Building Archetypes Research Articles

Towards codification of thermal resilience upgrades in midrise residential buildings: a Canadian archetype energy model approach

Extreme weather events are predicted to continue increasing in intensity and frequency, raising a serious concern about the thermal resilience and adaption of buildings to these events. A single residential archetype building will be chosen as the case study and evaluated in three locations across Canada (Vancouver, Ottawa, Charlottetown) using three historical extreme weather events. Two upgrade scenarios will be considered, a code-compliant (NECB 2020) and a high-performance building option, to compare thermal performance through hot/cold weather outages. By using a variety of outage events in cold-climate zones for an archetype building, this paper’s methodology offers a useful framework for building practitioners to assess the effects of building upgrades on performance metrics and improve building resilience. This research could eventually contribute to the development of codes by providing upgrade packages to create infrastructure that both mitigates and adapts to future climate change extreme weather.

Cost-effective and optimal pathways to selecting building microgrid components – The resilient, reliable, and flexible energy system under changing climate conditions

Amidst the changing climate conditions, electricity retailers-led demand response programs and the emergency backup power for possible grid outages are often discussed in isolation, although several underlying linkages exist, which are important for how the existing building stock evolves with the energy transition. This study navigates through the linkages while investigating the levelized cost of electricity (LCOE)-based building microgrid components and undertakes a comparative analysis of energy optimisation models with and without emergency diesel generators. It also examines the building energy system’s resilience, reliability, and flexibility by using OpenStudio and HOMER Grid to develop energy simulation and optimisation models and other probabilistic models for a chosen building archetype in Sydney, Australia. This study finds that excluding the emergency diesel generator will require a larger battery storage system, increasing LCOE from A$0.17/kWh to A$0.20/kWh across climate scenarios. However, the larger battery storage system increases the probability of surviving outages (> 4 h) by around 10 % across climate scenarios. The LCOE increases up to 45 % for outages up to 8 h across climate scenarios and up to 85 % for outages up to 12 h. Additionally, for the building archetype, the probability of grid purchase (below the current average) is 0.78 in 2030, which drops to 0.55 in 2050. The probability of grid sales (above the current average) also drops from 0.69 to 0.46. Thus, while the narratives around the building energy system’s flexibility overstate the electricity exchange between the building and the grid, this study finds that increasing the on-site production utilisation rate and larger battery throughput contributes to demand response application and flexibility.

Psychosocial well-being needs of alcohol/drug sufferers and therapeutic architectural solutions in rehabilitation centre, Nigeria: a cross-sectional study

Background In a rehabilitation centre designed for drug and alcohol recovery, the therapeutic environment may enhance the psychosocial well-being of sufferers and facilitate a speedy recovery. This study aims to investigate the psychosocial well-being needs (PWNs) of users in an alcohol and drug rehabilitation centre (ADRC) and how therapeutic architectural solutions (TASs) can promote a healing environment through design. Methods The study employed a mixed research method (qualitative and quantitative). The research strategy was a case study of four rehabilitation centres in Nigeria’s busiest cities; Lagos, Abuja, Port Harcourt, and Enugu. Data were collected via survey questionnaires, an in-depth interview guide, an observation guide, and a checklist of TACs in a healthcare facility. We first investigated what the PWNs of alcohol and drug sufferers (ADSs) are, then identified the therapeutic architectural components (TACs) of an ADRC, before determining what effect TACs have on the recovery process of substance abuse patients. Results The findings identify eight PWNs and nine TACs essential in an ADRC. The result indicates that TACs have a significant effect on the recovery process of ADSs. However, based on the study results, a framework (framework here refers to using an intelligent and sensitive conceptual design structure as a standard guide for the building archetype rehabilitation facility, which supports mental health service delivery) may be recommended for rehabilitation facilities with TASs for Substance Abuse Disorder (SUD). Conclusion The study result presents a correlation between ADSs’ recovery rates and a well-designed therapeutic environment. So, there is a need for evidence-based design (EBD) of rehabilitation centres with full compliments of a therapeutic architectural environment and integration of mental health services into routine care in sub-Saharan Africa. Limitations include that analyses were cross-sectional and thus may not deduce causal directions, and the authors based the result on self-report.

