Roof Types Research Articles

A TAXONOMY STUDY OF MOSQUE ROOF DESIGN IN KLANG VALLEY, MALAYSIA

Energy is the most important issue to focus on as it contributes to economic development and social growth. Buildings were identified to significantly impact energy usage, where 40% of global energy comes from buildings. Nowadays, many buildings in a tropical climate are equipped with air-conditioners as it is seen as the fastest way to cool down buildings. However, this action impacts the environment. One of the affected buildings that are equipped with air-conditioners is a mosque. However, minimal attention has been given to mosques even though this type of building has a significant total space as other commercial buildings. Furthermore, mosques have a unique pattern of occupancy and energy use. While the major contributor to buildings' energy consumption comes from the roof, scarce information is found regarding roof design and energy consumption for air-conditioned mosques in Malaysia. The study aims at exploring the roof types of mosques in the Klang Valley. The objectives are 1) to identify mosques with air-conditioners in the Klang Valley built between 1998 and 2018, 2) to characterize and classify the specific roof types designed for these identified mosques, and 3) to investigate building energy intensity for mosque buildings in the Klang Valley. This paper presents a taxonomy study to classify roof design for air-conditioned mosques in Klang Valley. The study showed that the flat roof demonstrated high energy consumption and cost. The use of HVLS fans and the association with air-conditioners probably increased the building energy.

ГЕОДЕЗІЯ, КАРТОГРАФІЯ І АЕРОФОТОЗНІМАННЯ

The aim of this work is to develop an algorithm for mathematical three-dimensional modeling of a typical roof of a Ukrainian church based on ground-based laser scanning and to find ways to optimize the model depending on the input data set. Method. The accuracy of the simulation depends on the laser scan data. The number of points obtained and their accuracy will affect the final result - 3D model of the roof. Given the typical design of the church roof in the shape of a cone, you can apply the standard mathematical algorithm for modeling part of the buildings of a typical church. Result The proposed algorithm was developed in the MathCad software environment. 3D scanning materials of the Ukrainian typical church were used to develop the mathematical algorithm. The algorithm analyzes the location of the scanning points of the church roof and performs its averaging. As a result of the algorithm, erroneous measurements were rejected and a model of the part of the roof was obtained, which forms the optimal geometry of the structure. Scientific novelty and practical significance. The proposed mathematical algorithm allows to automate some modeling processes of a typical Ukrainian church for design decisions. This method of modeling can be used for similar structures of other buildings.

Community Scale Assessment of the Effectiveness of Designed Discharge Routes from Building Roofs for Stormwater Reduction

Urban flooding is increasing due to climate change and the expansion of impervious land surfaces. Green roofs have recently been identified as effective solutions for mitigating urban stormwater. However, discharge routes that involve receiving catchments of stormwater runoff from roofs to mitigate high flows have been limited. Thus, a hydrological model was constructed to investigate the effects of changing discharge routes on stormwater flow. Three hypothetical scenarios were assessed using various combinations of discharge routes and roof types. The reduction effects on outflow and overflow were identified and evaluated across six return periods of designed rainstorms in the Tai Hung Tulip House community in Beijing. The results showed that green roofs, together with the discharge routes connecting to pervious catchments, were effective in reducing peak flow (13.9–17.3%), outflow volume (16.3–27.3%), drainage overflow frequency, and flood duration. Although mitigation can be improved by considering discharge routes, it is limited compared to that achieved by the effects of green roofs. However, integrating green roofs and discharge routes can improve community resilience to rainstorms with longer return periods. These results provide useful information for effective design of future stormwater mitigation and management strategies in small-scale urban areas.

