Building sustainability and performance through simulation

Performative Microforests: Investigating the potential benefits of integrating spatial vegetation environments into buildings, in regards to the performance of buildings, their occupants + local ecosystems

A novel big data building performance evaluation knowledge processing and mining system utilizing visual analytics is going to be presented in this paper. A large dataset comprised of building information, energy consumption, environmental measurements, human presence and behavior and business processes is going to be exploited for the building performance evaluation. Building performance evaluation is one of the most important factors in engineering that leads to building renovation and construction with low energy consumption and gas emissions in conjunction with comfort, utility and durability. For this purpose, business processes occurring in the building are correlated with the energy consumption and the human flows in the spatiotemporal domain modeling the dynamic behavior of the building. These models lead to the extraction of useful semantic information and the detection of spatiotemporal patterns that are important for the evaluation of the building performance. Furthermore, a number of novel visual analytics techniques allow the end-users to process data in different temporal resolutions and with different temporal filters, assisting them to detect patterns that may be difficult to be detected otherwise. The proposed visual analytics techniques support design and energy management decisions by visualizing the building measurements regarding business and comfort aspects. To do so, the proposed system includes a variety of techniques and components, properly selected to offer quick identification of focal points and evaluation of the building performance. Considering the increasing interest and the green building goals of almost all world governments including EU, the suggested methodology and application could be rendered a very useful tool for the Architecture and Engineering Community working on Building Performance Simulation and Analysis, and all related communities in Architect, Engineering and Construction (AEC) industry.

The scientific community has established a clear link between the built environment and various environmental problems. Various strategies have been implemented to mitigate the negative impacts of buildings and to address broader environmental challenges. One such strategy is the adoption of sustainable building practices. Among the factors contributing to the environmental impacts of buildings, efforts to achieve thermal comfort play significant role. Particularly due to the energy consumption involved. At the same time, thermal comfort is also a critical factor influencing human productivity, including academic performance. Comfortable learning environments are known to enhance students’ learning outcomes. This research presents a case analysis conducted at State Elementary School 91 Sipatana, Gorontalo City, Indonesia. Measurements were carried out on December 24, 2022, from 06.00 to 18.00. Room temperature was recorded using an Elitech GSP-6 data logger, and further simulations were carried out using Ladybugs and Honeybees. The purpose of this study is to evaluate building performance in achieving thermal comfort by considering solar radiation exposure, roof surface temperature, room temperature, and Predicted Mean Vote (PMV) values. Comparisons were made across different building materials, including variation in roofing, wall types, and ventilation systems. The wall in the existing structure are composed of concrete with a fiber wall. The findings highlight the impact of roofing materials, wall construction, and ventilation on the PMV, roof surface temperature, and indoor air temperature. Based on-site measurements, the average classroom temperature was 30.5°C. Among the simulation configurations, Model 3 which featured a metal roof with a cool roof technology, concrete walls, and added ventilation demonstrated the best thermal performance. It maintained a roof surface temperature just above 25°C and an indoor air temperature close to 30°C, showing the effectiveness of cool roof technology and adequate ventilation in reducing heat accumulation.

The urgent need to reduce global greenhouse gas emissions has positioned the building sector as a critical arena for advancing sustainable energy technologies. Hybrid solar–geothermal systems, which combine solar thermal collection with shallow or deep geothermal heat exchange, offer a reliable and renewable pathway to achieving net-zero energy performance in buildings. This review evaluates the role of advanced materials in enhancing the efficiency, durability, and environmental sustainability of hybrid systems. Emphasis is placed on materials used in solar collectors, geothermal piping, heat exchangers, and thermal energy storage, highlighting their thermal conductivity, mechanical resilience, corrosion resistance, and life-cycle impacts. Special attention is given to emerging bio-based and recyclable options, as well as to material optimization strategies tailored to diverse climatic conditions. Case studies demonstrate that the integration of innovative materials leads to measurable improvements in system performance, energy savings, and long-term durability. The review concludes by identifying research gaps in cost reduction, sustainable material development, and interdisciplinary integration, underscoring that advanced materials are not only enablers but key drivers of hybrid solar–geothermal adoption in sustainable buildings.

