Building Management Research Articles

A scoping review and analysis of green construction research: a machine learning aided approach

PurposeThe increasing urgency to address climate change in construction has made green construction (GC) and sustainability critical topics for academia and industry professionals. However, the volume of literature in this field has made it impractical to rely solely on traditional systematic evidence mapping methodologies.Design/methodology/approachThis study employs machine learning (ML) techniques to analyze the extensive evidence-base on GC. Using both supervised and unsupervised ML, 5,462 relevant papers were filtered from 10,739 studies published from 2010 to 2022, retrieved from the Scopus and Web of Science databases.FindingsKey themes in GC encompass green building materials, construction techniques, assessment methodologies and management practices. GC assessment and techniques were prominent, while management requires more research. The results from prevalence of topics and heatmaps revealed important patterns and interconnections, emphasizing the prominent role of materials as major contributors to the construction sector. Consistency of the results with VOSviewer analysis further validated the findings, demonstrating the robustness of the review approach.Originality/valueUnlike other reviews focusing only on specific aspects of GC, use of ML techniques to review a large pool of literature provided a holistic understanding of the research landscape. It sets a precedent by demonstrating the effectiveness of ML techniques in addressing the challenge of analyzing a large body of literature. By showcasing the connections between various facets of GC and identifying research gaps, this research aids in guiding future initiatives in the field.

Strengths, weaknesses, opportunities and threats of demand response in district heating and cooling systems. From passive customers to valuable assets

Buildings can deliver short-term thermal energy storage by utilising the thermal capacity of the building construction and/or by activating the water tanks included in the heating/cooling installation. The flexibility potential of demand management using decentralized thermal energy storage has been quantified in many theoretical modelling studies, and it is considered an essential technology for an affordable energy transition. We have investigated the drivers and barriers to the adoption of demand management in buildings in district heating and cooling systems via a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis and presented 17 elements that shape the current and future application of this concept. The results indicate that the application of the DR concept has left the theoretical studies and moved towards real-life applications. Yet, there is a lack of feasible business models and regulatory frameworks supporting the large-scale application of the concept. Utilities and their customers do not fully understand the benefits of the DR concept; therefore they are reluctant to adopt it outside of the research projects where the test environment is fully controlled and with limited impact and timeline. Therefore, the regulatory framework must be adjusted to allow DHC operators to develop new business models and DR tariffs that will incentivise the customers to deliver flexibility to the system without compromising their comfort and everyday practices and increasing energy poverty.

A New Model for Building Energy Modeling and Management Using Predictive Analytics: Partitioned Hierarchical Multitask Regression (PHMR)

Buildings are major consumers of energy, accounting for a significant proportion of total energy use worldwide. This substantial energy consumption not only leads to increased operational costs but also contributes to environmental concerns such as greenhouse gas emissions. In the United States, building energy consumption accounts for about 40% of total energy use, highlighting the importance of efficient energy management. Therefore, accurate prediction of energy usage in buildings is crucial. However, accurate prediction of building energy consumption remains a challenge due to the intricate interaction of indoor and outdoor variables. This study introduces the Partitioned Hierarchical Multitask Regression (PHMR), an innovative model integrating recursive partition regression (RPR) with multitask learning (hierML). PHMR adeptly predicts building energy consumption by integrating both indoor factors, such as building design and operational variables, and outdoor environmental influences. Rigorous simulation studies illustrate PHMR’s efficacy. It outperforms traditional single-predictor regression models, achieving a 32.88% to 41.80% higher prediction accuracy, especially in scenarios with limited training data. This highlights PHMR’s robustness and adaptability. The practical application of PHMR in managing a modular house’s Heating, Ventilation, and Air Conditioning (HVAC) system in Spain resulted in a 37% improvement in prediction accuracy. This significant efficiency gain is evidenced by a high Pearson correlation coefficient (0.8) between PHMR’s predictions and actual energy consumption. PHMR not only offers precise predictions for energy consumption but also facilitates operational cost reductions, thereby enhancing sustainability in building energy management. Its application in a real-world setting demonstrates the model’s potential as a valuable tool for architects, engineers, and facility managers in designing and maintaining energy-efficient buildings. The model’s integration of comprehensive data analysis with domain-specific knowledge positions it as a crucial asset in advancing sustainable energy practices in the building sector.