Performance assessment method for roof-integrated TSSCs

This paper proposes a performance assessment method for roof-integrated two-stage solar concentrators (TSSCs) as thermo-photovoltaic systems using a parametric modeling approach. The productivity of TSSCs does not only depend on system design, but also on building geometries ensuring good solar gains. Further, the integration process of TSSCs with buildings is complex and needs to address concerns from multiple domains. The proposed approach intends to be a rigorous yet adaptable method to inform decision making where multiple stakeholders from building and energy domains are involved, and promote the use of TSSCs as sustainable, building-integrated energy production technology. Our method contributes to engineering the TSSC designs in relation to the building design. The method allows generating various design alternatives by controlling a set of input parameters for buildings (i.e., roof type and slope, and building orientation) and TSCCs (i.e., design type, geometric ratio, and separation distance between mirrors). The method applies a parametric modeling approach that enables the generation of 20,736 design options of building and TSSCs in a collaborative manner, by featuring various combinations of the design inputs. We validated the method in an illustrative case study of a single-family house (California) for a number of performance indicators related to building and TSSCs e.g., the covered roof area, the annual average load match index (av.LMI), and the number of solar cells. We further perform a sensitivity analysis to find the relative importance of design parameters and discuss our results in terms of maximizing av.LMI and minimizing the number of cells and covered roof area. The illustrative application represents that the method enables designers to adopt a performance-based design approach to simultaneously design both; building, and TSSCs that can be integrated with the building, and searches for the most appropriate solutions i.e., building ensuring good solar potential, and TSSC designs offering high av.LMI, covering less building’s (roof) area, and utilizing less solar cells. Hence, the method serves as a foundation for future employing generative design approaches for building-integrated TSSCs in the early design stages to support informed decision making processes with a focus on solar integration, and energy planning and transition for self-sufficient and sustainable buildings.

Typological characterisation of vintage unreinforced masonry buildings of Queensland, Australia

Characterisation of buildings is critical for the rapid assessment and seismic loss estimation of buildings after an earthquake event. This paper presents a literature review on the characterisation of unreinforced masonry (URM) buildings followed by a typological study of the building stock in Queensland, Australia. The literature review showed that characterisation studies are aimed at cataloguing and developing inventories of buildings for the purpose of seismic vulnerability and risk assessment, and behaviour-influencing parameters are often used as a basis for building classification. Guided by the literature review, a field study was conducted to document important features of vintage (pre-1940) URM buildings that can influence their behaviour during an earthquake. The surveyed aspects included the construction year, number of storeys, roof type, irregularities in plan, isolated or inter-connected buildings, overall dimensions of the buildings, size and shape of windows, façade opening ratio, presence of chimneys, and the style of parapets. Importantly, it was found that certain parapet typologies are prevalent, but that their seismic behaviour is currently unknown. A few such typologies were recommended for future seismic assessment studies. This study can serve as a basis for conducting seismic assessment risk and vulnerability studies in the future.

Effect of Wind Loads on Low-Rise Pitched and Circular Arch Roof Structures: A Comparative Study based on Numerical Simulation

Through numerical simulations based on Computational Fluid Dynamics (CFD), this study examines the effect of wind load operating on a pitched roof and a circular arch roof of an identical plan area of a storage structure with one wall opening. Many numerical simulations have been performed by other researchers for other types of roofs to understand the behavior of wind flow associated with them. Also, the wind standards of multiple countries have provided the Coefficients of Pressures for standard building shapes. However, there is lack of similar work for structures having one wall opening. Ansys CFX fluid flow software has been used to carry out the simulations using a standard k-ε turbulence model. The simulations have been carried out for 7 wind incidence angles at an interval of 30ᵒ. Pressure, Force, Moment, Drag and Lift Coefficients generated on the roofs were compared. It was found that, although variation in these coefficients w.r.t the wind incidence angles are similar for both the roofs types, the sharp-edged rooftop of the pitched roof contributes to higher magnitudes of these coefficients as compared to the smooth arch roof. Mathematical modules between the face average of pressure coefficients and the wind incidence angles were established in a polynomial form which could be used to find the face average of pressure coefficients and corresponding pressures and forces for arbitrary wind incidence angles.