Experimental implementation of the nonstructural element simulator on shake table (NEST) for an archetype tall building

AbstractNonstructural damage, usually predominates in buildings subjected to low to moderate seismic intensities and contributes significantly to business interruption and economic losses. It promotes a rapidly growing need for experimental research and qualification of various types of nonstructural elements in buildings in the recent decades. To provide a novel option of experimentally simulating the realistic boundary conditions for various nonstructural elements in labs, we developed the Nonstructural Element Simulator on Shake Table (NEST), a passively controlled three‐layer substructural testbed driven by existing shake tables. This paper presents the first experimental implementation of NEST on a challenging 42‐story archetype tall building. The dynamic properties of the substructure were tuned to adapt the archetype building and the required shake table motions were solved as a reverse problem by an open‐loop control algorithm. The test results proved the capability of NEST to replicate the history responses of the target floors in the archetype building under either recorded earthquake ground motion or artificial loading protocols for qualification purposes. In all cases, the synthetic relative error in the floor accelerations and the inter‐story drift within the frequency range of interest was less than 5% in the numerical domain and less than 30% in the physical realization. The seismic responses of a variety of nonstructural elements to the substructural motions show that, in the mid‐story of the archetype tall building, the ceiling sustained minimum damage because of the small floor acceleration while the indoor contents slid significantly and even overturned.

Structural characterisation of Eastern Canada’s old industrial masonry buildings via typological analysis: part II – building database analysis

The typological analysis conducted in the companion paper is further expanded within this work through the critical analysis of a new extensive building database specifically conceived for old industrial masonry buildings in Eastern Canada. The identification of recurrent unreinforced masonry (URM) building archetypes in this low-to-moderate seismic region is essential to decrease uncertainties currently prevalent in the analysis of local old URM structures, often targeted for adaptive reuse. The database assembled herein comprises various heritage designated buildings across Eastern Canada and was compiled harmonising existing repositories at the federal, provincial and municipal level with an unprecedented focus on structural features. The characteristics of these resources are quantitatively analysed and applied to a case study of the city of Montréal, one of the most important industrial centres in nineteenth and twentieth century Canada. Outcomes from this study will guide practitioners and researchers involved in the structural and seismic assessment and retrofit of old industrial URM constructions, enable more and less invasive rational retrofit designs and inform the new Existing Structures provisions to be included in the 2030 National Building Code of Canada.

Structural characterization of Eastern Canada’s old industrial masonry buildings via typological analysis: part I – construction technologies through time

Industrial unreinforced masonry (URM) buildings are an important part of the existing building stock in Eastern Canada, a region characterised by high-frequency and low-to-moderate magnitude earthquakes. Despite their inherent vulnerability to earthquakes, these non-engineered buildings are being increasingly targeted for reuse purposes in many densely populated cities in the region. A lack of knowledge of local construction techniques, materials and architectural types paired with limited information of their characteristics leads seismic retrofits to be even more disruptive than usual. Often, such seismic retrofits result in expensive and complex designs or avoidable partial demolitions. Typological analysis is utilised in this work to identify local recurrent building archetypes and technologies through archival resources, as well as their evolution through time, as a first step to addressing the lack of information about the structural characteristics of this building type. Results from this study inform subsequent critical and more quantitative investigations conducted in the companion paper, where the first building database focusing on Eastern Canada’s old industrial URM constructions is presented and comprehensively reviewed. This research will enable engineering practitioners and applied researchers to decrease epistemic uncertainties and confidence factors in their structural/seismic assessments, vital to develop rational retrofits.

