Effects Of Building Characteristics Research Articles

Study on the Response of the Summer Land Surface Temperature to Urban Morphology in Urumqi, China

Increases in urban temperature affect the urban ecological environment and human health and well-being. In urban morphology, building characteristics are important factors affecting the land surface temperature (LST). Contemporary research focuses mainly on the effects of land use, urban tissue configuration, and street networks on the LST, and the effects of building characteristics on the LST need to be further understood. The mean LST and the urban morphology indicators of a single grid were calculated via a remote sensing inversion and a spatial analysis, and a geographically weighted regression (GWR) model was established to explore the influence of the building coverage ratio (BCR), mean building height (BH_mean), floor area ratio (FAR), and mean sky view factor (SVF_mean) on the LST. The results show that the correlations between the urban morphology indicators and the LST at a scale of 100~500 m are of different degrees, and the correlations are more significant at a scale of 200 m. Therefore, the optimal spatial scale for studying the influence of urban morphology indicators on the LST is 200 m. The fitting effect of the GWR model is significantly better than that of the ordinary least squares (OLS) method, and the effects of each indicator on the thermal environment have spatial non-stationarity. The BCR, BH_mean, FAR, and SVF_mean differ in their ability to raise and lower the temperature in different spatial zones, and the order of influence is as follows: BCR > SVF_mean > FAR > BH_mean. This study will provide a reference for the urban planning of Urumqi.

The Impact of Evaluated Daylight to the Total Light Ratio on the Comfort Level in Office Buildings

One of the main challenges in visual comfort assessment is controlling daylight in indoor spaces. The effect of daylight’s contribution to total light is one of the variables influencing how people perceive illumination in an indoor environment. This study investigates the optimal day-light-to-total light ratio that delivers the most satisfaction with the lighting environment. Therefore, a subjective survey of 509 questionnaires and field measurements in six office buildings in Tehran with a total of 257 rooms was conducted to assess lighting quality (daylight and artificial light). Furthermore, the effects of building characteristics and seasons on the acceptable range of daylight ratio are investigated. The results reveal that occupants prefer daylight to total light ratio ranging between 0.56 and 0.8. In contrast, occupants reported that a ratio less than 0.4 was unacceptable. It was also found that the optimum daylight-to-total light ratio is influenced by the season and the building characteristics.

A data-driven model for building energy normalization to enable eco-feedback in multi-family residential buildings with smart and connected technology

In this paper, we present a new unit-level data-driven modelling approach to normalize heating and cooling (HC) energy usage in multi-family residential buildings based on easily accessible data from smart thermostats and WiFi-enabled power metres. Our physics-informed approach starts from a heat balance equation to derive a linear regression model and uses a Bayesian mixture model to identify groups of units that have similar regression coefficients. Our model captures the effect of behaviour on HC energy consumption by normalizing the effect of building characteristics and accounting for the inter-unit heat transfer and unobserved variables. Our probabilistic approach incorporates unit- and season-specific prior information and sequential Bayesian updating of model parameters when new data become available. Using yearly data collected in a multi-family building, our model identifies distinct normalized HC energy use groups in different seasons and provides more accurate rankings compared to the case without normalization.

Correction to: Indoor and outdoor concentrations of BTEX and formaldehyde in Tehran, Iran: effects of building characteristics and health risk assessment.

The original publication of this paper contains a mistake. The correct name and affiliation of the 3rd Author is presented in this paper.

Indoor and outdoor concentrations of BTEX and formaldehyde in Tehran, Iran: effects of building characteristics and health risk assessment.

BTEX (benzene, toluene, ethylbenzene, xylene) and formaldehyde are toxic compounds that can induce adverse health effect in humans. This study measured in-home and ambient concentrations of BTEX and formaldehyde across Tehran, Iran. These pollutants were sampled from the indoor and adjacent outdoor air of 45 houses (9 in each city zone) during the winter of 2015. Sampling was repeated three times for each house. The analyses were performed according to NIOSH procedures. The effect of flooring material, wall covering, ventilation system, heating system, height above ground, presence of attached garages, and distance from highways was evaluated. In addition, carcinogenic and non-carcinogenic risks of these compounds were assessed. The average indoor concentrations of benzene, toluene, ethylbenzene, xylene, and formaldehyde were 53.2, 21.5, 14.4, 21.1, and 17.9μg/m3, respectively. The average outdoor concentrations of benzene, toluene, ethylbenzene, xylene, and formaldehyde were 43.5, 26.2, 10.0, 19.1, and 6.9μg/m3, respectively. Separate regression models showed that wall coating, ventilation system, heating system, flat level, and distance from highways explained 29, 60, 16, 60, and 59% of the BTEX concentrations, respectively. Houses with oil painted walls and parquet flooring had higher concentrations of BTEX and formaldehyde, respectively. The health risk assessment found that the carcinogenic risks of benzene and formaldehyde exceeded 1 × 10-4 and represent a definite risk. New buildings can be designed based on the results of this study to use better materials and optimum building designs to reduce exposure to these toxic air pollutants.