Priority Needs for Facilities of Office Buildings in Thailand: A Copula-Based Ordinal Regression Model with Machine Learning Approach

In the rapidly evolving business landscape of Thailand, the design and facilities of office buildings play a crucial role in enhancing employee satisfaction and productivity. This study seeks to answer the question: “How can office building facilities be optimized to meet the diverse preferences of occupants in Thailand, thereby improving their satisfaction and productivity”? This study employs a copula-based ordinal regression model combined with machine learning techniques to investigate the determinants of facility preferences in office buildings in Thailand. By analyzing data from 372 office workers in Bangkok, we identify the factors influencing facility needs and preferences, and measure the correlation between these preferences. Our findings reveal that safety and security are the highest-rated amenities, indicating their importance in the workplace. The findings reveal distinct preferences across demographic groups: age negatively influences the demand for certain amenities like lounges, while higher education levels increase the preference for cafeteria services. Employees in smaller firms show a higher preference for lounges and fitness centers but lower for restaurants and cafeterias. Interestingly, the size of the enterprise does not significantly affect preferences for fundamental facilities like security and cleaning. The study also uncovers the significant role of gender and income in shaping preferences for certain facilities. These results suggest that while basic amenities are universally valued, luxury or leisure-oriented facilities are more appreciated in smaller, possibly more community-focused work environments. This study highlights the need for tailored facility management in office buildings, considering the diverse needs of different employee groups, which has significant implications for enhancing workplace satisfaction and productivity.

Dynamics and Dependencies in Regional Collaboration for Biodiversity Restoration: Reflections from the Netherlands.

Biodiversity restoration on a landscape level requires people with different backgrounds to connect and collaborate over an extended period of time. Hence, understanding how conservation and restoration goals are negotiated and achieved necessitates an understanding of the dynamics of the social fabric: the social networks and interactions that develop, underpin, and sustain collective action. This paper identifies patterns and factors that have contributed to constructive collaboration for biodiversity in the rural area of Ooijpolder-Groesbeek, which has been at the vanguard of nature and landscape development in the Netherlands. We conducted a historical analysis of the period between 1985 and 2022, based on a broad range of literature and interviews with key actors in the region. We provide a narrative account of the tipping points and the preceding processes that propelled the region to its current state. The emergence of these tipping points is analyzed through the lens of a conceptual framework on the dynamic interplay between practices, social interactions, events, and circumstances. Our findings reveal how an integrative landscape approach, the use of suitable boundary objects, and continuous network building and relation management across various levels have contributed to the success of the collective effort.

A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management

Given the climate change in recent decades and the ever-increasing energy consumption in the building sector, research is widely focused on the green revolution and ecological transition of buildings. In this regard, artificial intelligence can be a precious tool to simulate and optimize building energy performance, as shown by a plethora of recent studies. Accordingly, this paper provides a review of more than 70 articles from recent years, i.e., mostly from 2018 to 2023, about the applications of machine/deep learning (ML/DL) in forecasting the energy performance of buildings and their simulation/control/optimization. This review was conducted using the SCOPUS database with the keywords “buildings”, “energy”, “machine learning” and “deep learning” and by selecting recent papers addressing the following applications: energy design/retrofit optimization, prediction, control/management of heating/cooling systems and of renewable source systems, and/or fault detection. Notably, this paper discusses the main differences between ML and DL techniques, showing examples of their use in building energy simulation/control/optimization. The main aim is to group the most frequent ML/DL techniques used in the field of building energy performance, highlighting the potentiality and limitations of each one, both fundamental aspects for future studies. The ML approaches considered are decision trees/random forest, naive Bayes, support vector machines, the Kriging method and artificial neural networks. The DL techniques investigated are convolutional and recursive neural networks, long short-term memory and gated recurrent units. Firstly, various ML/DL techniques are explained and divided based on their methodology. Secondly, grouping by the aforementioned applications occurs. It emerges that ML is mostly used in energy efficiency issues while DL in the management of renewable source systems.

