Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article AI-Supported Seismic Performance Evaluation of Structures: Challenges, Gaps, and Future Directions at Early Design Stages(Elsevier Sci Ltd, 2026) Ak, Fatma; Ekici, Berk; Demir, UgurThis study reviews 91 journal articles that intersect with earthquake-resistant building design and artificial intelligence (AI)- based modeling, utilizing machine learning, deep learning, and metaheuristic optimization algorithms. Previous reviews on AI applications have examined engineering problems without considering the impact of architectural design parameters and structural irregularities on seismic performance. This review discusses the role of AI in integrating architectural design variables and seismic performance objectives, highlighting challenges, gaps, and future directions in the early design phase. The reviewed articles demonstrate that AI is successful in addressing seismic performance objectives; however, a holistic framework for assessing architectural and structural variables has not been presented. The review highlights key findings, gaps, and future directions for those involved in earthquake-resistant building design utilizing AI.Article Citation - WoS: 27Citation - Scopus: 34Multi-Zone Optimisation of High-Rise Buildings Using Artificial Intelligence for Sustainable Metropolises. Part 2: Optimisation Problems, Algorithms, Results, and Method Validation(Pergamon-Elsevier Science LTD, 2021) Ekici, Berk; Kazanasmaz, Zehra Tuğçe; Turrin, Michela; Taşgetiren, M. Fatih; Sarıyıldız, I. SevilHigh-rise building optimisation is becoming increasingly relevant owing to global population growth and urbanisation trends. Previous studies have demonstrated the potential of high-rise optimisation but have been focused on the use of the parameters of single floors for the entire design; thus, the differences related to the impact of the dense surroundings are not taken into consideration. Part 1 of this study presents a multi-zone optimisation (MUZO) methodology and surrogate models (SMs), which provide a swift and accurate prediction for the entire building design; hence, the SMs can be used for optimisation processes. Owing to the high number of parameters involved in the design process, the optimisation task remains challenging. This paper presents how MUZO can cope with an enormous number of parameters to optimise the entire design of high-rise buildings using three algorithms with an adaptive penalty function. Two design scenarios are considered for quad-grid and diagrid shading devices, glazing type, and building-shape parameters using the setup, and the SMs developed in part 1. The optimisation part of the MUZO methodology reported satisfactory results for spatial daylight autonomy and annual sunlight exposure by meeting the Leadership in Energy and Environmental Design standards in 19 of 20 optimisation problems. To validate the impact of the methodology, optimised designs were compared with 8748 and 5832 typical quad-grid and diagrid scenarios, respectively, using the same design parameters for all floor levels. The findings indicate that the MUZO methodology provides significant improvements in the optimisation of high-rise buildings in dense urban areas.Article Citation - WoS: 38Citation - Scopus: 50Multi-Zone Optimisation of High-Rise Buildings Using Artificial Intelligence for Sustainable Metropolises. Part 1: Background, Methodology, Setup, and Machine Learning Results(Elsevier Ltd., 2021) Ekici, Berk; Kazanasmaz, Zehra Tuğçe; Turrin, Michela; Taşgetiren, M. Fatih; Sarıyıldız, I. SevilDesigning 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 The Author(s)Conference Object Citation - WoS: 6Citation - Scopus: 13A Methodology for Daylight Optimisation of High-Rise Buildings in the Dense Urban District Using Overhang Length and Glazing Type Variables With Surrogate Modelling(Iop Publishing Ltd, 2019) Ekici, Berk; Kazanasmaz, Zehra Tuğçe; Turrin, Michela; Taşgetiren, M. Fatih; Sarıyıldız, I. SevilUrbanization and population growth lead to the construction of higher buildings in the 21st century. This causes an increment on energy consumption as the amount of constructed floor areas is rising steadily. Integrating daylight performance in building design supports reducing the energy consumption and satisfying occupants' comfort. This study presents a methodology to optimise the daylight performance of a high-rise building located in a dense urban district. The purpose is to deal with optimisation problems by dividing the high-rise building into five zones from the ground level to the sky level, to achieve better daylight performance. Therefore, the study covers five optimization problems. Overhang length and glazing type are considered to optimise spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE). A total of 500 samples in each zone are collected to develop surrogate models. A self-adaptive differential evolution algorithm is used to obtain near-optimal results for each zone. The developed surrogate models can estimate the metrics with minimum 98.25% R2 which is calculated from neural network prediction and Diva simulations. In the case study, the proposed methodology improves daylight performance of the high-rise building, decreasing ASE by approx. 27.6% and increasing the sDA values by around 88.2% in the dense urban district. © Published under licence by IOP Publishing Ltd.