Building Geometry Research Articles

A predictive large-eddy simulation framework for the analysis of wind loads on a realistic low-rise building geometry

The accuracy of large-eddy simulations (LESs) for predicting wind-induced pressure loads remains an important topic of inquiry. This paper aims to advance this topic by validating an LES workflow for predicting wind pressures on a realistic low-rise building model exposed to a suburban neutral surface layer. We compare two wind tunnel data sets and LES predictions, obtained using a two-step workflow. First, we ensure that an accurate representation of the surface layer wind flow is obtained at the building location. Next, we assess the resulting wind loads on the building model. Using this workflow, we demonstrate consistent agreement between LES predictions and wind tunnel tests, where the discrepancies between the LES and wind tunnel results mimic the discrepancies between the two wind tunnel tests. This finding underscores that the pressure signals in certain locations are sensitive to inevitable, small differences in the approach flow. LES-based flow visualization uncovered that the most negative pressure peaks, which occur on the building roof, arise from hairpin-like vortices that are lifted from the separation region near the upstream roof edge. The results shed light on the complex dynamics of wind-induced pressure loads and contribute to quantifying the reliability of LES for wind load estimation.

Big Data Generation and Comparative Analysis of Machine Learning Models in Predicting the Fundamental Period of Steel Structures Considering Soil–Structure Interaction

The computing of the fundamental period of structures during seismic design is well documented in design codes but is mainly dependent on the height of the structure, which is considered to be the most influential parameter. It is, however, important to consider a phenomenon called the soil–structure interaction (SSI), as this has been found to have a detrimental effect, especially for buildings founded on soft soils. A pilot research project foresaw the use of machine learning (ML) algorithms trained on relatively limited datasets for the development of a more accurate and objective fundamental period formula. Therefore, a dataset that consists of 98,308 fundamental period data points was created through the use of a High-Performance Computer (HPC), which is the largest dataset of its kind. The HPC results were then used to train, test, and validate different ML algorithms. It was found that XGBoost-HYT-CV with hyperparameter tuning performed the best with a correlation of 99.99% and a mean average percentage error (MAPE) of 0.5%. Furthermore, the XGBoost-HYT-CV model outperformed all under-study ML models when using an additional dataset that consisted of out-of-sample building geometries and soil properties, with a resulting MAPE of 9%. Finally, irregular buildings were also used to test the performance of the proposed predictive models.

Street trees: The contribution of latent heat flux to cooling dense urban areas

Trees are the most important natural factor to alleviate the urban heat island effect and the latent heat flux (LE) they release contributes significantly to urban cooling. In this study, the model ENVI-met was used to study the influence of building geometry on the LE exchanged by trees at the block scale in compact urban areas. The building density (BD), building height (BH) and sky view factor (SVF) were used to characterize building geometry. The sensitivity of LE to building geometry was estimated by multi-linear regression analysis. The following conclusions were drawn:(1) the LE of trees is sensitive to building geometry, higher during daytime than night-time in winter and the higher during night-time than daytime in summer; (2) LE changes as expected across the seasons, with LE larger in summer; (3) The shade affects the LE of a tree by influencing the solar irradiance; (4) In winter the average LE of a single tree is larger with 0.06 than 0.12 fractional vegetation cover (FVC). This study can provide useful leads towards further research to explore latent heat exchanges by trees at the street scale.

Modelling of Indoor Air Quality and Thermal Comfort in Passive Buildings Subjected to External Warm Climate Conditions

Air renewal rate is an important parameter for both indoor air quality and thermal comfort. However, to improve indoor thermal comfort, the air renewal rate to be used, in general, will depend on the outdoor air temperature values. This article presents the modelling of indoor air quality and thermal comfort for occupants of a passive building subject to a climate with warm conditions. The ventilation and shading strategies implemented for the interior spaces are then considered, as well as the use of an underground space for storing cooled air. The indoor air quality is evaluated using the carbon dioxide concentration, and thermal comfort is evaluated using the Predicted Mean Vote index. The geometry of the passive building, with complex topology, is generated using a numerical model. The simulation is performed by Building Thermal Response software, considering the building’s geometry and materials, ventilation, and occupancy, among others. The building studied is a circular auditorium. The auditorium is divided into four semi-circular auditoriums and a central circular space, with vertical glazed windows and horizontal shading devices on its entire outer surface. Typical summer conditions existing in a Mediterranean-type environment were considered. In this work, two cases were simulated: in Case 1, the occupation is verified in the central space and the four semi-circular auditoriums and all spaces are considered as one; in Case 2, the occupation is verified only in each semi-circular auditorium and each one works independently. For both cases, three strategies were applied: A, without shading and geothermal devices; B, with a geothermal device and without a shading device; and C, with both shading and geothermal devices. The airflow rate contributes to improving indoor air quality throughout the day and thermal comfort for occupants, especially in the morning. The geothermal and shading devices improve the thermal comfort level, mainly in the afternoon.