Green, recyclable and high latent heat form-stable phase change composites supported by cellulose nanofibers for thermal energy management

Efficiently addressing the challenge of leakage is crucial in the advancement of solid-liquid phase change thermal storage composite materials; however, numerous existing preparation methods often entail complexity and high energy consumption. Herein, a straightforward blending approach was adopted to fabricate stable phase change nanocomposites capitalizing on the interaction between TEMPO-oxidized cellulose nanofibers (TOCNF) and polyethylene glycol (PEG) molecules. By adjusting the ratio of TOCNF to PEG and the molecular weights of PEG, TOCNF/PEG phase change composites (TPCC) with customizable phase transition temperature (40.3–59.1 °C) and high phase transition latent heat (126.3–172.1 J/g) were obtained. The TPCC of high-loaded PEG (80–95 wt%) ensured a leakage rate of less than 1.7 wt% after 100 heating-cooling cycles. Moreover, TPCC exhibits excellent optical properties with a transmittance of over 90 % at room temperature and up to 96 % after heating. The thermal response analysis of TPCC demonstrates exceptional thermal-induced flexibility and good thermal stability, as well as recyclability and reshaping ability. This study may inspire others to design bio-based phase change composites with potential applications in thermal energy storage and management of smart-energy buildings, photothermal response devices, and waste heat-generating electronics.

Real-world implementation of a cloud-based MPC for HVAC control in educational buildings

In their quest to enhance energy efficiency and carbon footprint management, many enterprises are turning their attention towards optimizing their facilities through amplified data monitoring and innovative control methods. In this context, this research highlights a practical solution in a commercial building located in the United States. It explains the creation, implementation, and costs of a universally applicable model predictive control (MPC) framework for the building’s heating, ventilation, and air conditioning (HVAC) system. The paper elaborates on both the hardware and software used to accumulate pertinent data and the formulation of the MPC system. This approach leverages cloud-based microservices and can be seamlessly integrated into current building management systems. In this regard, this paper presents three innovative strategies: Proportional–integral (PI)+Preheating, MPC, and occupancy-based control. These scenarios were designed to improve energy efficiency and thermal comfort significantly by reducing temperature fluctuations and adhering more closely to the setpoint temperature. The implementation of these strategies resulted in substantial energy savings, with reductions in energy consumption of 12.83%, 19.21%, and 14.98%, respectively.

Infrastructure building and post-Cyclone management in Odisha: A study of people’s perception in one select village in Nilagiri Block

Infrastructure building and post-Cyclone management in Odisha: A study of people’s perception in one select village in Nilagiri Block

Low-voltage electrochromic behaviour of polymer-stabilised liquid crystal with considerable broadband reflection

ABSTRACT In this work, a novel method was proposed to achieve electric-induced colouration of polymer-stabilised liquid crystal (PSLC) with considerable broadband reflection. The formation of broadband reflection was due to the introduction of the UV absorber that induced a light intensity gradient within the liquid crystal system and promoted the non-uniform distribution of the pitch. Experiments were performed to optimise the components and polymerisation conditions, allowing the bandwidth to be broadened from 252 nm to 906 nm. The texture of PSLC was characterised using POM, revealing that the planar texture was not disrupted. Thereafter, the addition of ionic liquid as supporting electrolyte enabled the PSLC to be switched from colourless to coloured at DC voltage as low as 1.7 V and the colouration behaviour can be independently modulated by changing the voltage level and the duration. Meaningfully, after the DC electric field was removed, the colour of the PSLC can also disappear gradually. In addition, the as-prepared electrochromic PSLC presented enhanced UV shielding, IR thermal control efficacy and good colouration performance on flexible substrates, which can be applied to low-energy smart glass and thermal management of buildings. This study provided a promising approach for the development of smart optical devices.

Utilizing Hybrid Sine Cosine Shuffled Frog Leaping Algorithm for Optimal Energy Management in the Residential building with Renewable Energy Resources and Corresponding Uncertainties

Utilizing Hybrid Sine Cosine Shuffled Frog Leaping Algorithm for Optimal Energy Management in the Residential building with Renewable Energy Resources and Corresponding Uncertainties

Enhancing the performance of sustainable energy management of buildings in smart cities

