Building Performance Evaluation Research Articles

Energy retrofit technology for modern and contemporary educational historical buildings considering preservation and utilization aspects

Energy retrofit technology is proposed for achieving continuous and efficient energy utilization in modern educational facilities. To improve the accuracy of diagnosis rather than improving numerical analysis, the building performance evaluation of exterior walls and windows was conducted using on-site measuring equipment. The target building had a substandard domestic performance; therefore, improvement measures were required to use building energy efficiently. We evaluated the energy consumption of the target building through the building energy analysis program DesignBuilder. Energy efficiency and economic benefits of retrofit technologies were evaluated, with seven selected technologies categorized into passive, active, and renewable. Efficient technologies included PV panel, High-efficiency HVAC system, Airtight film, and LED lights. 8 retrofit packages were formulated, considering energy savings, economic viability, and thermal comfort improvements. Among these, the energy-saving package, the package incorporating all technologies, the economically beneficial package, and the conservation package were efficient for energy savings, while the conservation package, the economic benefits, and the passive were economically efficient. energy-saving package was found effective for warm improvement and demonstrated significant improvement in cold thermal comfort conditions. Considering the modern nature of the target building, a comprehensive conservation package was deemed most suitable, offering energy savings, economic benefits, thermal comfort enhancements, and preservation aspects. With a payback period of 4 years and achieving a 41.4% energy saving, it was deemed the optimal choice for the target building.

Classification of architectural and MEP BIM objects for building performance evaluation

Numerous factors in current workflows reduce the completeness, accuracy, and reliability of the information exchanged between Building Information Modelling (BIM) authoring tools and building performance simulation and analysis software. Automated classification of BIM objects could ameliorate most of the interoperability problems. However, architectural, structural, mechanical, electrical, and plumbing (MEP), and other building objects have a wide range of shapes, features, and properties, which makes identifying them reliably and accurately with any single AI tool difficult. This study explored methods and techniques for automated classification of architectural and MEP BIM objects that can significantly reduce or eliminate the manual effort required to correct data exchange and interoperability issues in pre-processing building models for performance evaluation. A series of ensemble models using a stacking mechanism of four classification tools (semantic keyword search, rule-based inferencing, machine learning using object geometry features, and deep learning using visual shape features) were implemented and tested. A BIM object dataset with 3,410 instances belonging to 40 classes was compiled for training and testing the models. The dataset contains diverse architecture and MEP objects with high-quality selected instances and is available for public use. We found that an ensemble learning approach that exploits the various types of information available in instance models may exhibit superior object classification performance than any individual tool. The ensemble model applying all four tools achieved 91.0 % accuracy (F1 score 0.88), suggesting that automated classification using ensemble learning is an effective strategy.

Seismic Performance Evaluation of Multi-Storey Residential Building with Friction Pendulum Bearings: Indonesia case study

The methodology for seismic performance evaluation of a residential building in Indonesia with the use of seismic isolation is considered. An 8-storey reinforced concrete frame residential building with shear wall structural system was selected as a case study. Nonlinear methods of seismic response analysis were used to calculate the response of the structure: nonlinear static (Pushover) and Nonlinear-Time History Analysis, NLTHA. The analysis is performed in STERA 3D freeware. The nonlinear time history analysis was performed for seven pairs of horizontal components of earthquake ground motions, selected according to the parameters of possible earthquakes for the considered site (Bandung city). The selected earthquake records were modified using the spectral matching procedure for design spectrum. Friction-pendulum bearings developed by Nippon Steel Corporation of Japan were used as seismic isolation. The results of nonlinear time history analysis show that shallow earthquakes result in greater damage compared to megathrust earthquakes, with both scenarios providing a life safety (LS) performance level. The use of seismic isolation can reduce seismic loads, as evidenced by the reduction in top-level accelerations and shear forces at the base.