Investigating the efficacy of a fast urban climate model for spatial planning of green and blue spaces for heat mitigation

Problems caused by urban heat have prompted the exploration of urban greenery and blue spaces for heat mitigation. Various numerical models can simulate heat-related processes, but their use as support-tools to urban planners remains underexplored, particularly at the city-scale, due to high computational demand and complexity of such models. This study investigates the spatial relationships between urban heat, urban form and urban green and blue spaces with the fast climate model TARGET (The Air-temperature Response to Green/blue-infrastructure Evaluation Tool), which only requires minimal inputs of standard meteorological data, land cover and building geometry data. Using the City of Zurich as our case study, we: (i) validated the TARGET model against air temperature measurements from private sensor networks, (ii) performed a sensitivity analysis to identify key variables affecting urban heat, and (iii) investigated urban heat relationships with blue-green cover at locations frequented by pedestrians. Presence of urban green and blue spaces across the region shows potential for reducing local air temperatures by up to 5.2 °C (with urban forest). Investigating this relationship at different locations in the city revealed key districts that should potentially be targeted for reduction of pedestrian heat-impacts, due to their high pedestrian traffic, fewer green and blue spaces and high daytime air temperatures. Our results not only provide insights into the cooling effect of different amounts of green and blue features in the urban environment, but also demonstrates the application and integration potential of simplified models like TARGET to support the planning of more liveable future cities.

Wind tunnel measurements of cross-ventilation flow in a realistic building geometry: Influence of building partitions and wind direction

Wind tunnel measurements have widely been used for validation of computational fluid dynamics simulations of natural ventilation airflows. However, the majority of such measurements employed simple generic single-zone buildings, while there is a lack of studies on realistic buildings including flow-critical geometrical features (e.g. internal partitions). To assess the effect of internal partitions at different incident flow angles (α = 0° and α = 30°), wind tunnel measurements of velocities in and around a cross-ventilated realistic residential building (with and without internal partition) were performed. Measurements were conducted at a geometric scale 1:40, using laser Doppler anemometry. Results indicate a large impact of the internal partition on indoor airflow distribution and resulting ventilation flow rates. For instance, for α = 0°, on the partitioned building side, regions of velocity increase (from ∼0 m/s to ∼80% of the outdoor reference velocity, Uref), but also regions of velocity decrease (from ∼50% of Uref to ∼0 m/s) were observed. The ventilation flow rate through the windows at the partitioned side decreased by 23% and 32%, respectively. For the partitioned building, a change from α = 0° to α = 30° resulted in regions of velocity increase from 0 m/s to ∼60% of Uref.

A hybrid load prediction method of office buildings based on physical simulation database and LightGBM algorithm

Building load prediction plays an important role in building energy savings and mechanical and electrical system optimization control. The dynamic energy consumption can be accurately calculated using the traditional physical energy simulation method that entails a complex setup and verification process due to the numerous input parameters required. It is also difficult to change the physical model once it has been determined. The data-mining method is fast in calculation and simple to use, but its prediction accuracy is limited by historical data quality. It is difficult to predict the load of new buildings without historical data. To solve these problems, this study proposes a hybrid building load prediction method for office buildings. The proposed method uses EnergyPlus to generate a building load database that includes than 25.14 million data cases, covering 35 types of building geometry and 2870 building examples. Based on the above database, the LightGBM algorithm was selected to extract feature variables that affect the load and build a load prediction model. The training results show that there are 24 key feature variables for office building load prediction. The hourly MAPE of the cooling load prediction model is 6.95 % and RMSE is 4.31 W/m2, and the hourly MAPE of the heating load prediction model is 7.09 % and RMSE is 11.64 W/m2 compared with EnergyPlus model. Two actual office buildings are selected as case studies to validate the model prediction accuracy. Results show that comparing predicted results with measured data, the hourly cooling load of the MAPE is 12.42 %. Coparing predicted results with actual heating load, daily MAPE is 7.97 %.