This study systematically examined recent research trends in multi-objective optimization (MOO) for building performance from 2020 to 2024 and proposed a conceptual framework integrating intelligent algorithms, simulation tools, and climate adaptation strategies. A thematic analysis of 40 peer-reviewed articles was conducted using ATLAS.ti, revealing three dominant research themes: intelligent algorithms, building performance simulation techniques, and adaptive design for climate change. Quantitative analysis highlights China’s prominent contributions to the field, while the thematic analysis reveals three key findings: (1) optimization methods based on intelligent algorithms such as NSGA-II, artificial neural networks, and gradient-boosted decision trees significantly enhance computational efficiency; (2) dynamic simulation integrated with lifecycle assessment enables a more comprehensive evaluation of building performance; and (3) climate-adaptive strategies improve building resilience to future climate uncertainties. Based on these insights, the proposed framework combines these three components to achieve computational efficiency, maintain accuracy, and improve adaptability. The framework provides a systematic, data-driven approach to address trade-offs among energy efficiency, thermal comfort, and indoor air quality. Its practical value is demonstrated through applications in residential, educational, and commercial buildings across various climate zones. These case studies highlight the framework’s capability to guide high-performance, sustainable, and climate-responsive building design. Furthermore, this review identifies future research priorities, including the integration of dynamic simulation with real-time optimization, and the development of lifecycle-oriented, comprehensive evaluation systems to address emerging environmental challenges. Overall, the study contributes a holistic perspective and actionable methodology for advancing intelligent, climate-adaptive building performance optimization.

Green buildings are created to provide healthy and productive indoor environments for their occupants. While achieving these goals can consume energy and funds, efforts to conserve energy can end up degrading the quality of the indoor environment. Therefore, a healthy indoor environment will more likely be achieved if certain priorities are established early on in the design process. These goals include understanding the integrated relationship between saving energy and the achievement of good indoor air quality, the importance of effective air sealing of the building envelope, the ability to control air contaminants, and the importance of continually managing moisture and ventilation performance over the life of the building.While this market transformation toward green buildings has begun largely through the efforts of the U.S. Green Building Council and the LEED products, there are still many more steps along this path that need to be taken if healthy green buildings are to become the norm. Just as operational investment decisions need to focus more on Life Cycle Costs as opposed to merely minimizing initial costs, building performance evaluations need to be more holistic in scope and consider how all of the building components interact to contribute to the achievement of a healthy indoor environment as a whole.The goal of achieving and maintaining healthy green buildings is dependent on having accurate and timely feedback on such key building parameters as the effectiveness of air barrier sealing, air contaminant control, and ventilation and moisture management performance. The evaluation of these parameters depends on knowing what metrics relating to building performance are most important to have, and what are the best and most accurate ways of obtaining this information on building performance in a timely fashion. Having accurate diagnostic feedback on building performance in these areas will not only help achieve and maintain a healthy environment, it will also reduce risks and uncertainties in building operation and reduce the time between identifying a problem with the building, and when it is understood and corrected. The longer this interval takes, the more the occupants will be adversely affected, and potentially suffer degradations in health, morale, and productivity.