Deterioration-induced asymmetry-insensitive collapse behavior in low-rise torsionally-stiff buildings

This study investigates a significant point in the collapse behavior of asymmetric torsionally-stiff buildings, which is ignored by seismic design codes. The aim is to show how the effect of asymmetry on the collapse capacity depends on the deterioration characteristics of structures. To this end, the collapse performance evaluation of low-rise moment-resisting frame buildings is performed according to the FEMA-P695 methodology, emphasizing the drawbacks of ignoring degrading properties of structures in the collapse assessment. Then, the importance of deterioration is measured by a novel criterion, which is able to resolve the deficiency of prior justifications for the effect of asymmetry on the collapse performance of structures. Accordingly, there is a limit state for the proposed measure above which we may have an asymmetry-insensitive collapse performance for low-rise torsionally-stiff buildings. A two-step verification process is employed to confirm the validity of the results.

Performance evaluation of high-rise buildings using database-assisted design approach

In recent years, performance-based design (PBD) has gained attention and is sought to be the benchmark approach in the field of wind engineering. While the concept of performance-based design is well-accepted in earthquake engineering, it is yet to be embraced for the design of buildings to resist severe wind loads. This paper introduces a framework for the performance-based wind design (PBWD) of tall steel buildings using a time domain analysis that keeps the process of wind effects and the structural design process integrated, transparent, and fully auditable. From the perspective of PBWD, the main objective is to achieve a desirable performance level for a given hazard level, i.e., mean recurrence interval of extreme wind. The wind effects are directly related to the mean annual return of wind through a well-accepted simulation approach. A 180 m tall standard CAARC building is used for the case study to illustrate the proposed methodology. The wind load time histories are determined using the pressure tap data on exterior faces of the building measured in the wind tunnel. With the calculated wind loads, nonlinear dynamic analysis is conducted with various wind directions and mean wind speeds based on database-assisted design (DAD) approach. The key performance measures such as demand-to-capacity indices, inter-story drift, damage deformation index, and floor accelerations are calculated as a function of wind directions and mean wind speed. The obtained responses are used in conjunction with a local wind climatological database to determine the extreme wind effects for any specific mean recurrence interval. The performance of the steel building is evaluated for three performance criteria, including occupant comfort, operational, and continuous occupancy. The conducted performance assessment reveals that building fails to satisfy the serviceability requirement of drifts. However, the building satisfies the requirements for the occupant comfort and operational performance levels for strength design, while it also satisfies the continuous occupancy, limited interruption in Risk category II. The result reveals that the proposed framework provides realistic assessment of performance of the building incorporating the wind directionality and return period of the wind speeds.

Investigating Three-Dimensional RCC Frames under Seismic Loading with Various Soil Conditions

Equivalent lateral forces are used in earthquake engineering to build structures that can survive seismic shocks. Considering seismic waves affect how the Earth moves, buildings in India’s seismically active areas must be built to withstand earthquakes. This study examines how multistorey reinforced concrete building frames function seismically while taking into account different soil types, loading scenarios, and seismic Zones IV. For a twelve-storey skyscraper, the analysis includes earthquake reaction storey displacement. This study looks into how soil changes in seismic zone IV affect buildings’ responses, as well as how seismic zones themselves affect them. The building’s natural time periods were evaluated using both response spectrum analysis and time history analysis methods. These results provide insightful information about the complex interaction between soil type and seismic zone, Member stresses and maximum displacement are calculated using static and dynamic analysis. In India’s seismically active region, the necessity for earthquake-resistant structures is highlighted by seismic waves that alter the motion of the earth. Response spectrum analysis combines modal responses via techniques including SRSS, CQC, and ABS, taking into account a variety of response modes. This study compares earthquake loads using various soil types in Zone IV to evaluate building performance during earthquakes. This project’s primary goal is to analyse a multistorey building’s seismic response. Staad Pro Software does load calculations in order to analyse the entire structure. Staad-Pro analysis employs the Limit State create approach, which complies with the Indian Standard Code of Practice.