Development of a georeferenced atlas of energy renovation in residential buildings using GIS tool

Abstract This paper presents a method for modelling the energy needs for heating the residential building stock on a macro scale. The method is based on the use of Geographic Information Systems (GIS) tools to manage various existing geolocalised databases and the collection of field data. It combines the calculation of energy demand with the estimation of solar gains received by the building envelope. In order to estimate the heat energy balance, a description of the building geometry is necessary. The geometrical characteristics of the envelope are extracted from the building entities information available in the spatial database. These parameters are then enriched and cross-referenced with other data gathered from the government’s complex taxation files. The TABULA buildings typology was chosen because it is an exhaustive European classification adapted for use in GIS utilities. The objective of this work is to produce a georeferenced atlas that can identify potential energy savings on large zones, such as urban areas. An understanding of energy consumption at this scale is crucial for the effective management of massively scaled energy-saving solutions. The atlas will promote energy efficiency in the residential sector and guide environmental policy measures by identifying priority areas for renovation.

Real-time monitoring of the spatiotemporal thermal state of fused filament fabrication process for shape memory polymers

This study investigates a multi-sensor approach to estimate the surface thermal fields of Fused Filament Fabrication (FFF), an additive manufacturing process, at high spatial (4 μm) and temporal (0.1 msec) resolutions. The current state-of-the-art methods for quantifying the thermal, mechanical, and chemical state of the FFF processes are categorized into two main groups: numerical simulations and in-process thermal monitoring. Although numerical simulations can precisely estimate the process state for simple components, they are computationally demanding, become less accurate when complexity increases, and are therefore not suitable for real-time monitoring. In-process thermal monitoring, on the other hand, is becoming vital to track the process’ state, and offers a means to discern the underlying phenomena. While the current thermal sensors offer sub-mm scale spatial resolutions, their temporal resolutions (at best 1 msec) do not allow tracking of the thermal gradients necessary to determine the build geometry, microstructure, and the properties of the components. They are also limited by the cost and size, and need custom fixtures for mounting to a printer. The study proposes an approach based on pairing a primary high-tech thermal sensor with a secondary small footprint vibration sensor using a machine learning model to reconstruct the thermal fields at high time-resolutions from vibration signals. The key steps of this novel ML pipeline consisted of a singular value decomposition (SVD) of the ground truth of the thermal images and a time–frequency transformation with fast Fourier of the vibration signal. The overall model was able to make inferences and predict with 91.36 % accuracy the thermal state of the process using only vibration data as input. In addition, the ML pipeline filtered part of the experimental error contained in the ground truth data.

3D LoD2 modelling of Halong City based on UAV point cloud

3D urban building models are crucial for linking, converging, and integrating economic and social urban data. They are extensively utilized in numerous domains such as smart city development, comprehensive social management, and emergency decision-making. Advanced technologies like affordable UAV (Unmanned Aerial Systems) imagery enable a greater level of automation in data acquisition compared to traditional digital photogrammetry methods. The main objective of this research is to build a 3D LoD2 CityGML model with dense point clouds from UAV images. This study presents a completed workflow for generating 3D CityGML models of the city at LoD2 using UAV data. The use of dense point cloud data from UAV technology in the experimental area has been performed using a DJI Phantom 4 Pro. The original point clouds should be denoised using the statistical outlier remover (SOR), the main goal is to reduce noise while preserving the building's geometry. After that, the point clouds of object features were vectored for the level of detail 2 (LoD2) of the object's 3D volume corresponding to its actual height, and objects belonging to the Feature Class 3D layer will represent LoD2 on SketchUp Pro 2021 software to generate a highly accurate 3D model. The evaluation results show that the square errors calculated from the test points for the three axes X, Y, Z are 1.4 cm, 1.6 cm, 1.7 cm, respectively. Conducting research to choose the UAV photography method aims to offer an efficient and cost-effective solution, saving time and human resources, to address the 3D mapping challenges in urban areas across Vietnam.