INTRODUCTION Architecture is now more than ever dominated by scientific method. This paper examines what is really at stake when designing sustainable architecture. It is a methodological study and focused on science as understood in Post Occupancy Evaluation (POE) of sustainable buildings, specifically school buildings. Questions raised in this paper include the very real problem of designing for children’s comfort and education in school architecture, but also much wider issues than simply those of the design of children’s environments. Children's ways of knowing, thinking and feeling, represent the antithesis of what might be considered reliable feedback about the performance of a building. Children are not considered to have the same status as adult human beings; the truth of their experience, their understanding of reality, the reliability of their data, is in question. Diverse ways of constructing knowledge are recognized by researchers, even in the field of construction, but even from the perspective of contemporary architectural theory, working with children tends to be seen as the extreme of what is possible in the co-production of knowledge. Children's contribution and their right to research has dubious significance for many, but the problem exposes a conflict between different world views at stake in design for a community, especially a community as extensive as that for a school, and for scientific method which is at the forefront of research in sustainable design and building evaluation. Green or sustainable schools are an important site from which to explore the broad questions of changing social behaviors, inclusivity and difference–as well as the more usual reductions in energy efficiency and innovation in material production. Building performance studies, including post-occupancy evaluations (POE), are increasingly taking into account not only the performance of buildings, in terms of energy and water efficiency, but also the behavioral and social dimensions of sustainability, in order to assess the actuality of sustainable buildings, rather than simply design intent. Within the field of construction research, scholars are increasingly challenging methods adopted to collect performance data, questioning tools and measures, and highlighting the complexity of the impact of any building in its environment, including its social or economic contexts. Innovative ethnographic and art-based research methodologies are being adopted as examples of new ways to examine building users' relationship to their environments and this has included building contractors' experiences (Pink et al 2010). Nevertheless, these are experimental studies with little exploration as yet, and little impact on the industry and professions.

The rise of artificial intelligence (AI) presents a transformative opportunity for architects seeking to achieve sustainable design goals. AI’s ability to extract and analyze vast amounts of data during the pre-design, design and construction, and post-construction phases empowers architects to make data-driven decisions that optimize building performance. This data can provide strategies for material selection, time and energy efficiency, and resource management, ultimately contributing to the realization of Sustainable Development Goals (SDGs) through building design. This research explores the specific applications of artificial intelligence in sustainable building design. To achieve this objective, a systematic literature review and case study analysis were conducted, utilizing academic journals and article reports as primary data sources. Specific keywords were employed to narrow the scope of the research. The analysis involved classifying AI applications and investigating their potential benefits in : 1) analyzing environmental conditions, such as historical and real-time climate data to optimize building orientation, and natural ventilation strategies, 2) facilitating the design process by providing real-time visualization and recommendations for achieving sustainable designs, 3) optimizing building materials and construction efficiency, by analyzing material properties, and providing accurate data during the construction stage to gives broader image on how to build efficiently and 4) simulating building performance, by predicting and doing assessment on the building’s energy consumption and thermal performance, allowing architects to refine design for optimal sustainability. By integrating AI into these workflows, architects can address critical challenges like global warming and climate change by creating high-performing, sustainable buildings. Keywords: Artificial Intelligence, Energy Efficiency, Global Warming, Sustainable Architecture

PurposeBecause of global environmental concerns, sustainable design has become a mainstream building design goal in recent years. Sustainable development is even more urgent in the light of global climate change. This paper aims to examine the contributions which building information modelling (BIM) can make to the production of sustainable building designs.Design/methodology/approachVarious research methodologies have been adopted, including literature review, design tool analysis, a case study and structured face‐to‐face interviews. Data collected were synthesized as part of the research process.FindingsBIM is found to be ideally suited to the delivery of information needed for improved design and building performance. Two most significant benefits of BIM for sustainable building design are: integrated project delivery (IPD) and design optimization. However, there are also barriers to adopting BIM for sustainable design.Research limitations/implicationsThis paper does not attempt to address all aspects of BIM functionality because the scope of BIM is very great and the resources of this research were limited.Practical implicationsSuccessful implementation of BIM is able to eliminate the extra cost of design changes during the subsequent phases of construction process. BIM, therefore, is also capable of enhancing the project delivery culture in future.Social implicationsBIM solutions can contribute to the selection of best solutions to reduce energy and resources consumption. This new technology and the approach also can generate the need of more innovative professionals and job opportunities.Originality/valueThis paper investigates the contribution of BIM to sustainable buildings from the perspective of design performance and improved communication and coordination.

