The Thermal Environment of Housing and Its Implications for the Health of Older People in South Australia: A Mixed-Methods Study

Older people are often over-represented in morbidity and mortality statistics associated with hot and cold weather, despite remaining mostly indoors. The study “Improving thermal environment of housing for older Australians” focused on assessing the relationships between the indoor environment, building characteristics, thermal comfort and perceived health/wellbeing of older South Australians over a study period that included the warmest summer on record. Our findings showed that indoor temperatures in some of the houses reached above 35 °C. With concerns about energy costs, occupants often use adaptive behaviours to achieve thermal comfort instead of using cooling (or heating), although feeling less satisfied with the thermal environment and perceiving health/wellbeing to worsen at above 28 °C (and below 15 °C). Symptoms experienced during hot weather included tiredness, shortness of breath, sleeplessness and dizziness, with coughs and colds, painful joints, shortness of breath and influenza experienced during cold weather. To express the influence of temperature and humidity on perceived health/wellbeing, a Temperature Humidity Health Index (THHI) was developed for this cohort. A health/wellbeing perception of “very good” is achieved between an 18.4 °C and 24.3 °C indoor operative temperature and a 55% relative humidity. The evidence from this research is used to inform guidelines about maintaining home environments to be conducive to the health/wellbeing of older people.

Long-Term Impacts of Weather Conditions on Indoor Radon Concentration Measurements in Switzerland

Radon is a natural and radioactive gas that can accumulate in indoor environments. Indoor radon concentration (IRC) is influenced, among other factors, by meteorology, which is the subject of this paper. Weather parameters impact indoor radon levels and have already been investigated, but rarely in Switzerland. Moreover, there is a strong need for a better understanding of the radon behaviour inside buildings in Switzerland for public health concerns as Switzerland is a radon prone area. Based on long-term, continuous, and hourly radon measurements, radon distributions classified according to different weather event definitions were investigated and then compared at three different study sites in Western Switzerland. Outdoor temperature influences the most indoor radon, and it is globally anti-correlated. Wind influences indoor radon, but it strongly depends on intensity, direction, and building characteristics. Precipitation influences periodically indoor radon levels relatively to their intensity. Atmospheric pressure and relative humidity do not seem to be huge determinants on IRC. Our results are in line with previous findings and provide a vivid example in Western Switzerland. This paper underlines the different influence complexities of radon, and the need to communicate about it within the broader public and with construction professionals, to raise awareness.

Influence of Building Characteristics and Building Lifespan on Condominium Operating Expenses

This paper is an exploration of building lifespan, building characteristics, and operating expenses. The main objectives are to identify the building component lifespan, including architectural components and engineering components, to determine the pattern of building component replacement life cycle and to examine the relationship between building characteristics and facility operating expenses. The investigation was undertaken through a study of thirty-nine residential condominiums located in Bangkok. The expense data were collected through document searches and surveys with key juristic persons of each condominium. The building service life document was collected from international references and standards. The data were examined using cross-case analysis to identify the lifespan of the buildings and to identify the relationships between the condominium operating expenses and the characteristics of the buildings. It was found that the typical building replacements occur on a broad 60-year cycle that can be subdivided into several phases. Further findings indicate that a significant pattern of building component replacement shifts every two decades through the building lifespan. It was also found that the condominium operating expenses vary according to the building age and building characteristics. Direct variation, inverse variation, and joint variation from the characteristics of the condominium building can be identified. The findings add to the understanding of condominium operating expenses based on building characteristics. The study can provide a reference for consideration of building selection criteria and replacement plans, and for building budget planning based on age and building characteristics.

