Courtyard is often regarded as a microclimate modifier that improves comfort conditions of the surrounding environment. While several literature have identified aspect ratio of a courtyard and its orientation as two design variants that are critical to the microclimatic performance of courtyards, this claim need to be substantiated with empirical evidence. This study assessed the microclimate performance of a U-shape courtyard in a General Hospital in Malaysia. The intention was to verify two critical design variants affecting the performance of an institutional scale courtyard in a tropical climate. The study combined experimental and simulation method. The field study recorded three physical environmental variables namely the air temperature, humidity and wind patterns. The simulation study was performed using IES suite in two parts beginning with a calibration procedure, followed by the parametric analysis. The result verifies that the manipulation of courtyard configuration and its orientation impact its microclimate modifying ability

Computational Fluid Dynamics (CFD) has been applied in many researches in order to investigate the ventilation in buildings. However, researches on natural ventilation inside courtyard using CFD techniques are rare. Courtyard aspect ratio and cantilevered roof are selected for investigation using CFD in IES software. Wind and air temperature was obtained and thus, the thermal comfort is evaluated by the Predicted Mean Vote (PMV) index. Results show that aspect ratio and cantilevered roof plays a significant effect on the wind speed and, consequently, the thermal comfort. The (1:2) plan aspect ratio performs better than the (1:1) plan aspect ratio

A courtyard is an architectural design element commonly applied in tropical buildings for its social, environmental and therapeutic potentials. However, effort to examine its usage and design variants is limited. This paper focuses on the application of courtyards in the context of Malaysian hospitals. Thirty-two courtyards in 19 hospitals were inventoried. Besides the courtyard functions, design variants were recorded in terms of its form and aspect ratio, courtyard orientation and physical features within the courtyard. The results revealed that the courtyards in the Malaysian hospitals buildings are creatively manipulated. The paper concludes with an outline of means to optimise a courtyard's microclimatic and healing performances

The dramatic growth in the number of buildings worldwide has led to an increase interest in predicting energy consumption, especially for the case of residential buildings. As the heating and cooling system highly affect the operation cost of buildings; it is worth investigating the development of models to predict the heating and cooling loads of buildings. In contrast to the majority of the existing related studies, which are based on historical energy consumption data, this study considers building characteristics, such as area and floor height, to develop prediction models of heating and cooling loads. In particular, this study proposes deep neural networks models based on several hyper-parameters: the number of hidden layers, the number of neurons in each layer, and the learning algorithm. The tuned models are constructed using a dataset generated with the Integrated Environmental Solutions Virtual Environment (IESVE) simulation software for the city of Buraydah city, the capital of the Qassim region in Saudi Arabia. The Qassim region was selected because of its harsh arid climate of extremely cold winters and hot summers, which means that lot of energy is used up for cooling and heating of residential buildings. Through model tuning, optimal parameters of deep learning models are determined using the following performance measures: Mean Square Error (MSE), Root Mean Square Error (RMSE), Regression (R) values, and coefficient of determination (R${}^2$). The results obtained with the five-layer deep neural network model, with 20 neurons in each layer and the Levenberg–Marquardt algorithm, outperformed the results of the other models with a lower number of layers. This model achieved MSE of 0.0075, RMSE 0.087, R and R${}^2$ both as high as 0.99 in predicting the heating load and MSE of 0.245, RMSE of 0.495, R and R${}^2$ both as high as 0.99 in predicting the cooling load. As the developed prediction models were based on buildings characteristics, the outcomes of the research may be relevant to architects at the pre-design stage of heating and cooling energy-efficient buildings.

The study focuses on addressing the growing concern of noise pollution resulting from increased transportation. Effective strategies are necessary to mitigate the impact of noise pollution. The study utilizes noise regression models to estimate road-traffic-induced noise pollution. However, the availability and reliability of such models can be limited. To enhance the accuracy of predictions, optimization techniques are employed. A dataset encompassing various landscape configurations is generated, and three regression models (regression tree, support vector machines, and Gaussian process regression) are constructed for noise-pollution prediction. Optimization is performed by fine-tuning hyperparameters for each model. Performance measures such as mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R²) are utilized to determine the optimal hyperparameter values. The results demonstrate that the optimization process significantly improves the models’ performance. The optimized Gaussian process regression model exhibits the highest prediction accuracy, with an MSE of 0.19, RMSE of 0.04, and R² reaching 1. However, this model is comparatively slower in terms of computation speed. The study provides valuable insights for developing effective solutions and action plans to mitigate the adverse effects of noise pollution.

Arid and hot regions, like Saudi Arabia, utilize up to 60% of the country’s energy to regulate buildings’ indoor comfort. Energy efficiency is a long-term sustainability measure that is part of the government’s Vision 2030 strategy. A standard method of improving the thermal performance of buildings is through the use of insulation materials. Considering the cooling loads’ requirement and the Global Warming Potential (GWP), the present research evaluated the effectiveness of insulation materials, including extruded polystyrene, expanded polystyrene, rock wool, and glass wool in the hot, arid climate. For this case study, four similar villas facing the cardinal directions were selected from the residential project at Qassim University. HOBO data loggers were used to collect indoor temperature data. Thermal performance and Life Cycle Assessment (LCA) were conducted in accordance with Saudi Building Code-602 (SBC-602). Simulation outputs based on the four cardinal directions were used for assessing the thermal performance and LCA of the different thicknesses and densities of insulation materials. This was done using IESVE and SimaPro, IMPACR2002+, to assess their cooling load and GWP, respectively. The results suggest the potential for using lower insulation thickness for the northern and western façades without violating the SBC. The results obtained the actual thicknesses of the three insulation materials for achieving indoor temperatures in the four cardinal directions and the selection of materials and their densities along with associated GWP. The outputs of the study have been generalized in the form of a performance-based flowchart as a tool for selecting the type and thickness of thermal and environmental insulation in residential buildings in the Qassim region of Saudi Arabia.