Architecture / Mimarlık

Permanent URI for this collectionhttps://hdl.handle.net/11147/24

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Subject: search.filters.subject.SustainabilityWoS Q: search.filters.wosQuartile.Q2

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  • Article
    Citation - WoS: 5
    Citation - Scopus: 5
    De-Sealing Reverses Habitat Decay More Than Increasing Groundcover Vegetation
    (MDPI, 2023) Couch, Virginia; Salata, Stefano; Saygın, Nicel; Frary, Anne; Arslan, Bertan
    Modeling ecosystem services is a growing trend in scientific research, and Nature-based Solutions (NbSs) are increasingly used by land-use planners and environmental designers to achieve improved adaptation to climate change and mitigation of the negative effects of climate change. Predictions of ecological benefits of NbSs are needed early in design to support decision making. In this study, we used ecological analysis to predict the benefits of two NbSs applied to a university masterplan and adjusted our preliminary design strategy according to the first modeling results. Our Area of Interest was the IZTECH campus, which is located in a rural area of the eastern Mediterranean region (Izmir/Turkey). A primary design goal was to improve habitat quality by revitalizing soil. Customized analysis of the Baseline Condition and two NbSs scenarios was achieved by using local values obtained from a high-resolution photogrammetric scan of the catchment to produce flow accumulation and habitat quality indexes. Results indicate that anthropogenic features are the primary cause of habitat decay and that decreasing imperviousness reduces habitat decay significantly more than adding vegetation. This study creates a method of supporting sustainability goals by quickly testing alternative NbSs. The main innovation is demonstrating that early approximation of the ecological benefits of NbSs can inform preliminary design strategy. The proposed model may be calibrated to address specific environmental challenges of a given location and test other forms of NbSs.
  • Article
    Citation - WoS: 27
    Citation - Scopus: 34
    Multi-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. Sevil
    High-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: 38
    Citation - Scopus: 50
    Multi-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. Sevil
    Designing 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)