Designing Climate-Adaptive Buildings: Impact of Courtyard Geometry on Microclimates in Hot, Dry Environments

Designing climate-adaptive buildings is crucial for mitigating the adverse effects of climate change by enhancing energy efficiency and reducing greenhouse gas emissions. Additionally, such designs improve thermal comfort and resilience in urban environments, particularly in regions with extreme climates, thereby promoting sustainable living conditions. This study aims to mitigate climate change through strategic urban and building design, focusing on the impact of building geometry and courtyard configurations on enhancing microclimates and thermal comfort in the UAE's hot arid climate. Utilizing ENVI-met software for qualitative analysis, the research examines design modifications in a school building's layout and courtyards. The analysis and findings reveal that strategic alterations can reduce outdoor air temperatures by up to 1.45°C and average building temperatures by approximately 1.89°C. Additionally, these modifications significantly improve thermal comfort perceptions on the PMV scale. The findings underscore the potential of architectural design to contribute to climate change mitigation efforts, highlighting the importance of thoughtful building and courtyard designs in promoting sustainable architecture and urban planning. This study offers novel insights into the role of design in enhancing thermal environments, providing a practical approach for developing climate-adaptive buildings in hot, dry environments. Doi: 10.28991/CEJ-2024-010-08-017 Full Text: PDF

Leveraging machine learning to generate a unified and complete building height dataset for Germany

Building geometry data is crucial for detailed, spatially-explicit analyses of the building stock in energy systems analysis and beyond. Despite the existence of diverse datasets and methods, a standardized and validated approach for creating a nation-wide unified and complete dataset of German building heights is not yet available. This study develops and validates such a methodology, combining different data sources for building footprints and heights and filling gaps in height data using an XGBoost machine learning algorithm. The XGBoost model achieves a mean absolute error of 1.78 m at the national level and between 1.52 m and 3.47 m at the federal state level. The goal is proving the applicability of the methodology at a large scale and creating a useful dataset. The resulting dataset is thoroughly evaluated on a building-by-building level and spatially resolved statistics on the quality of the dataset are reported. This detailed validation found that the building number and footprint area of German building stock is 90.31 % and 94.84 % correct, respectively, and the building height accuracy is 0.59 m at the national level. However, errors are not homogeneous across Germany and further research is needed into the impact of including additional datasets, especially for regions and building types with lower accuracies. This study proves that the chosen methodology is useful for generating a building height dataset and the workflow, with some modifications for regional data availability, can be transferred to other countries. The generated building dataset for Germany constitutes a valuable data basis for the research community in fields such as energy research, urban planning and building decarbonization policy development.

Using deep learning for enrichment of heritage BIM: Al Radwan house in historic Jeddah as a case study

Building information modeling (BIM) can greatly improve the management and planning of historic building conservation projects. However, implementing BIM in the heritage has many challenges, including issues with modeling irregular features, surveying data occlusions, and a lack of predefined libraries of parametric objects. Indeed, surface features can be manually distinguished and segmented depending on the level of human involvement during data scanning and BIM processing. This requires a significant amount of time and resources, as well as the risk of making too subjective decisions. To address these bottlenecks and improve BIM digitization of building geometry, a novel deep learning based scan-to-HBIM workflow is used during the recording of the historic building in historic Jeddah, Saudi Arabia, a UNESCO World Heritage site. The proposed workflow enables access to laser scanner and unmanned aerial vehicle imagery data to create a complete integrated survey using high-resolution imagery acquired independently at the best position and time for proper radiometric information to depict the surface features. By employing deep learning with orthophotos, the method significantly improves the interpretation of spatial weathering forms and façade degradation. Additionally, an HBIM library for Saudi Hijazi architectural elements is created, and the vector data derived from deep learning-based segmentation are accurately mapped onto the HBIM geometry with relevant statistical parameters. The findings give stakeholders an effective tool for identifying the types, nature, and spatial extent of façade degradation to investigate and monitor the structure.