REVIEW: POTENSI MANFAAT APLIKASI GREEN ROOF

ABSTRACTRecently, The construction industry is growing rapidly in the world, including in Indonesia. The construction of high-rise buildings, industry, and housing certainly has an impact on the environment, one of which is the lack of rain catchment areas. The green building concept is expected to reduce energy consumption significantly through several methods of passive design and active design without sacrificing comfort and productivity due to energy savings. One solution to support the concept of green building is the application of green roofs in buildings. Green roof is a building construction where there is a planting medium and vegetation on the roof. The most important green roof capability is the ability to withstand and hold rainwater, especially in urban areas and reduce energy consumption. This study provides an overview of the differences between extensive and intensive types of green roofs, the potential benefits of green roof applications on water quantity and quality, environmental impacts, opportunities and challenges of green roof applications, and economic benefits. Based on this positive aspect, it can be concluded that green roof is one of solution to reduce the environmental impact of buildings, save money, and save water reserves.

Numerical analysis of the effect of roof types and porch on particle dispersion and deposition around a low-rise building

Outdoor air quality demonstrates one of the major challenges for preserving human health and the environment and also involves the indoor environment of buildings. Accordingly, besides urban morphology, building form should be considered as a passive strategy to reduce regional particle deposition and dispersion. In this framework, this study numerically pursues the influence of building shape on improving outdoor air quality around buildings. For this pursuit, eight cases of low-rise buildings with different roof types and a porch at different heights are compared with a simple building form with a flat roof to examine their impact on airflow and particle dispersion and deposition around low-rise buildings. The RNGk−ε turbulence model, in line with the discrete particle model (DPM) is implemented to model particulate flow and investigate particle dispersion and deposition. The discrete random walk (DRW) model is used to simulate the turbulent particle concentration. The results confirm that the combination of shed roof and porch can reduce the amount of particle deposition on the downstream wall of the building by about 20% less than the building with a flat roof. Moreover, using a porch at different heights integrated with a flat roof accumulates more particles on the downstream wall. Evidently, the vortex size influences particle concentration around buildings depending on the building shape. These findings will be helpful for designers regarding the impact of porch and roof types to improve outdoor air quality around buildings. Additionally, these promotions can be employed for decreasing particle deposition on buildings’ walls.

A wildfire vulnerability index for buildings

Recent wildfire events (e.g. Mediterranean region, USA, and Australia) showed that this hazard poses a serious threat for wildland–urban interface (WUI) areas around the globe. Furthermore, recent events in regions where wildfire does not constitute a frequent hazard (e.g. Siberia, Scandinavia) indicated that the spatial pattern of wildfire risk might have significantly changed. To prepare for upcoming extreme events, it is critical for decision-makers to have a thorough understanding of the vulnerability of the built environment to wildfire. Building quality and design standards are important not only because building loss is costly but also because robust buildings may offer shelter when evacuation is not possible. However, studies aiming at the analysis of wildfire vulnerability for the built environment are limited. This paper presents an innovative solution for the vulnerability assessment to wildfires, making use of an all-relevant feature selection algorithm established on statistical relationships to develop a physical vulnerability index for buildings subject to wildfire. Data from a recent and systematically documented wildfire event in Greece (Mati, 2018) are used to select and weight the relevant indicators using a permutation-based automated feature selection based on random forests. Building characteristics including the structural type, the roof type, material and shape, the inclination of the ground, the surrounding vegetation, the material of the shutters and the ground covering were selected and formed into the index. The index may be used in other places in Europe and beyond, especially where no empirical data are available supporting decision-making and risk reduction of an emerging hazard amplified by climate change.

Building vulnerability assessment in seismic areas using ensemble learning: A Nepal case study

The vulnerability assessment is a crucial method for earthquake-prone areas to manage seismic risk. This paper develops an Extreme gradient boosting (XGBoost)-based model to assess the seismic vulnerability of buildings in Kavrepalanchok, Nepal. The vulnerability assessment model is trained based on the building survey data of the 2015 Gorkha earthquake. To rectify the impacts of class imbalance in raw data, the Synthetic Minority Oversampling Technique (SMOTE) technique is adopted in the data preprocessing stage. Then, XGBoost models for the 3 selected target variables are developed respectively. Finally, the performances of the developed models are evaluated, and the significant features that influence the vulnerability are investigated using the SHapley Additive exPlanations (SHAP) technique. Important findings are: (1) The developed SMOTE-XGBoost model for the overall building damage achieves the highest macro f1 score of 0.729 compared with other machine learning models. (2) The SMOTE technique is favorable when the label requiring more attention is the minor group (e.g., severe damage). (3) The foundation type, the roof type, and the building age are identified as the most significant features for the overall building damage grade. The developed learning model can be used as a decision tool to automate building vulnerability assessment in seismic areas using pre-earthquake data, providing decision support for disaster risk reduction at an early stage.