Impact of glazing type, window-to-wall ratio, and orientation on building energy savings quality: A parametric analysis in Algerian climatic conditions

Opaque surfaces, such as walls, are well-known for their significant contributions to heat loss and energy demands in buildings. However, transparent surfaces, such as windows, are equally critical to a building's energy performance. The design of these transparent elements requires a careful balance of various factors, including window size, glazing type, and orientation, each of which plays a pivotal role in enhancing energy efficiency. This study explores the optimization of these factors during the design process, emphasizing their impact on the overall building performance.This research evaluates the potential energy savings in a building archetype representative of the Algerian building stock. Utilizing the EnergyPlus simulation tool, the study conducted 1152 simulations on a baseline model to generate a comprehensive dataset detailing the building's energy demands for heating and cooling across various climatic conditions. The findings reveal that annual energy savings for this type of housing essentially depend on its climatic zone and can range from 6.92 % for a hot semi-arid climate (Bsh) to reach a maximum of 9.75 % in a cold semi-arid climate (Bsk), a window-to-wall ratio (WWR) of 60 % typically maximizes energy efficiency, low-E glazing proved most effective in most cases, although regions needing significant solar protection favored alternative glazing types. Optimal window orientation generally trends Eastward, except in regions where southern exposure better supports solar management, highlighting the complex relationship between architectural design choices and energy efficiency.

Gaussian-based plug load profile prediction in non-residential buildings archetype

This paper presents an innovative approach to addressing the prevalent challenge of simulation uncertainty in urban building energy modeling (UBEM), focusing on accurately determining occupant-related input parameters. Traditional UBEM methods typically rely on standard schedules to create archetype models, which often fail to reflect the variability observed in real-world scenarios. To overcome this limitation, this research introduces a novel framework for generating electricity use profiles in institutional building archetypes across various climate zones. This framework integrates k-means clustering with Gaussian processes, effectively incorporating uncertainties into the prediction models. The evaluation of this stochastic model suggests that the methodology can give acceptable predictions on the electricity consumption of institutional buildings. The model demonstrates robust predictive capabilities, achieving a CVRMSE as low as 11% on weekdays and 8.7% on weekends, reflecting its strong predictive performance. However, its performance varies among different clusters and time periods, with specific clusters displaying more significant predictive inaccuracies at particular times. These results emphasize the importance of fine-tuning models and offer opportunities for improvement in predicting urban building energy consumption. This can be achieved by incorporating sensor-derived data to develop more detailed building profiles that include variable electricity usage patterns. This methodology has been integrated into a UBEM tool, enabling the generation of more realistic electricity load profiles.

Shake‐table tests on steel moment‐resisting frames with detachable yielding zones

AbstractThis article presents an experimental campaign based on shake table tests conducted as part of the European research project FUTURE, funded in the framework of the H2020‐INFRAIA SERA program. Three steel moment‐resisting frame structures equipped with replaceable dissipative elements were tested. The experimental mockups were scaled two thirds from a reference building archetype designed per the latest draft of the upcoming version of Eurocode 8. Each mockup has different bolted end‐plate beam‐to‐column joints, namely reduced beam section (RBS), extended stiffened endplate (ESEP), and Haunched types, designed with European seismic prequalification rules. Additionally, detachable column segments at the base were designed with Reduced Column Sections (RCS) to localize the plastic deformation and enforce the intended global dissipation mechanism. The slab was detailed with reduced areas around the columns to minimize the direct composite action at beam‐to‐column joints. The mockups were subjected to incremental near‐field excitations with combined horizontal and vertical components. In this article, the experimental campaign is first described, providing details on the mockup's geometry, design, scaling, ground excitations, and sensor distribution. Then, the observed global and local responses are discussed. The results demonstrate that the global response of the mockups was significantly influenced by the type of beam‐to‐column joints, which exhibited the expected damage. The RCS configuration in the column bases also showed satisfactory behavior without leading to local or global instabilities throughout the performed tests.