Reconciling retrofitting with IEQ to maintain energy, environmental and economic performance: A methodological approach for generic office buildings

Office building retrofitting may instigate significant reductions in energy consumption and there is growing recognition that buildings’ energy, economic and environmental performances (the 3Es) must be considered in tandem. This study aims at maintaining the 3Es when retrofitting office buildings towards nZEB standards. A simulation-based methodology was designed consisting of three generic building forms describing common office building geometries constructed between 1952 and 1980 in Egypt; linear, square with courtyard and L-shaped. Each accommodated for open, closed and semi-closed office layouts as recommended in architectural manuals, and was oriented in four directions, giving 36 base cases. A combined retrofitting strategy was applied and both base cases and retrofitted cases were simulated in DesignBuilder using Cairo weather data, which was validated indirectly through real measurements using preliminary simulations that retrieved statistical significance.Results were analyzed for each of the 3Es by comparing base cases to post-retrofitting performances. Results revealed that not all retrofitted cases that demonstrate acceptable environmental performance and/or energy-efficiency are cost-optimal. However, the following patterns were deduced. Square courtyard buildings yield superior performances, and L-shapes tend to perform outside acceptable ranges. Closed plans demonstrate superior energy and environmental performance but poorer economic performance. 17 out of 36 retrofitted cases achieved nZEB, illustrating the potential of implementing nZEB in Egypt. This work may be considered prototypical, encouraging a national office building retrofitting process toward nZEB. However, implications extend beyond the national level, as results may be used to support a kick-off retrofitting programme based on basic office building geometries.

Enhancing LES efficacy in wind load evaluation of low-rise buildings using synthesized inflow turbulence

In Large-Eddy Simulation (LES), the turbulence inflow can be generated synthetically, where the input value of the maximum turbulence frequency can impact the accuracy. LES efficacy can be influenced by the designated computational domain (CD) size and grid specifications. This study proposes multiple parametric studies that aim to correlate the effective parameters controlling the accuracy of numerical wind load evaluation on a low-rise building without compromising the computational cost. The study includes the efficient selection of turbulence maximum frequency (fmax) that can accurately capture the pressure fluctuation induced on the building facade. Both CD size, which involves the distances from the building to the boundary condition, and CD discretization in terms of grid size scheme and shape, are examined. The error assessment is conducted by comparing wind flow aerodynamics and evaluating the RMSE for the pressure parameters with respect to the wind tunnel test. The findings of this study suggest using fmax which can be resolved by the minimum grid size as an input in the inflow turbulence generated to accurately transport the majority of eddies when a satisfactory wavelength of 4 Δx is utilized. This study shows that computational domain size and discretization can be significant sources of error that can reach 21 %, compromising the reliability of computational wind load predictions. The current code in selecting computational minimum width and length is found to be insufficient. In addition, it is found that there are currently no recommendations for selecting the width computational dimension specifications when examining low-rise buildings oriented in an oblique wind direction. The domain grid specification induced a considerable error of 14 % in the mean pressure across the roof when tetrahedral grid shapes were used. Overall, this study's proposed recommendations can significantly impact the efficacy of computational wind load evaluation for low-rise building geometry.

Semantic enrichment of BIM with IndoorGML for quadruped robot navigation and automated 3D scanning

Planning scan routes with prior knowledge can improve scan data quality and completeness. This paper presents a BIM-enabled approach to optimize quadruped robot navigation for automated 3D scanning. The BIM data schema is enriched with IndoorGML, integrating building geometry with spatial data to establish an indoor navigation model describing multi-scale spatial topological networks. This navigation model, which includes an enhanced greedy algorithm, optimizes quadruped robot scanning positions and traversal sequences. The scan planning optimization outperforms existing heuristic algorithms in computational efficiency, coverage, and scan point count. The BIM-enabled approach is validated on ROS and in real-world conditions with a 3D LiDAR sensor integrated with a quadruped robot. The robotic scans achieve visible coverage of 70–90% of the structure, with a fluctuation of 0.006–0.021 mm compared to traditional laser scans. The findings demonstrate robotic scans as a viable way of obtaining complete and accurate point clouds, reducing human effort in traditional scanning.