Energy utilization has been the most influential parameter in recent decades, especially in the smart city model. The energy management system has been a more attractive research problem due to its utility, ability, and applications. This paper has an objective that the article discusses innovative energy management methods for sustainability and highlights the potential for integrated smart energy sources. The discussion also touches on the understanding of energy management and production, various storage systems, and their potential future applications. This paper explores challenges in sustainable smart energy management, focusing on methodologies like smart energy systems, PV calculations, electric grid models, and energy management strategies in smart cities. The passive infrared receiver (PIR) sensor has been used in real-time energy management systems to integrate these methodologies into the city's infrastructure. The energy management design aims to coordinate electrical appliances such as fans and lights to minimize energy consumption. The article proposes new energy management and security techniques based on data sources to enhance city intelligence, adaptability, and sustainability by reducing human involvement in controlling electrical appliances in residential buildings. The proposed design and development system optimizes energy utilization more efficiently and effectively than conventional systems, meeting real-time energy management objectives.

Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches.

Buildings are rapidly becoming more digitized, largely due to developments in the internet of things (IoT). This provides both opportunities and challenges. One of the central challenges in the process of digitizing buildings is the ability to monitor these buildings' status effectively. This monitoring is essential for services that rely on information about the presence and activities of individuals within different areas of these buildings. Occupancy information (including people counting, occupancy detection, location tracking, and activity detection) plays a vital role in the management of smart buildings. In this article, we primarily focus on the use of passive infrared (PIR) sensors for gathering occupancy information. PIR sensors are among the most widely used sensors for this purpose due to their consideration of privacy concerns, cost-effectiveness, and low processing complexity compared to other sensors. Despite numerous literature reviews in the field of occupancy information, there is currently no literature review dedicated to occupancy information derived specifically from PIR sensors. Therefore, this review analyzes articles that specifically explore the application of PIR sensors for obtaining occupancy information. It provides a comprehensive literature review of PIR sensor technology from 2015 to 2023, focusing on applications in people counting, activity detection, and localization (tracking and location). It consolidates findings from articles that have explored and enhanced the capabilities of PIR sensors in these interconnected domains. This review thoroughly examines the application of various techniques, machine learning algorithms, and configurations for PIR sensors in indoor building environments, emphasizing not only the data processing aspects but also their advantages, limitations, and efficacy in producing accurate occupancy information. These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects. The article seeks to offer a thorough analysis of the present state and potential future advancements of PIR sensor technology in efficiently monitoring and understanding occupancy information by classifying and analyzing improvements in these domains.

The effects of three environmental factors on building evacuation time

Building fires can be considered a risk to the health and safety of occupants. Environmental factors in building fires might affect the speed of an evacuation. Therefore, in this study participants (N = 153) were tested in an experimental design for the effects of (1) a fire alarm, (2) darkness and (3) the use of emergency exit signs on building evacuation time. In addition, the effects of age and gender on evacuation time were investigated. The main results indicate that the combination of a fire alarm, darkness and not illuminated emergency exit signs had a significant negative influence on evacuation time, namely an increase in evacuation time of 26.6% respectively 28.1%. Another important finding is that age had a significant negative effect on evacuation time. The increase in evacuation time was at least 30.4% for participants aged 56 years or older compared to participants aged 18–25 years. For gender no significant effect was found. Building and safety managers can use these results by including longer evacuation time considerations – based on darkness and older age – in their evacuation plans. Future research should focus further on investigating the effects of personal and psychological characteristics on evacuation behaviour and evacuation time.

Development of novel form-stable PCM-biochar composites and detailed characterization of their morphological, chemical and thermal properties

Storing heat with phase change materials has the potential for several modern-day applications such as building thermal regulation, isothermal solar drying, electronic cooling, etc. However, to suitably incorporate latent heat energy storage systems, the challenges of leakage and low thermal conductivity of these materials need to be addressed. In this direction, biochar derived from abundantly available biomass feedstocks has been explored to develop form-stable, environmentally compatible energy storage materials. Biochar has been derived from wheat straw and softwood and has been analysed for their surface structure and utilised in the preparation of form-stable phase change material. The biochar derived from softwood was found to have a superior porous structure with a surface area of 441 m2/g compared to wheat straw. Biochar and phase change material Rubitherm 28 (RT28) were blended at various wt% and tested for leakage. The composite with 50 wt% of softwood biochar and RT28 had the lowest leakage rate. The developed green phase change-bio composite with minimum leakage has been thoroughly evaluated for its chemical and thermal properties. The latent heat of fusion has been found to be 228.6 J/g, 132.3 J/g and 51.6 J/g, for the RT28, softwood biochar composite, and wheat straw biochar composite respectively. Thermal conductivity was also higher for the composites than that of RT28. The thermal cycling reliability is suitable for thermal storage applications as there is a minimal alteration in the thermal properties observed after 200 cycles. The melting enthalpy of the new and aged composites after 200 cycles has been found to be 136.6 J/g and 132.42 J/g, respectively. The composite with minimal leakage and high thermal conductivity can potentially revolutionise the area of low temperature latent heat storage. The properties of the form-stable phase change-bio composite synthesised in this work make it suitable for application in the field of thermal management of photovoltaic modules, electronic devices, and buildings.