DCRN: An Optimized Deep Convolutional Regression Network for Building Orientation Angle Estimation in High-Resolution Satellite Images

Recently, remote sensing satellite image analysis has received significant attention from geo-information scientists. However, the current geo-information systems lack automatic detection of several building characteristics inside the high-resolution satellite images. The accurate extraction of buildings characteristics helps the decision-makers to optimize urban planning and achieve better decisions. Furthermore, Building orientation angle is a very critical parameter in the accuracy of automated building detection algorithms. However, the traditional computer vision techniques lack accuracy, scalability, and robustness for building orientation angle detection. This paper proposes two different approaches to deep building orientation angle estimation in the high-resolution satellite image. Firstly, we propose a transfer deep learning approach for our estimation task. Secondly, we propose a novel optimized DCRN network consisting of pre-processing, scaled gradient layer, deep convolutional units, dropout layers, and regression end layer. The early proposed gradient layer helps the DCRN network to extract more helpful information and increase its performance. We have collected a building benchmark dataset that consists of building images in Riyadh city. The images used in the experiments are 15,190 buildings images. In our experiments, we have compared our proposed approaches and the other approaches in the literature. The proposed system has achieved the lowest root mean square error (RMSE) value of 1.24, the lowest mean absolute error (MAE) of 0.16, and the highest adjusted R-squared value of 0.99 using the RMS optimizer. The cost of processing time of our proposed DCRN architecture is 0.0113 ± 0.0141 s. Our proposed approach has proven its stability with the input building image contrast variation for all orientation angles. Our experimental results are promising, and it is suggested to be utilized in other building characteristics estimation tasks in high-resolution satellite images.

Spatial analysis of public residential housing's electricity consumption in relation to urban landscape and building characteristics: A case study in Singapore

In a highly populated country like Singapore, a significant percentage of our gross annual electricity consumption stems from our domestic electricity usage in our residential houses. Analyzing and understanding factors that could influence such patterns is thus essential in order to derive effective measures to reduce usage. In this research, 16 identified variables were calculated and considered in the spatial analyses based on various buffer sizes. Both multilinear regression (MLR) and geographically weighted regression (GWR) based analyses were conducted using each residential housing's Energy Unit Intensity (EUI) as the dependent variable. The analyzed results have shown that building characteristics variables have more significant influences towards energy consumption patterns as compared to urban landscape variables. Although little difference was observed across different buffer sizes, more reliable results were obtained from a smaller buffer size of 50 m, suggesting its suitability in using these obtained values for further prediction model analysis and development. Results obtained from the GWR-based analysis have shown a significant improvement in the goodness-of-fit value compared to the MLR-based analysis, effectively indicating that GWR performs better in this context, apart from its better explanation on the contribution of these identified variables to the EUI in this case study.

DETECTION AND SPATIAL ANALYSIS OF LAND-USE: A CASE OF BUTUAN CITY WITH HISTORY OF MAJOR INFORMAL SETTLEMENTS

This study detects the significant informal settlements in Butuan City proper. It determines the growth rate in 15 years with the given five-year interval. Machine learning algorithms and spatial analysis were applied to obtain the possible locations of informal settlement buildings. The projected locations of informal settlement buildings were validated thru aerial image validation using Remote Sensing and GIS-based techniques in ArcGIS software. Eight (8) barangays satisfy all the informal settlement building characteristics during the aerial validation process and ground-truthing, namely, Golden Ribbon, Holy Redeemer, Limaha, New Society, Ong Yiu, Port Puyohon, San Ignacio, and Tandang Sora. The eight (8) barangays were manually digitized from the given 5-years interval from 2005, 2010, 2015, and 2010. The value of the major informal settlement buildings area was computed to excel. The area growth rate was calculated using the growth rate formula. This study showed that the significant informal settlement in the study area increased. Among the eight (8) focused barangays, Tandang Sora ranked the highest informal settlements growth from 2005 to 2020. Its area increases up to 178.52%, a total of 24,608.43 square meters. Finally, the results revealed that the area of informal settlement buildings in Butuan City from 2005–2020, in 15-years, its value increases up to 9.74%, a total of 19,172.88 square meters.

A Machine Learning approach to enhance indoor thermal comfort in a changing climate

This paper presents an alternative workflow for thermal comfort prediction. By using the leverage of Data Science & AI in combination with the power of computational design, the proposed methodology exploits the extensive comfort data provided by the ASHRAE Global Thermal Comfort Database II to generate more customised comfort prediction models. These models consider additional, often significant input parameters like location and specific building characteristics. Results from an early case study indicate that such an approach has the potential for more accurate comfort predictions that eventually lead to more efficient and comfortable buildings.