Informative design exploration of tower building geometry typology and solar potential using SOM-MLPNN with generalized form-description-based parametric modelling

To synchronize designers early in the design process and help them understand the building forms and performance metrics needed to meet both qualitative and quantitative requirements is crucial for sustainable building designs. While the prevalent design exploration technique, which integrates self-organizing mapping with a Supervised Learning-based Agent Model (SOM-MLPNN), offers insights into design geometry and solar potential, it still has limitations in the range of the design exploration space, the efficiency and accuracy of data approximation, and the uncertainty in dealing with performance metrics related to more complex environmental factors. Using the solar potential of tower buildings as an example, this study proposes a method based on SOM-MLPNN by integrating a Generalized Tower Generator (GTG) and form description, testing its usability for the inclusion of complex surrounding environments, and improving its design exploration breadth as well as the efficiency and accuracy of data approximations. The results demonstrate improved efficiency and accuracy in data approximation across an expansive design space, facilitating visual and interactive exploration of design-performance relationships. The proposed method emphasizes the critical interplay between building geometry and environment-related performance, providing designers with a refined toolkit for discerning the correlation between design and performance, thereby enhancing their design decision-making abilities.

Machine learning-driven real-time identification of large-space building fires and forecast of temperature development

In firefighting, real-time knowledge of fire dynamics is crucial yet often unavailable. The building geometry and measured gas temperatures are integrated to ascertain fire states and predict temperature evolution, utilizing a machine learning framework that combines long short-term memory networks and transfer learning. The model is pre-trained on datasets with 1000 samples from parametric fire models and 200 from field simulations, enabling real-time predictions with on-site data. Numerical simulations in portal frame buildings demonstrate over 95% accuracy in identifying fire states and 90% in predicting gas temperatures. A method using correlation coefficients and standard deviations effectively detects damaged thermocouples with over 96% accuracy, given a damage ratio below 30%. Validation in two real fire scenarios showed more than 92% accuracy in locating fires and over 89% in forecasting gas temperatures 20 min ahead, which costs 2.14 s and 1.83 s, respectively. The machine learning framework also exhibits robustness to changes in ventilation conditions by making temperature predictions using reliability theory. The proposed framework provides clear information about the state and development of the fire to firefighters and is useful for promoting smart firefighting.

Refining urban morphology: An explainable machine learning method for estimating footprint-level building height

Fine-grained 3D buildings with diverse morphology are a cornerstone of urban physical structures and have profound implications for sustainable city development. However, accurately estimating building height at the footprint-level is a challenge. This study bridged this gap by using random forest models to integrate the elevation, geometry and shape attributes of individual buildings, further refining those with spatial aggregation. It considered over one million buildings across 10 large Chinese cities and trained two-types models that demonstrated commendable performance in city-specific (the mean absolute error (MAE) ranged from 3.43 m to 5.06 m) and combined (MAE = 4.68 m) models. Results revealed that the current dataset had a finer urban morphology compared with existing datasets and showed outstanding generalisability in method transfer and feature ablation tests. By incorporating Shapley values, we explored the features' global and local impacts. The explainable results demonstrated that building area was the most impactful feature, and the elevation-dimension features were particularly beneficial in estimating high-rise buildings. Using the fine-grained 3D buildings, we explored the connections between explicit morphology differences and implicit contexts in cities. Overall, our work is an endeavour to estimate footprint-level building height as fuel for refining urban morphology and enabling sustainable city studies.

Research on geometry optimization of park office buildings with the goal of zero energy

At the conceptual design stage, the building geometry design can predict the building load demand and renewable energy application potential to some extent. Geometry design interventions are critical to efficiently achieving the goal of zero-energy buildings. In the existing studies, the typicality and representativeness of the object geometry in the practice project are not high, and the influence of different geometry indices on energy savings and photovoltaic (PV) utilization potential under the same type of plane shape is rarely considered. In addition, the surrounding building shading situations considered are single. There is still significant room for improvement in the typical geometries selection and optimization condition settings. This paper presents a study of three typical park office building geometries in hot summer and cold winter regions. An optimization method based on the genetic algorithm and energy consumption simulation is proposed that takes into account both building energy savings and PV utilization potential improvements. The geometry-related indices of each typical geometry are then optimized under different PV layout scenarios and surrounding shading situations, and suitable geometry design strategies for typical office buildings are proposed under each optimization scenario. Geometry optimization can significantly reduce building energy consumption (the building energy-saving rate is 1.3–10.7 %) and increase PV generation (enhancement of 13.37–66.67 % for PV generation potential), thus lowering the net energy consumption of buildings. The findings of this study can be used to guide the design of park office building geometries in hot summer and cold winter regions with the goal of zero energy, as well as to promote the practice of zero-energy office buildings at the regional and national levels.