Interpretable machine learning models for displacement demand prediction in reinforced concrete buildings under pulse-like earthquakes

This work proposes a novel procedure to guide the development of machine learning models for estimating the seismic demand in existing reinforced concrete (RC) buildings. The proposed approach is organized across two scales. A large-scale (nonparametric) machine learning model is first obtained by means of Gaussian Process Regression (GPR) using all candidate building attributes and intensity measures. SHapley Additive exPlanations (SHAP) values are utilized to facilitate its interpretation and to assist the rational selection of a small subset of intensity measures, which is finally employed to develop a (symbolic) reduced-scale machine learning model by means of Genetic Programming (GP). Simplified models of archetype buildings are adopted to develop machine learning techniques at both scales, in such a way to alleviate the simulation time for preparing large datasets. Refined models representative of actual buildings are instead considered for the unbiased final assessment.The proposed approach is applied to develop predictive machine learning models for the maximum inter-storey drift in bare frames, pilotis frames and frames with infills under pulse-like seismic ground motions. Consequently, the critical examination of the SHAP values revealed the most significant intensity measures and unfolded interesting patterns depending on the occupancy rate of the infills. Moreover, the final assessment demonstrates that this approach allows the management of a non-homogeneous building stock consisting of very diverse structural systems (i.e., spanning from existing buildings designed against gravity loads only to buildings that comply with outdated seismic codes) while providing satisfactory predictions of the seismic demand with minimum computational effort.

CLIMAEXTREMO: A New Risk Indicator for the Health Risk to Building Occupants during Extreme Weather Events in Portugal

Portugal is the country in Europe where the death rate in winter and summer has the highest correlation with outdoor temperatures. The Portuguese National Institute of Public Health Ricardo Jorge has developed a national warning system for heat waves called ICARO, which has been in place since 1999 (and is the oldest in Europe). However, it presents some limitations, namely, the low spatial resolution (five regions in Portugal’s mainland), the low temporal forecasting period (one day), and the fact that it was only accessible to health authorities until very recently. This work describes the development of a new public dashboard that uses a new early warning index for extreme weather events, the CLIMAEXTREMO index, which extends the current warning system by improving the current forecasting models for risk by integrating new sources of public data and increasing the spatial and time resolution of the warnings to the municipality or the parish level. The new index is a combination of a new model to estimate the relative mortality increase (updating the model used in ICARO) together with a model of the indoor temperature of building archetypes for all municipalities and a vulnerability index that considers socio-demographic economic indicators. This work discusses the results of the new risk indicator for the heat waves that occurred in Portugal at the end of June and mid-August 2023, and it shows that the index was able to indicate a high risk for the municipalities that had an increase in the number of deaths during that period.

Climate change impact on energy savings in mixed-mode ventilation office buildings in Brazil

With the projected increase in temperature, coupled with the rise in intensity and frequency of heatwaves caused by climate change, it has become essential to investigate the impact of climate change on building energy consumption and thermal comfort. This study focuses on office buildings with mixed-mode ventilation in Brazil as a case study. Using a typical 15-story office building archetype for various Brazilian cities, considering current and predicted scenarios (2050 and 2080), the potential for energy savings through natural ventilation was assessed. It was observed that the average increase in temperature and wind speed in the predicted climate varied in each city and showed significant variations over the years. Predicted projections under high-emission scenarios (SSP5-8.5-2080) reveal significant increases in energy consumption, highlighting the vulnerability of cities like Belo Horizonte, which could face a 77% increase in total energy consumption. Additionally, the analysis of HVAC hours of use over time indicates a progressive increase, suggesting a potential reduction in natural ventilation as a mitigation strategy. Mitigation measures, especially in the SSP1-2.6 scenario, are crucial to stabilize HVAC consumption and preserve the use of natural ventilation, emphasizing the importance of sustainable approaches to address the energy challenges arising from climate change.

Extending the concepts of response spectrum analysis to nonlinear static analysis: Does it make sense?