Thermal performance and energy consumption analysis of eight types of extensive green roofs in subtropical monsoon climate

The green roof is important for sustainable urban development. There is relatively lesser research on the thermal performance of green roofs considering the characteristics of the subtropical climate and differentiated structural design. In this study, the effects of different structural configurations of extensive green roof on its thermal performance under a subtropical monsoon climate condition were explored. The effect of different green roof components (vegetation type, aquifer, soil layer thickness) on energy consumption was investigated. The results demonstrated that (1) the order of cooling ability of three plants was Ophiopogon japonicus > Ajuga ciliata Bunge > Sedum lineare Thunb. (2) Aquifers improved the water retention capacity of green roofs, and higher water content lowered the thermal gradient in the matrix. (3) Among the meteorological factors, the correlation between air temperature and the temperature of each structural layer of the green roof was the greatest, and the correlation coefficient of each layer was 0.871–0.925. (4) Among the three soil thicknesses tested for extensive green roofs, 200 mm was the most energy efficient, which can save 30.2%–56.4% of energy consumption throughout the year. It is concluded that the use of appropriate green roof structures can effectively reduce the roof temperature and save building energy consumption.

Research on Roof Cutting Pressure Relief of the Gob-Side Entry Retaining With Roadside Backfilling

Based on the application of the retained gob-side entry in a fully mechanized face E2307 headgate, the stress distribution and deformation characteristics of the roadside backfill body for different types of roof cutting were investigated in this study by employing a combination method of theoretical analysis, numerical simulation, and field measurement. The study uses RocLab software to accurately modify the lithology of the simulation area, discuss the position of roof cutting and the pressure relief parameters of roof cutting, and select the optimal solution of the roof-cutting plan. The results show that the advanced pre-cracking effectively blocks the stress transmission in the high-level roof strata; the roadway is in the stress unloading zone, the stress environment of the surrounding rock in the roadway is significantly optimized, and the deformation of the surrounding rock in the roadway is significantly reduced. In the roof-cutting scheme, the deformation of the surrounding rock in the 90° main roof-cutting scheme is reduced by 78% compared with the uncut roof and by 39% compared with the 90° immediate roof cutting. The research results were successfully applied at the E2307 headgate of the Gaohe coal mine; the maximum roof subsidence and the maximum rib convergence were 121 and 74 mm, respectively. Field monitoring indicates that the deformation of the retained entry was controllable, and the final retaining effects were good.

Extracting planar roof structures from very high resolution images using graph neural networks

Roof structure information is essential for creating detailed 3D building models. These serve in many applications that require knowledge of the roof type and geometry. Automated extraction of the roof structure from remotely sensed images is a challenge because of scene complexity and the large variety of roof top configurations. This paper introduces a fast and parsimonious parsing method to extract vectorized building rooflines and structures from very high resolution remote sensing imagery in an end-to-end trainable way. Our Roof Structure Graph Neural Network (RSGNN) method consists of two components: 1) a Multi-task Learning Module (MLM) designed for geometric primitives extraction and matching, 2) a Graph Neural Network (GNN) based Relation Reasoning Module (RRM) to reconstruct the roofline structure. We tested RSGNN against state-of-the-art computer vision methods on two data sets: the vectorizing world building data set and a custom data set acquired in Enschede, The Netherlands. It provides an increase of 0.6/1.3 and 1.2/2.1 for msAP and FH on two data sets using only half of the training time. In addition, we performed a systematic ablation study to further validate its robustness. We conclude that RSGNN automatically generates the planar roof structure that outperforms competing models in both qualitative and quantitative evaluations.

