Based DesignHigh-Rise BuildingMachine LearningBuilding SimulationOptimizationSustainabilityArticle