The study presented in this paper aims to assess the recent novelties proposed by new generation building codes about the extension of some concepts at the base of linear analyses to the procedures of nonlinear static analysis. The nonlinear static analysis (pushover) is an extensively adopted approach by engineers and practitioners for purpose of assessing the seismic performance of new and existing buildings, although several drawbacks characterize this methodology, as evidenced by the existing scientific literature. In order to overcome the well-known limitations characterizing nonlinear static analysis, and to offer an alternative to nonlinear dynamic analysis, the recent upgrades of some building codes, such as the Italian one, provide different rules to employ in nonlinear static procedures. Among these new provisions, one regards the possibility to use a horizontal load profile proportional to the storey forces derived from a response spectrum analysis in all cases (i.e., also for irregular buildings). The main goal of this study is to demonstrate the reliability of this new load profile when strong irregularities arise. To this scope, the seismic behaviour of a sample of archetype buildings characterized by increasing in-height irregularity was investigated. The sample of buildings was subdivided in two subsets characterized by different design levels, one conceived by considering the prescriptions of the new Italian building code and one designed according to the oldest Italian building code (i.e., without considering anti-seismic details). The sample of buildings, simulating new and existing cases, was firstly modelled and after investigated through nonlinear static analyses by employing both traditional (i.e., inverse triangular- and uniform-like) and new (i.e., derived from response spectrum analysis) load profiles. After, nonlinear dynamic analyses were run, in order to assess the capacity of both traditional and new load profiles to capture the dynamic behaviour of the investigated archetype buildings. The performed analysis campaign demonstrated the efficiency of the new load profile proposed by the code, and the output of the work provided an answer to the main question posed by the authors: does it make sense to extend the concepts of response spectrum analysis to nonlinear static analysis?

Transferring energy signatures across space and time to assess their viability for rapid urban energy demand estimation

Energy signatures offer a rapid bottom-up energy modelling tool that relates thermal building space heating and cooling demand to ambient temperature. While simplified from a building physics perspective, energy signatures can easily exploit existing buildings’ operational datasets for their calibration, fitting individual buildings or archetypes. To assess their viability in modelling buildings’ heating and cooling demand at urban or larger scales, it is important to assess whether the same building model expressed as an energy signature could be used under different weather conditions, through spatial (location) or temporal (future weather) transfer. As energy demand depends on environmental factors other than temperature, a systematic error quantification from transferring energy signatures across different climatic contexts is required. These errors are assessed by comparing signature-based estimates to EnergyPlus simulations of a single-family home and an office building archetype exposed to current and future climate across eight US climate zones. Energy signatures are confirmed to be a valid approach if sufficient heating and cooling data is used for their identification. On an annual basis, signature-based estimates result in an error of approximately ± 30 % if the signature is spatially transferred, which can exceed 75 % for unsuitable source locations. When energy signatures are temporally transferred, an annual error of only ± 5–10 % is expected. At a daily resolution, the inherent error from applying the energy signature method is significantly larger than the additional error introduced through a spatiotemporal transfer of the energy signature.

A genetic algorithm framework for seismic retrofit of building portfolios to enhance community resilience

Earthquakes can impose significant human, societal, and economic burdens, emphasizing the necessity of community preparedness, including mitigation. Seismic retrofitting, a key preparedness measure, seeks to enhance structural resilience against earthquakes. Despite the effectiveness of community-level plans, challenges persist in quantifying their effectiveness and optimizing resource allocation. To address these challenges, this paper introduces a genetic algorithm (GA) framework designed to optimize seismic retrofitting of building portfolios in communities, aiming at achieving community-level resilience goals. The proposed framework, part of a simulation-based approach, probabilistically models building damage and post-earthquake functionality of the community. It calculates seismic retrofit levels (in terms of percentage shift in the median of fragility curves) for each building archetype, considering different shifts in the damage fragility curves and the percentage of retrofitted buildings for each retrofit level. The optimization problem maximizes average post-earthquake functionality, constrained by available community funds. The proposed GA framework is illustrated using the real-world example of Salt Lake City, Utah, in collaboration with the NIST Center for Risk-based Community Resilience Planning, showcasing its application through the Interdependent Networked Community Resilience Modeling Environment (IN-CORE). The results of the considered illustrative example reveal substantial improvements in community functionality under different budget scenarios and retrofit levels. The GA framework represents a significant advancement in supporting informed decisions for community resilience. Community leaders can use its outputs to make informed decisions, considering specific factors and trade-offs between benefits and costs.