Integrating views on building performance from different stakeholder groups

PurposeThis paper aims to highlight a broader definition of good building performance that goes beyond the traditional emphasis on technical aspects, only adopting the lens of the social construction of technology (SCOT) in the empirical space of the workplace sector. Several building performance evaluation (BPE) methods focus on technical aspects such as energy consumption, indoor environmental conditions and compliance with building regulations and standards. Technical aspects, albeit important goals, only embed a partial component of what buildings are expected to deliver. There is growing interest in considering the organisational and experiential expectations of building performance, particularly integrating various views of performance as expected by different user groups.Design/methodology/approachThe paper adopts an archival research method to analyse the archive of the workplace consultants DEGW, particularly focusing on their work on London’s Broadgate development in the 1980s and the 1990s.FindingsThe findings reveal how voices from a pluralistic client organisation can be addressed to articulate a broad definition of building performance that integrates different viewpoints encompassing technical, organisational and experiential expectations. In DEGW’s work, the views on building performance of various stakeholders involved in the everyday use and management of buildings are identified without imposing predetermined agendas or research notions of performance. Particular emphasis is given to understanding clients as not a homogenous entity but consisting of different interest groups, which implies multiple conceptualisations of building performance and the building itself.Research limitations/implicationsThe performance expectations of a building vary between organisations and even within any organisation. Moreover, the needs of an organisation will change over time, and the BPE criteria need to be changed to ensure better alignment between organisations and the physical spaces they occupy. A critical reflection on the conceptualisation of “users” and “building” in BPE methods is required to create an integrated approach towards building performance.Originality/valueThe paper offers insights by adopting the theoretical lens of SCOT to explore an integrated approach to building performance that captures the varied needs of building users through the example of London’s Broadgate development.

To Shade or not to Shade (and how)? Annual heating energy balance and internal temperature in low energy houses in Wrocław, Poland.

One of the important areas of occupant–building fabric interaction with IEQ consequences is windows’ shading. The relevance of shading has been explored through modeling studies in the context of energy, daylighting and thermal comfort, typically aiming to inform the design stage. In owner occupied housing the architectural design sets the scene for subsequent residents’ decisions concerning windows treatment. Here, based on varied treatments for 20m2 living room window observed during an in-depth building performance evaluation in same homes, dynamic modeling in TRNSYS is used to establish the related heating loads and internal temperatures. The results allow to rank five shading usage scenarios for three physical settings, namely lack of shading, external blind and internal curtain on lower half of the window, in terms of their overheating mitigation potential, based on simplified assumptions for heating, ventilation and shading practices. The modelling points towards severe overheating issue and crucial impact of shading usage patterns on its mitigation. Yearly modelled share of overheating hours (>26 °C) are almost double for unshaded scenario compared to the most effective shading strategy (25% and 13%). The simulation contributes to understanding of the field monitoring results where the unshaded living rooms experience more severe overheating, and the two shading settings in place are associated with comparable internal thermal conditions, respectively 22% vs. 5% and 6% of yearly share of overheating hours. The simplification of human-HVAC-building interaction model likely obfuscates solar heat gain control contribution to heating load.

A revised morphing algorithm for creating future weather for building performance evaluation

Climate change is one of the greatest challenges the building industry faces. Engineers and architects require representative future weather data if they would like to see how their buildings and designs will fare under a changing climate. The most common method used to create future weather involves manipulating observations commonly known as morphing, but the most used algorithms can create implausible weather conditions due to their unbounded nature. Here, bounded morphing algorithms will be described and their effectiveness proved mathematically. The improved bounded method applies two additional conditions on the morphed distribution to the maximum and minimum values, in addition to the mean values. The benefits over the standard approach will also be illustrated considering the changes in the distribution of temperature and solar irradiation due to climate change. The improved algorithms outperform the standard morphing procedures in terms of preserving the underlying climate signal while not creating unrealistic or implausible weather conditions. This method should give engineers confidence that the generated future weather series are more robust and representative of potential future weather. Practical application: The use of future weather to inform building design is now commonplace within the industry. Reliable weather files are crucial to support and deliver strategies for decarbonisation and adaptation to climate change in the built environment and the wider industry. This article provides support for the use of revised morphing algorithms which result in improved future weather time series which can be used in building simulation. For example, when applied to the temperature, it can be used to produce more accurate representations of future temperature profiles due to climate change, and for building performance assessment, such as energy consumption and overheating. It plays an important role in producing reliable and realistic weather data for future-proof building design.