Using the environment as a strategic tool is one of the most cost-effective and enduring approaches for improving public health; however, it is one that requires multiple perspectives. The purpose of this article is to highlight an innovative methodology that has been developed for conducting comprehensive performance evaluations in public sector health facilities in Canada. The building performance evaluation methodology described in this paper is a government initiative. The project team developed a comprehensive building evaluation process for all new capital health projects that would respond to the aforementioned need for stakeholders to be more accountable and to better integrate the larger organizational strategy of facilities. The Balanced Scorecard, which is a multiparadigmatic, performance-based business framework, serves as the underlying theoretical framework for this initiative. It was applied in the development of the conceptual model entitled the Building Performance Evaluation Scorecard, which provides the following benefits: (1) It illustrates a process to link facilities more effectively to the overall mission and goals of an organization; (2) It is both a measurement and a management system that has the ability to link regional facilities to measures of success and larger business goals; (3) It provides a standardized methodology that ensures consistency in assessing building performance; and (4) It is more comprehensive than traditional building evaluations. The methodology presented in this paper is both a measurement and management system that integrates the principles of evidence-based design with the practices of pre- and post-occupancy evaluation. It promotes accountability and continues throughout the life cycle of a project. The advantage of applying this framework is that it engages health organizations in clarifying a vision and strategy for their facilities and helps translate those strategies into action and measurable performance outcomes.

ABSTRACTRaymond J. Cole’s body of work, spanning sustainable design, system complexity and human agency, has encouraged researchers to reconceptualize the notions of comfort and building performance. However, methods for predicting energy use and assessing environmental performance have remained predominantly within a reductionist approach common to physics and engineering. The recognition that building performance is characterized by interactive adaptivity and co-evolution of the physical with the social has not been matched by the generation of new methods. Although social practice theories that articulate the socio-technical nature of the built environment have been increasingly appropriated to understand occupants’ role in performance, the challenge of studying buildings as complex socio-technical systems remains. This methodological paper discusses the application of the case study method (CSM) to the study of 10 retrofit projects selected from the Retrofit for the Future (RfF) Programme in UK between 2011 and 2012. Guided by Greene’s framework for methodological discourse, the epistemic regime is articulated under four headings: philosophical assumptions, investigative logics, guidelines for practice and contribution to system perspective. The discussion of these domains highlights the fecundity of CSM in providing a more nuanced understanding of the interaction between social and technical systems in performance.