Lateral load response and collapse probability of reinforced concrete shear walls retrofitted for repairability

AbstractThis paper demonstrates the efficacy of a new reinforced concrete shear wall seismic retrofit method through a series of nonlinear static and incremental dynamic analyses. Unlike traditional retrofit methods, the method investigated aims to convert conventional walls into self‐centering walls whose behavior is governed by rocking and flexure. The retrofit involves creating a cold joint at the foundation–wall interface, cutting some reinforcing bars to allow rocking, adding external post‐tensioning to enable self‐centering, and externally confining wall toes to prevent concrete crushing. The retrofit was applied to two building archetypes, each with two different shear wall designs. The four walls were retrofitted by varying retrofit parameters (portion of the vertical reinforcement bars cut, and external post‐tensioning amount). Nonlinear static and nonlinear response history analyses were performed using experimentally validated, computationally efficient models that simulate walls with fiber‐based beam–column elements. Incremental dynamic analysis was used to create collapse fragility functions for pre‐ and post‐retrofit walls. The results show that the retrofit is effective when some vertical reinforcement bars are left uncut across the foundation–wall interface. The retrofit is more effective for walls with vertical reinforcement distributed across cross‐section as compared to walls with reinforcement concentrated near boundary elements and for walls with structurally efficient amounts of reinforcement as compared to walls with higher amounts of reinforcement. This is attributed to the larger amount of reinforcement bars cut in walls with concentrated reinforcement layouts or heavy reinforcement amounts, leading to a larger loss of strength, recovery of which requires larger amounts of post‐tensioning.

A multi-criteria decision-making method for thermal insulation material selection in nZEB level questioned affordable multifamily housings

An affordable multifamily housing building archetype project was researched to improve the building envelope’s thermo-physical performance based on thermal insulation configurations to optimize the energy use, life-cycle cost, environmental impact, and thermal comfort with questioning nZEB concept. Five thermal insulation materials as extruded polystyrene (XPS), expanded polystyrene (EPS), rockwool (RW), glass wool (GW), and cellular glass (CG), were studied with four> attribute variations (thermal comfort, density, embodied carbon, and embodied energy) and five thickness variations (0.04, 0.08, 0.12, 0.16, 0.20 m). A total of 100 alternative scenarios> were obtained for the decision-making process, with four performance criteria to be evaluated in terms of energy, cost, thermal comfort, and environmental impact. Equal weights method (EWM), weighted sum method (WSM), and analytical hierarchy process (AHP) were examined for the multi-criteria decision-making among 46 scenariosvel. The analysis beyond the cost-optimal energy efficiency level, which varies between 64.89 and 72.62 kWh/m2a for primary energy use with a potential 10% further reduction, ensures the European Commission’s recommendations that target 50–70 kWh/m2a of primary energy use for housings in the continental climate to achieve nZEB. The best scenarios cover significantly the XPS at the head due to lover investment costs, where RW, GW, and EPS are followed among the best 10 scenarios, respectively. Besides, AHP provides a more effective distribution of weighting factors than the WSM where the best scenarios of the AHP covers 12 cm thermal insulation alternatives by balancing the investment costs and energy efficiency levels. The paper provides insight into thermal insulation material selection while searching for the advantages and priorities of thermal insulation materials’ attributes beyond the cost-optimal energy efficiency level to reach the nZEB range.