A bi-level charging management approach for electric truck charging stationconsidering power losses

This article presents an optimized heavy duty electric truck charging station (ETCS) design based on bi-level mixed integer linear programming. Electric truck parameters are integrated with the grid model and charging sequences are firstly formulated to optimize charging stages. As the second level of the optimization stage, line losses are aimed to be minimized for the charging station. ETCS model is obtained from actual parameters of the Istanbul Muratbey Customs zone which is one of the busiest customs zone in Europe and an ideal location for ETCS application in the future. The ETCS is equipped with roof type photovoltaic (PV) modules and the capacity of the PV generation is determined in accordance with the actual data of the customs zone buildings. The PV generated power is integrated with electric truck demand which varies in daytime based on hourly intervals along with the seasonal impact. Ultimately, an optimized ETCS model is presented including PV generated power to reduce line losses and CO2 emission along with optimized charging sequences for ETCS with huge power consumption. The proposed model represents the reduction of line losses along with the obtained environmental and economic benefits.

An Investigation to Identify the Effectiveness of Socioeconomic, Demographic, and Buildings’ Characteristics on Surface Urban Heat Island Patterns

Despite implementing adaptation strategies and measures to make cities sustainable and resilient, the urban heat island (UHI) has been increasing risks to human health and the urban environment by causing hot spots in city areas. This study investigates the spatial patterns in the surface urban heat island (SUHI) over the study site and develops its relationships to socioeconomic, demographic, and buildings’ characteristics. This paper examines the role of building roof types, building roof material, building height, building age, and socioeconomic and demographic factors in driving the SUHI in a city. Numerous studies have focused primarily on the influence of biophysical and meteorological factors on variations in land surface temperatures (LSTs); however, very little attention has been paid to examining the influence of socioeconomic, demographic, and building factors on SUHIs within a city. The analysis has been carried out by processing Landsat based LST data to UHI in the Google Earth Engine (GEE) cloud-based platform. The satellite-based research is further integrated with GIS data acquired from the state government and local city council. Linear regression and multiple regression correlations are further run to examine selected factors’ variance on SUHI. Results indicate socioeconomic, demographic, and building factors contribute significantly to SUHI generation; these factors collectively can explain 28% of the variance in SUHI patterns with significant p-values.

Numerical Simulation of the Temperature Distribution and Evolution Law of Underground Lignite Gasification.

To study the temperature distribution characteristics and evolution law of underground lignite gasifiers, a three-dimensional heat conduction model of underground lignite gasification was constructed. Moreover, the effects of different coal thicknesses, advance speeds of the flame working face, and surrounding rock types on the gasifier were analyzed. The results show that with the increase in the coal thickness, the transfer range and distance of temperature in the roof, floor, and coal seam gradually increase, as does the coal quantity in the three zones. The heat loss rate of the gasifier decreased gradually with the coal seam thickness. When the advance speed of the flame working face is 0.5 m/d, the ideal gasification coal thickness range of lignite is 2.5–17.5 m. With the increase in the gasification rate, the maximum transfer distance of temperature to the roof and floor, the average temperature of the gasifier, and the coal quantity of the three zones gradually increase. Conversely, the coal thickness corresponding to the intersection of the coal quantity of the oxidation and reduction zones and the heat loss rate of the gasifier gradually decrease. When the coal seam below 2.5 m is gasified, the gasification rate can be increased appropriately. When the coal seam is above 13 m, increasing the gasification rate will make the coal quantity in the oxidation zone close to or even higher than that in the reduction zone. Regarding the surrounding rock types comprising a combination of siltstone, mudstone, sandy mudstone, and fine sandstone, the most favorable roof and floor type for underground coal gasification is the combination of fine sandstone and sandy mudstone (without considering the sealing and mechanical properties). These results provide important theoretical support for the industrialization of underground coal gasification.