Factors affecting building security cost sustainability using PCA

Purpose – The purpose of this study is to evaluate built environment experts’ perception of factors affecting the building security cost for sustainable development. It examines the effects of building characteristics and protective measures on the cost of building security within the built environment. Design/methodology/approach – The study uses a quantitative research technique, using questionnaires to source relevant information from respondents. Of the 333 questionnaires administered, 293 usable responses were returned, giving an 88 per cent response rate. Exploratory factor analysis (EFA) is used to examine the suitability of these data for factor analysis. Principal component analysis (PCA) is used to extract the factors. Findings – Components 1, 2 and 3 reflect concern for procurement of security devices, design process of security requirements and safety of life and property and fear of crime, respectively. The communalities represented by R2 are relatively strong across the variables. Aesthetics is significant with an R2 value of 0.71, which shows that this factor should be given due consideration when procuring building security devices. Also, height, location and use of building are significant with R2 values of 0.70, 0.63 and 0.71, respectively. These factors positively influence the building security cost and should be given due consideration when designing protective buildings. Practical implications – The findings would assist in the evaluation, planning and control of the rising cost of building security. Social implications – This study serves to sensitize built environment experts, criminologists and policymakers of the design implication of protective requirements of building security. Originality/value – This study provides empirical evidence that the various factors considered have an effect on the building security cost and contribute immensely towards sustainable the building security cost within the built environment.

Allergens and β-Glucans in Dutch Homes and Schools: Characterizing Airborne Levels

BackgroundIndoor air quality has an effect on respiratory health. Children are more vulnerable to a decreased indoor air quality as their lungs are still developing. We measured levels of allergens and β-(1,3)-glucans in 19 school buildings and determined whether measured levels could be reproduced. School levels were compared to those in 169 homes and the effect of building characteristics on both home and school exposure was explored.MethodsElectrostatic Dust fall Collectors were placed in school buildings for 8 weeks and in homes for 2 weeks to collect settled airborne dust. Cat, dog, and mouse allergen levels, domestic mite antigen levels and β-(1,3)-glucans were measured in the extracts from the collectors. Results were corrected for sampling duration. Using questionnaire data, relations between measured levels and building and classroom characteristics were explored.ResultsIn schools, exposure levels were highest in classrooms and were influenced by the socioeconomic status of the children, the season measurements were performed, moisture status of the building and pet ownership. Repeated measurements in different seasons and over the years showed significantly different levels. Home exposure was influenced by socioeconomic status, occupancy and pet ownership. Domestic mite antigen was found in higher levels in extracts from homes compared to schools while pet allergen levels were 13 times higher in schools compared to homes without pets. For mouse allergen overall levels of exposure were low but still two times higher in schools compared to homes. Levels of β-(1,3)-glucans were also approximately two times higher in schools than in homes.ConclusionExposure levels of several allergens and β-(1,3)-glucans in schools differ over time and are higher than in homes. For children, exposure levels measured at school could contribute to their total exposure as especially animal allergen levels can be much higher in schools compared to homes.

Green Roof Adoption in Atlanta, Georgia: The Effects of Building Characteristics and Subsidies on Net Private, Public, and Social Benefits

