Architecture Engineers Award

This study investigates the application of six machine learning regression models to predict building performance in a residential unit located in Sari, Iran. Using a calibrated EnergyPlus model and three years of utility data, the research evaluates primary energy consumption, emissions, indoor air quality, thermal comfort, and visual discomfort. The aim is to enhance sustainable design decision-making by comparing the efficiency and accuracy of different models. Machine Learning Models in Building Performance The research evaluates Random Forest, K-Nearest Neighbors, Support Vector Regression, Artificial Neural Network, Extreme Gradient Boosting, and Linear Regression. Each model is tested against real-world performance indicators, highlighting their predictive strength and weaknesses in handling multidimensional building datasets. Dataset and Methodology A synthetic dataset of 1,826 configurations with 25 input variables was developed using EnergyPlus. The dataset was split in...

Traditional building energy simulation models often overlook the stochastic behavior of occupants and the complex interactions between multiple devices. This limitation creates a significant gap between predicted and actual building energy consumption. Addressing this issue requires behavior-sensitive frameworks that integrate both human and technical dimensions of building performance. The proposed study bridges this gap by introducing a fuzzy multi-criteria decision-making (FMCDM) approach coupled with evolutionary optimization to ensure realistic and adaptive performance predictions. Significance of Occupant Behavior in Energy Modeling Occupant behavior is one of the most influential yet uncertain factors in determining building energy performance. Stochastic patterns, such as irregular use of appliances, varying thermal preferences, and diverse daily routines, make deterministic models insufficient. Incorporating behavioral diversity through FMCDM provides more accurate results, ...

  Sustainable hospital design plays a pivotal role in reducing the ecological footprint of healthcare infrastructure while ensuring effective service delivery. In resource-constrained regions such as Jordan, the need for a balanced approach that integrates environmental, economic, and sociocultural dimensions has become increasingly vital. This study employs the Analytic Hierarchy Process (AHP) to evaluate multiple sustainability criteria, thereby offering a systematic framework for decision-making in hospital planning and development. Role of AHP in Sustainable Healthcare Planning The Analytic Hierarchy Process (AHP) is a multi-criteria decision-making tool that allows experts and policymakers to prioritize sustainability factors based on structured comparisons. In this study, AHP enabled the evaluation of seven primary sustainability criteria, capturing both quantitative and qualitative aspects. By ensuring logical consistency, AHP proved effective in aligning expert judgment ...