ANN-based optimization framework for the design of wind load resisting system of tall buildings

Alanani, Magdy; Elshaer, Ahmed

doi:10.1016/j.engstruct.2023.116032

Cited by 12 publications

(1 citation statement)

References 34 publications

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“…To determine the relationship between building geometry and thermal index, 800 datasets were used for ANN training. Based on experience, 70% of the datasets (560 datasets) were randomly used for training, 15% (120 datasets) were randomly used for validation, and the remaining datasets were used for testing [58,59]. The next step involved the construction of a surrogate model structure.…”

Section: Machine Learning Surrogate Model Training and Validationmentioning

confidence: 99%

Energy Saving Optimization of Commercial Complex Atrium Roof with Resilient Ventilation Using Machine Learning

Xu,

Zhang,

et al. 2023

Smart Cities

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Carbon-neutral architectural design focuses on rationally utilizing the building’s surroundings to reduce its environmental impact. Resilient ventilation systems, developed according to the thermal comfort requirements of building energy-saving research, have few applications. We studied the Jin-an Shopping Mall in Harbin and established the middle point height (h), middle point horizontal location (d), roof angle (α), and exposure to floor ratio (k) as the morphological parameters of the atrium. Using computational fluid dynamics (CFD), the mean radiant temperature (MRT), and the universal thermal climate index calculations (UTCI), this program was set to switch off air conditioning when the resilient ventilation met the thermal comfort requirement to achieve energy savings. The energy-saving efficiency (U) was calculated based on the energy consumption of the original model, and U could reach 7.34–9.64% according to the simulation and prediction. This study provides methods and a theoretical basis for renovating other commercial complexes to improve comfort and control energy consumption.

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Section: Machine Learning Surrogate Model Training and Validationmentioning

confidence: 99%