Buildings account for nearly 40% of the global process and industrial greenhouse gas emissions, and within the building sector. And within the building sector, nearly 72% of the CO2 emissions are due operational carbon demand in the building sector. To meet the goals set out in the Paris Accord, we need to halve carbon emissions by the end of this decade: an aggressive and ambitious timeline. It has become an immediate and urgent need to design and construct buildings that are minimally intrusive in their impact on the environment and operate efficiently. Improvement of the building's energy performance must begin at the early design stage as the potential for sustainable intervention in the project's early phases is higher. The opportunity to improve building performance as the design progresses, is constantly reducing, while the cost of optimization is constantly increasing. One of the major design variables impacting the performance at the early phase is the building morphology/form and its associated variables. Sensible optioneering of the building form and its variables at the early stages of design - especially the conceptual design stage - can help improve the performance of the design early on in the design process at minimum cost. However, for effective early-phase design optimisation, there is a need to develop tools/methods that allow instantaneous evaluation of a large design sample space without much “lag” between the design and performance evaluation workflows. Also, in regular early phase sustainable design workflows, multiple stakeholders - like the designer, the performance analyst, etc. - are involved, which leads to a cognitive divide in the design process. For instance, while the architect designs the initial conceptual massing with a functional design intuition, the performance analysts suggest design interventions with a performance oriented design intuition. This cognitive divide leads to a dipartite design cognition in early phase design. The underlying premise of this research is to support the notion of blurring the dichotomy between conflicting design intuitions through exploring Human + Artificial Intelligence (AI) synergies and their underlying foundational technological requirements with respect to early phase design, to enable intuitive high performance design scenarios with a centralized design cognition where there are no other stakeholders other than the designer and the design environment. With the paradigm shift of other industries towards Machine Learning (ML) and AI, recent research advances in ML and the increasing availability of Big-data in the AEC industry have bolstered multivariate problems like building performance evaluation to a great extent. Design optioneering with instantaneous performance feedback through a ML method called Surrogate Modelling is an upcoming and promising methodology, which delivers feedback based on knowledge through available data, rather than simulation. Also, paradigms in Concurrent Human Machine Interaction (HMI) have explored avenues of augmenting the architectural design process through data-driven approaches. The type of data (input-output pairs) forming the basis for these paradigms depend on the target problem at hand. For early phase performance optimization - especially energy use optimization - Building Energy Model (BEM) forms the primary data. The US Department of Energy defines BEM as a versatile, multipurpose tool that is used in new building and retrofit design, code compliance, green certification, qualification for tax credits and utility incentives, and even real-time building control. BEM is also used in large-scale analyses to develop building energy-efficiency codes and inform policy decisions. A comprehensive BEM dataset that is large and accessible can support the growing interest in ML and HMI research in the high performance building design field. However, common techniques to acquire large BEM datasets like manual 3D energy modeling and simulation, 3D scanning, etc. are very tedious and time consuming. Also, existing datasets are usually incomplete, inconsistent and very difficult to access, owing to data privacy and open-access issues. And lastly, these datasets are fixed, static and cant be easily reproduced for different use case scenarios, nor scalable to adapt to needs. This research aims to tackle this problem by introducing a novel framework to generate custom problem-specific synthetic BEM dataset that generates a user defined amount of context specific 3D early phase building geometries and their associated BEM models, that is suitable for ML and HMI research. Synthetic datasets generated with this framework offer flexibility and customization in the generation process, making these datasets modular, reproducible and scalable. The framework uses the concepts of Generative Design, Geometry Manipulation, Simulation and Computational Automation to build the dataset. The dataset is qualified through the concepts of Preservationism and Sustainability, which are discussed in further sections of this article. The research identifies the importance and multifacetedness of the impact of building geometry on its performance, and demonstrates the application of generated synthetic BEM datasets by developing an enactive, conversational design environment that allows the designer to make real-time sustainable design decisions based on instantaneous machine feedback, for an intuitive, centralized design intuition.

To construct a sustainable and healthy building, it is necessary to build a system capable of efficiently analyzing, assessing and managing health performance data over the life cycle of a building. To fulfill such needs, health-friendly resources database, life cycle health-performance tree, health performance evaluation and health management concepts were suggested. Such studies focused on health performance. Selecting building materials based only on their health performance while ignoring cost is likely to lead to not only rapid construction cost increases, but also higher maintenance costs. For cost-benefit management, the health performance and cost management model was proposed. However, while the model can be used in decision-making, it is unable to suggest an optimization solution. To supplement this need, this study proposes a health performance and cost optimization model for sustainable healthy buildings. The model proposed here is composed of simulation and optimization modules for health performance and cost using the concepts mentioned above.

Sustainable High-rises

Building design is important for saving energy and reducing GHG emissions by applying passive solar heating and cooling design principles and using the right materials and appropriate design tools. This will make the home healthier and more comfortable. The design of energy efficient and sustainable buildings is critical for the future. A key aspect of any design is the realistic and accurate prediction of the performance of the building under a wide range of weather conditions. This paper examines the effect of different climate zones in Australia (which are comparable to the world's major climates) on the thermal performance of a complete building and recommended design techniques to suit each climate zone to enhance the overall thermal performance. To examine the effect of the location (different climates) on the overall thermal performance and how a good design in one location may not be suitable at another location, AccuRate will be used to assess the thermal performance for the exact module in different climates zones to allow a fair comparison to find the appropriate design for the climate where the building is located. Also, in this research, each climate zone design requirements and techniques were addressed for various climate variables (including: solar radiation, rainfall, wind speed and direction and humidity) to design sustainable building which save great amount of energy while sustaining occupants thermal comfort.