The influence of torsion on acceleration demands in low-rise RC buildings

AbstractThis paper presents a study of acceleration demands in low-rise reinforced concrete (RC) buildings with torsion, evaluated by quantifying peak floor accelerations (PFAs) and floor response (acceleration) spectra (FRS). The study was performed with the aim to provide simple empirical formulas to quantify the amplification effects due to torsion, which can occur in most of the existing and new RC buildings. With this goal in mind, a set of eight archetype buildings was selected, characterized by an increasing floor eccentricity obtained by moving the centre of rigidity (CR) away from the centre of mass (CM). Numerical models of the proposed set of archetype RC buildings were considered in both linear elastic and nonlinear configurations. For the latter, the properties of models were widely varied, by systematically modifying parameters of plastic hinges, in order to obtain a sample of 1000 models. Non-structural components (NSCs) were considered linear elastic in all cases. To investigate acceleration demands, a set of forty Eurocode 8 spectrum-compatible ground motion records were used as input. For linear elastic building models, it was observed that the change of demands depends on the position of the NSC (in-plan and in-height), and on the distance between CR and CM. On the other hand, for nonlinear models, additional parameters must be considered, such as the building ductility (μ) and yielding force (Vy). New regression models were proposed for quantifying the observed differences in PFAs and FRS when torsion occurs. The efficiency of the proposed models was assessed by testing the new formulas on an existing case study building, as well as on the well-known SPEAR building.

Community‐level post‐hazard functionality methodology for buildings exposed to floods

AbstractThis paper presents a building‐level post‐hazard functionality model for communities exposed to flood hazards including the interdependencies between the population, buildings, and infrastructure. An existing portfolio of building archetypes is used to model the post‐hazard physical flood functionality of different building typologies within the community with the goal of supporting resilience‐informed decision‐making. Specific fragility‐based flood functionality curves were developed for this portfolio to quantify the exceedance probability of a prescribed set of functionality states. While the physical functionality of buildings is significant to the total functionality of a building and community resilience assessment, the functionality of utilities such as power and water along with the accessibility of the household to essential services such as schools and hospitals is crucial to measure their total functionality. Therefore, functionality models for essential infrastructure were developed to assess housing unit‐ and building‐level functionality following flood hazards. This model also accounts for the functionality of the road network following a flood hazard to identify the level of accessibility of households to different services (e.g., schools, hospitals, gas stations, shopping centers, etc.). The main novelty of this paper is the ability to quantify the total functionality of buildings based on a socio‐physical formulation after including the interdependencies between the functionality of the physical systems and the subsequent functionality of the socio‐economic systems, which is key to measuring resilience at the community level. This was evident from the analysis results using a testbed community of Lumberton, NC, showing that the physical functionality is not sufficient to quantify the total post‐hazard functionality of buildings and that the functionality of other subsystems such as utilities and accessibility to essential services are also needed to quantify the total functionality of buildings and the livability of households.

Expanding Building Archetypes to Estimate the Indoor Environment Thermal Storage Capacity in the Danish Building Stock when Performing Demand Response

Short-term demand response leveraging the energy flexibility of the building stock is a key solution to decarbonise and ensure the reliability and sustainability of smart energy grids dominated by intermittent renewable energy sources. Most of the building demand response capacity depends on different forms of energy storage in the built environment. In addition to electric batteries, hot water storage tanks, ground source heat exchanger or dispatchable on-site renewable energy sources, buildings possess a tremendous thermal storage capacity within the thermal inertia of their structure and indoor environment. The latter can easily be harnessed with heating/cooling indoor temperature setpoint modulation strategies. However, there is a clear lack of large-scale estimates of this indoor environment thermal storage capacity in the different building stocks. To tackle this issue, detailed dynamic numerical models of the different building typologies in European countries are generated to assess the potential of demand response strategies at nationwide levels. The current paper presents the preliminary results on the thermal storage capacity in the Danish building stock when performing heating temperature setpoint modulation. These results are based on estimates of the effective thermal inertia of the different building archetypes in Denmark. They show that the Danish building stock contains immense thermal storage capacity, which is comparable to all combined batteries in a large fleet of electric vehicles or all industrial-size storage tanks in district heating plants.