Performance evaluation of building integrated photovoltaic system arrays (SP, TT, QT, and TCT) to improve maximum power with low mismatch loss under partial shading

Performance evaluation of building integrated photovoltaic system arrays (SP, TT, QT, and TCT) to improve maximum power with low mismatch loss under partial shading

Evaluating the Environmental Footprint: BPE Framework for Sustainable and Energy-Efficient Residential Buildings in India

In the context of enhancing building performance assessment, this study introduces the BPE (Building Performance Evaluation) framework and explores its application through a residential complex in India. An expert evaluation of the questionnaire is carried out to investigate the main element and obstacles to the execution of BPE. The framework, designed to scrutinize five parameters, initially assesses design aspects, including building form, orientation, and aesthetics. Findings reveal that the building design lacks efficient circulation, storage facilities, and satisfactory spatial allocation. Building energy monitoring, essential for comprehensive analysis, faces limitations due to insufficient data availability, emphasizing the need for thorough planning. Thermal comfort analysis, based on temperature and humidity measurements, unveils significant fluctuations beyond comfort thresholds. Expert surveys and occupant feedback further expose reduced utilization of natural ventilation, high air conditioner adoption rates, and adaptive behaviours. The framework's insights prompt opportunities for improvement, yet validation requires broader application across diverse buildings. The study's academic survey emphasizes the importance of integrating BPE in industries with government policies. Field observations highlight challenges in space utilization, material selection, and occupant engagement. This study's findings underscore the BPE-RBPI framework's potential to refine performance assessment, sustainable and energy efficient to foster industry confidence, and drive holistic improvements in India's building sector.

Building Performance under Untypical Weather Conditions: A 40-Year Study of Hong Kong

As a common engineering practice, the buildings are usually evaluated under the Typical Meteorological Year (TMY), which represents the common weather situation. The warm and cool conditions, however, can affect the building performance considerably, yet building performances under such conditions cannot fully be given by the conventional TMY. This paper gives approaches to constructing the weather data that represents several warm and cool conditions and compares their differences by studying the cumulative cooling demands of a typical building in a hot and humid climate. Apart from the Extreme Weather Year (EWY), the Near-Extreme Weather Year (NEWY) and Common warm/cool Years (CY) data are proposed according to the occurrence distributions of the weather over the long term. It was found that the cooling demands of NEWY and EWY differ by 4.8% from the cooling needs of TMY. The difference between the cooling demands of NEWY and CY for most calendar months can be 20% and 15%, respectively. For the hot months, the cooling demands under NEWY and CY take 7.4–11.6% and 2.3–5.6% differences from those under TMY. The uncertainties of building performance due to the ever-changing weather conditions can be essential to the robustness of building performance evaluations.

Automated monitoring applications for existing buildings through natural language processing based semantic mapping of operational data and creation of digital twins

Buildings' operation constitutes 36% of the German energy consumption. Currently, operators lack the knowledge on energy-saving techniques. There is a shortage of cost-effective and easily-implementable solutions to evaluate building performance. The cause of this problem lies with the semantically heterogeneous operational data used in technical applications. Integrating the data into monitoring applications demands substantial and costly manual efforts. This paper presents a method that enables automated generation of technical monitoring applications for existing buildings. The method outlined represents existing automation stations as digital twins and employs artificial intelligence to map the heterogeneous data to a standard and create semantic digital twins of buildings. The paper introduces a method using natural language processing for the semantic processing of data. The developed method involves a four-stage process for classification of data points, which are subsequently mapped to a uniform vocabulary. To classify the data points, language models were trained on a created dataset of 54,125 data points. Following successful training, the models can process semantically heterogeneous data points. The results, demonstrating an average F1-Score of over 95%, indicate that the developed method is suitable for automating data point mapping. The models were implemented as an Industry 4.0 service and integrated into an application.

Explainable semi-supervised AI for green performance evaluation of airport buildings

Despite the global sustainability trend, airport buildings have received limited attention regarding their environmental impact within the built environment and aviation sectors. With less than 1% of green building certifications worldwide, there is a lack of essential green performance assessment tools, including frameworks, primary datasets, and models that require less human supervision. Previous research has addressed these issues by proposing frameworks, primary datasets, and a supervised method that leverages Classification and Regression Trees. However, these methods still rely on human effort to fix credit scores and assign category weights, necessitating significant supervision from human experts. This study presents a green performance evaluation model for airport buildings using an explainable semi-supervised AI approach to reduce human dependency during the inference of green scores. Principal Component Analysis and Hierarchical Clustering algorithms (agglomerative and divisive) are employed to construct the models. The dataset's dimensionality is reduced based on metrics such as scatter coefficient, Psi Index, variation, and permutation. The cluster numbers are determined using elbow point and silhouette coefficient values, resulting in cluster initializations between 6 and 12. Performance measures, including the rand index, mean absolute error, precision, recall, F1-measure, and accuracy, are evaluated for the models. The AHC and DHC models exhibit a maximum accuracy of 74%, albeit with different cluster numbers. Consequently, models are developed with suitable cluster numbers to derive green rules with improved predictive accuracy. Each agglomerative and divisive hierarchical clustering model yields 12 practical green rules that can assist airport operators and facility managers in enhancing the green performance of their airport buildings. These explainable green rules serve as guidelines to improve airport facilities. Additionally, a web-based green rating tool is developed to demonstrate the proof-of-concept, utilizing the green rules extracted from the agglomerative model.