Efficient Plant Types and Coverage Rates for Optimal Green Roof to Reduce Urban Heat Island Effect

Green roofs are implemented to reduce the urban heat island effect; however, studies are limited to comparing the reduction in urban heat island effect before and after implementation, and the focus is on the structural stability of the building rather than urban heat island reduction. In this study, using the sky view factor (SVF) in ENVI-met, a 3D microclimate modeling program, urban spaces were classified as closed, semi-open, and open areas. Meanwhile, the green roof types were subdivided according to the vegetation coverage rates, which included grass, shrubs, and trees. The vegetation ratio was evaluated using ENVI-met to determine which of the 10 scenarios was most effective for each urban space. The thermal environment was most comfortable in semi-open areas. Therefore, the green roof scenario with 70% grass and 30% trees was effective in closed areas, 50% shrubs and 50% trees were best in semi-open areas, and 70% grass with 30% trees, or 30% grass and 70% trees, was best in open areas. This study provides a basis for creating green roof guidelines aimed at improving the urban thermal environment, as well as creating other green infrastructure elements in cities.

Plant Selection for Living Roofs in Auckland, New Zealand

An extensive living roof is constructed with load bearing structures with a deep layer of soil media that supports a range of plants. Living roofs have environmental benefits, particularly in an urban setting, such as slowing water runoff and promoting evapotranspiration. Auckland Botanic Gardens (ABG) has been trialling different native and exotic plant species on living roofs to identify top performing plants for these unique growing conditions. The living roofs at ABG were constructed and planted in 2010, so after 10 years of observations, we are able to provide plant lists of what has worked well, challenges with the type of roofs we have, and the adventive species observed.

An Experimental and Numerical Approach to Multifunctional Urban Surfaces through Blue Roofs

Uncontrolled urban growth causes a number of problems associated with land use, stormwater management and energy generation. Sustainable Urban Drainage Systems (SUDS) are positioned as an alternative to traditional constructive solutions, contributing towards the generation of multifunctional urban spaces for efficient stormwater management and energy consumption reduction. Nevertheless, this combined goal calls for a deeper understanding of the heat transfer processes that govern the temperature performance in SUDS in order to be further validated as infrastructure to house renewable energy elements. This study intends to determine the thermal properties of two types of blue roofs under extreme conditions of performance (wet and dry), depicting the operation features of their layers and comparing their performances based on the materials used. With this aim, a hybrid experimental methodology, combining laboratory and numerical modelling, was designed using standardized equipment (ISO 8990:1994 and ASTM C1363-05), improving previous methods proposed in the study of the thermal properties of SUDS. The section with expanded clay improved the hydraulic capacity by 4.8%. The section without expanded clay increased its thermal transmittance value by 64.9% under wet conditions. It was also found that the presence of water increased the equivalent thermal conductivity in both sections by 60%.

Plant Growth on a Mediterranean Green Roof: A Pilot Study on Influence of Substrate Depth, Substrate Composition, and Type of Green Roof

Green roofs have been proposed as a significant method of “renaturing” the city, mitigating the urban heat island effect and controlling storm water movement. Plant growth on green roofs affects the environmental performance of the roof. Thus, it is important to examine which parameters influence this growth. Green roofs in the Mediterranean region, due to the climatic specificities of the area, have been a challenge. What types of plants are appropriate, how green roofs should be constructed and other related topics have concerned scientists in the recent years. The aim of this pilot study was to explore the growth of five plants—Sedum sediforme, Drosanthemum floribundum and Lampranthus spectabilis, Medicago arborea, and Lavandula angustifolia—on a pilot Mediterranean green roof in relation to substrate depth, organic content of substrate and type of green roof (open and modular). Data were analyzed using multiple regression with Analysis of Variance. The results indicated that substrate depth, type of green roof and substrate organic content are not significantly important parameters affecting the growth of plants and cannot predict plant performance by themselves. A main insight arising from this study is that plant communities and interspecies relations should be examined more closely in future green roof research as they may affect the impact of selected parameters on plant growth on Mediterranean green roofs. Furthermore, although the type of the green roof—open or modular—could not be directly associated with plant performance, it may be worth examining its impact on plant community performance in future studies.