This research draws on and expands previous studies that have quantified the costs and benefits associated with conventional roofs versus green roofs. Using parameters from those studies to define alternative scenarios, we estimate from a private, public, and social perspective the costs and benefits of installing and maintaining an extensive green roof in Atlanta, GA. Results indicate net private benefits are a decreasing function of roof size and vary considerably across scenarios. In contrast, net public benefits are highly stable across scenarios, ranging from $32.49 to $32.90 m(-2). In addition, we evaluate two alternative subsidy regimes: (i) a general subsidy provided to every building that adopts a green roof and (ii) a targeted subsidy provided only to buildings for which net private benefits are negative but net public benefits are positive. In 6 of the 12 general subsidy scenarios the optimal public policy is not to offer a subsidy; in 5 scenarios the optimal subsidy rate is between $20 and $27 m(-2); and in 1 scenario the optimal rate is $5 m(-2). The optimal rate with a targeted subsidy is between $20 and $27 m(-2) in 11 scenarios and no subsidy is optimal in the twelfth. In most scenarios, a significant portion of net public benefits are generated by buildings for which net private benefits are positive. This suggests a policy focused on information dissemination and technical assistance may be more cost-effective than direct subsidy payments.

Residential Energy Use and the City-Suburb Dichotomy

Data from the 2005 Residential Energy Consumption Survey (RECS) indicate suburbanites, compared with city dwellers, have on average higher per-capita residential energy consumption. However, it is not clear why suburbanites tend to consume more energy at home. Using a two-group structural equation modeling, I analyzed direct and total effects of household’s sociodemographic characteristics, income, and building’s characteristics on per-capita energy consumption for suburbanites and city-dwellers. Results show that while modeling results show that household’s sociodemographic characteristics have the largest total effect on energy consumption per-capita in both models, the difference between the magnitudes of effect is only marginal between cities and suburbs. The direct impact of income on energy consumption index is also identical between the two models; however, the total effect is bigger for the suburbanites. Impact of income on housing characteristics is also bigger for the suburbanites. Overall, the biggest difference between cities and suburbs appears to be the effect of building characteristics on per-capita energy consumption, with suburban homes being 19% more influential in increasing per-capita energy consumption. Such an increased impact also carries the indirect effect of households’ characteristics, implying an often-neglected hierarchy of effects and consequences between household characteristics, housing characteristics and residential energy outcomes.

The Effects of Building Characteristics and Site Conditions on the Damage Distribution in Boumerdès after the 2003 Algeria Earthquake

This study highlights the major contributing factors to the observed damage distribution in the city of Boumerdès after the 2003 Algeria earthquake. The results of field investigations and statistical analyses show that a majority of the damaged buildings, mostly mid-rise reinforced concrete (RC) moment-frame systems, were located on steep slopes and small hilltops, along river valleys. The horizontal-to-vertical (H/V) ratios from free-field microtremor measurements at these sites did not show clear results. In contrast, buildings with the same structural characteristics located on flat ground did not suffer much damage, and clear peaks were observed from the H/V ratio curves. The amplification effects of topography have not been incorporated into the revised Algerian seismic code, but the results from this study show the importance of considering this factor when designing new buildings for earthquake resistance.

Effect of Building Characteristics on Vibration-Induced Acceleration of Non-Structural Components

The effective accelerations for non-structural components located at different heights in a building are amplified due to the vibrations of the building and the components themselves. The amplifications along the height of buildings having different heights, structural systems and stiffness were studied. Four, eight and sixteen storied, frame and frame-shear wall buildings with similar plan and storey heights, designed as per relevant Indian codes of practice, were considered in the study. The effect of the building characteristics on the amplification of motion was studied and the observed amplification was compared with the various design practices. It was observed that the design practices overestimate the floor amplification and underestimate the component amplification. However, for eight storey bare frame building, the overall amplification having the combined effect of floor amplification and component amplification was observed to be close to that specified by FEMA. African Research Review Vol. 2 (4) 2008: pp. 69-87

MODELING FLOOD DAMAGE: CASE OF TOKAI FLOOD 2000

: In conventional flood damage reduction studies, flood damage is usually estimated with a damage function according to the depth of inundation. However, this method may not reflect the conditions of each family residing in the floodplain because it ignores not only the distribution of flood damage but also the effect of building characteristics and residents' preparedness. This paper uses data from a questionnaire based survey (N= 3,036) conducted 17 months after the Tokai Flood of 2000 that caused disastrous losses to household properties. It provides a conceptual “doughnut structure” model of flood damage to houses and house contents and a mathematical basis for models to explore the determinants of flood damage. Besides the inundation depth, house type significantly affects both the house structural and content damage probabilities, while house ownership and house structure affect house damage probability but not house content damage probability at a given depth. Inundation depth, residing period, and household income significantly affect both house and content damage values. In addition, house ownership has a significant impact on the house damage value, while house structure has an impact on content damage value.