A novel method for measuring and analyzing the characteristics of coarse aggregates in concrete

The characteristic of coarse aggregate is an important factor affecting the building performance of concrete, so it is of great significance to measure and characterize it. In this paper, a method of measuring the characteristics of coarse aggregate in concrete buildings by using borehole camera technology is proposed. For the obtained borehole wall image, the corresponding image processing method is proposed, which can accurately recognize the coarse aggregate structure in the image. On this basis, the characteristics of coarse aggregate in the borehole wall image are calculated and analyzed. The calculation parameters include uniformity, gradation, aspect ratio, inclination angle, shape index, angularity and surface texture. The results show that the coarse aggregate in the borehole wall image used in this paper is not evenly distributed at different depths, the gradation continuity is good, and there are some coarse aggregates with large particle size. The aspect ratio of most coarse aggregates is less than 3 (AR1:97.71%; AR2:96.26%), and the content of needle flake coarse aggregate is less. The inclination angle is mostly distributed in the horizontal direction, accounting for more than 50%. The distribution of shape index and angularity has obvious correlation with the particle size range, and the shape index and angularity in the large particle size range are richer and more uniform. The distribution characteristics of surface texture of coarse aggregate in different particle size range are basically the same, and there is no obvious correlation with particle size. In this paper, a new in-situ measurement and multi characteristic parameter analysis of coarse aggregate in borehole is carried out, which provides a new idea for the evaluation of concrete building performance.

Seismic performance evaluation of self-centering balloon-framed CLT building

Balloon-framed cross-laminated timber (CLT) construction has a number of advantages when compared to platform-type construction; however, many international standards only include provisions for the latter as there is little research reported on the seismic performance of the former. In this paper, a seismic fragility assessment of a 4-storey balloon-framed CLT building in Vancouver, Canada, is presented. This structure is the first in North America that adopts a self-centering friction-based technology for the hold-downs (HDs). The seismic performance of the building was assessed by performing incremental dynamic analysis (IDA) on a three-dimensional finite element model using Cascadia Subduction Zone ground motions, and compared to a second building model with conventional dowel-type HDs. The analyses at the design intensity level showed that the self-centering building had an average maximum inter-story drift ratio of 0.67%, well below the 2.5% drift limit specified in the National Building Code of Canada. Based on the IDA and taking into account uncertainties, the building had a collapse margin ratio of 2.96 and a 5.2% probability of collapse at the design level. Compared to the conventional HDs, the friction-based HDs did not improve the building’s collapse capacity, but reduced the building drift by more than 20% at lower damage states, demonstrating the effectiveness of designing balloon-framed CLT structures with a resilient self-centering HDs in high seismic zones.

Performance Evaluation of High-Rise Buildings Integrated with Colored Radiative Cooling Walls in a Hot and Humid Region

