Architecture / Mimarlık
Permanent URI for this collectionhttps://hdl.handle.net/11147/24
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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)Article Citation - WoS: 23Citation - Scopus: 25Analysing Visual Pattern of Skin Temperature During Submaximal and Maximal Exercises(Elsevier Ltd., 2016) Balcı, Görkem Aybars; Başaran, Tahsin; Çolakoğlu, MuzafferAims of this study were to examine our hypotheses assuming that (a) skin temperature patterns would differ between submaximal exercise (SE) and graded maximal exercise test (GXT) and (b) thermal kinetics of Tskin occurring in SE and GXT might be similar in a homogenous cohort. Core temperature (Tcore) also observed in order to evaluate thermoregulatory responses to SE and GXT. Eleven moderately to well-trained male athletes were volunteered for the study (age: 22.2 ± 3.7 years; body mass: 73.8 ± 6.9 kg; height: 181 ± 6.3 cm; body surface area 1.93 ± 0.1 m2; body fat: 12.6% ± 4.2%; V̇O2 max: 54 ± 9.9 mL min-1 kg-1). Under stabilized environmental conditions in climatic chamber, GXT to volitional exhaustion and 20-min SE at 60% of VO2 max were performed on cycle ergometer. Thermal analyses were conducted in 2-min intervals throughout exercise tests. Tskin was monitored by a thermal camera, while Tcore was recorded via an ingestible telemetric temperature sensor. Thermal kinetic analyses showed that Tskin gradually decreased till the 7.58 ± 1.03th minutes, and then initiated to increase till the end of SE (Rsqr = 0.97), while Tskin gradually decreased throughout the GXT (Rsqr = 0.89). Decrease in the level of Tskin during the GXT was significantly below from the SE [F (4, 40) = 2.67, p = 0.07, ηp 2 = 0.211]. In the meantime, Tcore continuously increased throughout the SE and GXT (p < 0.05). Both GXT and SE were terminated at very close final Tcore values (37.8 ± 0.3 °C and 38.0 ± 0.3 °C, respectively; p > 0.05). However, total heat energies were calculated as 261.5 kJ/m2 and 416 kJ/m2 for GXT and SE, respectively (p < 0.05). Thus, it seems that SE may be more advantageous than GXT in thermoregulation. In conclusion, Tcore gradually increased throughout maximal and submaximal exercises as expected. Tskin curves patterns found to be associated amongst participants at both GXT and SE. Therefore, Tskin kinetics may ensure an important data for monitoring thermoregulation in exercise.