Radiative sky cooling is an appealing form of heat exchange between terrestrial objects and outer space through thermal radiation, which is attracting worldwide interest due to its nature as passive cooling, that is, cooling without consuming energy. Due to a recent breakthrough in material science, sub-ambient daytime radiative sky cooling has been effectively achieved, which has significantly stimulated research interest in this field. In view of the numerous radiative coolers being reported as having excellent spectral properties and cooling ability under sunlight, integrating these superb cooling materials into building skins is a promising route to implementing radiative sky cooling technology. To this end, this study deploys state-of-the-art colored radiative cooling coatings as a new retrofitting strategy for building walls, and then conducts a comprehensive performance evaluation by considering a high-rise building situated in the hot-humid city of Hong Kong. Potential benefits of implementing differently colored cooling wall strategies, including their performance regarding thermal insulation, energy savings, economic viability, and environmental sustainability, were thoroughly investigated. The obtained results elucidate that for the utilization of the porous P(VdF-HFP)-based bilayer wall, relative to the monolayer, the frequency of the wall temperature exceeding the surrounding environment on an annual basis can be further reduced by up to 4.8%, and the yearly savings in cooling electricity vary from 855.6 to 3105.6 kWh (0.4–1.5%) with an average of 1692.4 kWh. Besides this, the yearly savings in net electricity cost vary from 1412.5 to 5127.3 HKD and the reduction in carbon emissions ranges from 1544.4 to 5606.1 kg with an average of 3055.0 kg. In addition, discussions of the combination of the super-cool roof strategy with blue porous polymer-based cooling walls reveal that the achievable savings in terms of energy costs and reductions in carbon emissions are 1.6 and 2.2 times more than either the application of the super-cool roof or porous polymer bilayer walls alone, respectively. This research offers new understandings of the deployment of colored cooling coatings on vertical building façades in hot and humid regions, which can considerably facilitate the realization of low-energy buildings in a passive approach for stakeholders.

Post-occupancy assessment of student housing facilities in an educational campus: case study of Visvesvaraya National Institute of Technology Nagpur, India

PurposeThis study attempts to conduct a post-occupancy assessment of the primary technical, functional and behavioral features in a student housing facility at the Visvesvaraya National Institute of Technology campus in Nagpur, India to see if the students are satisfied with the residential hostel's environs and facilities.Design/methodology/approachThe study reviews knowledge areas pertinent to the technical, functional and behavioral performance of student housing facilities by looking at the body of accessible literature. Even though the literature review gave a thorough overview of the broad issues, it was essential to learn about the situation as it is and addresses the specific issues brought up to undertake a thorough evaluation, just as every in-depth study depends on a reliable methodology. Therefore, a methodology appropriate for the research was developed, which helped in identifying relevant attributes for the questionnaire to be used for the survey. This aided in analyzing the building's performance evaluation through the sole users of the hostel which are the students. The survey data is analyzed by statical methods using SPSS software to identify the key determinants of satisfaction in students' residential hostels and if these are affected by the gender of the students.FindingsThe survey results were examined to determine the level of satisfaction with the selected performance aspects, and the major determinants governing it. The findings concluded that most of the technical and functional determinants of satisfaction in hostels are identified the same by both genders, except for some of the attributes in behavioral elements. The research looked at how well key success elements in the student housing facility had been implemented.Research limitations/implicationsResearch is carried out in CFTI only wherein architecture is one of the branches.Originality/valuePlanning professionals, architects, housing managers and facility managers involved in the design, construction and management of student housing facilities might benefit from feedback from a post-occupancy evaluation. The post-occupancy study can therefore aid in continually enhancing the standard and effectiveness of the facilities for planning, constructing, managing and maintaining student residential housing.

Modelling Short-Term Appliance Energy Use with Interpretable Machine Learning: A System Identification Approach

The modelling and analysis of appliance energy use (AEU) of residential buildings are important for energy consumption control, energy management and maintenance, building performance evaluation, and so on. Although some traditional machine learning methods have been applied to produce good prediction results, these models are usually not interpretable, in that they fail to explain how appliance factors make contributions to the variation of AEU individually and interactively. Explicitly knowing the role played by each of the appliance factors in explaining AEU, however, is very important for energy saving. Motivated by this observation, this study introduces an interpretable machine learning approach which is built upon the nonlinear autoregressive moving average with eXogenous inputs model. The advantage of the proposed model is that in comparison with other state-of-the-art machine learning methods, for example, feedforward neural network, recurrent neural network (e.g., gated recurrent unit), and long short-term memory network, the established model is not only able to produce more accurate energy use prediction, but more importantly, also fully transparent and physically interpretable, clearly and explicitly indicating which factors significantly affect the variation of AEU. The findings of this study provide meaningful insights for improving the AEU efficiency.

Application of BST Surfaces in the Seismic Performance Evaluation of Piloti-type Buildings

Application of BST Surfaces in the Seismic Performance Evaluation of Piloti-type Buildings