Blockchain technology applications in maintaining heritage buildings

Preservation of cultural heritage in the face of human and natural disasters, as well as improper maintenance practices, is a crucial endeavor that must be undertaken throughout the lifespan of heritage buildings. In order to maximize the value derived from these assets and strike a harmonious balance between accurate documentation and performance optimization, asset management strategies have been employed. In recent years, the advent of innovative methodologies such as Building Information Modeling (BIM) and laser scanning has opened new possibilities for their application in various contexts. However, successful implementation of these methodologies faces a range of challenges, including limited knowledge, inexperienced teams, and reluctance to embrace change on the part of administrators. To address these obstacles and enhance asset management practices, the integration of BIM, Chatbot, and Blockchain methodology has emerged as a viable solution. This methodology not only facilitates the improvement of data security and user data capture but also streamlines the process of updating BIM models. In this research, we propose a data-driven Chatbot framework based on Blockchain, specifically designed to manage maintenance activities through the utilization of BIM. The proposed framework serves as a valuable tool for government authorities and asset managers seeking to adopt BIM for the preservation of heritage buildings. Comprising three key components – the BIM model, the Blockchain model, and the Chatbot model – the framework offers comprehensive support. The BIM model encompasses all relevant information pertaining to the management of the heritage building, while the Chatbot model replicates human interaction through text-based communication. Users can access up-to-date BIM data by posing predefined questions to the Chatbot. On the other hand, the Blockchain model serves as a secure and immutable record-keeping system, validating and verifying user updates, thereby fostering trust among stakeholders. This proposed model empowers asset stakeholders to request and update asset information via a trusted and reliable channel. To showcase the capabilities of the framework, we present a real-world case study centered on the Omar Toson Palace in Cairo, Egypt. Through this case study, we demonstrate the effectiveness and potential of our proposed framework in meeting the challenges associated with heritage building preservation.

Systematic Review of Green Building Trends in South Korea from 2001 to 2023 Using Research Topic Words

In 2020, South Korea implemented the Green New Deal, and in 2022, it officially proposed the goal of achieving carbon neutrality by 2050. To effectively promote the development of green buildings in South Korea, it is crucial to analyze their evolutionary trends and processes. Therefore, the purpose of this study was to summarize and predict the development trajectory of green buildings in South Korea by studying the research and development trends in this field. Accordingly, this study used content analysis as a research method to analyze 89 research papers and their subject words published from 2001 to 2023. Further in-depth analysis was conducted on five sub-dimensions: technical, economic, legal, systemic, and the popularization and development of green buildings. Since 2000, research has mainly focused on the improvement and development of evaluation index systems and the exploration of energy-saving technologies. In terms of the research and development of green building materials, there is a problem of slow development. In terms of green building management, there is a lack of specific management methods or systematic research. Economic aspects should be studied from the consumers’ perspective. Hence, opportunities and challenges coexist and require joint efforts from the government, academia, enterprises, and other parties.

Integration of Building Information Modeling (BIM) with Transportation and Facilities: Recent Applications and Future Perspectives

At present, increased modes of transport have facilitated daily life. Building information modeling (BIM) integration has become a key strategy to foster efficiency, collaboration, and sustainability in the fields of buildings, transport, and facilities. Currently, there is a scarcity of comprehensive examinations of the performance of integrated BIM with transport facilities and applications to help identify potential prospects for sustainable development. Hence, this research paper attempts to scrutinize the integration of BIM, transportation, and facilities (T and Fs) by highlighting future directions and trends, revealing the existing status and hotspots of research in the field, and clarifying the developmental pulse of research as well as emerging areas in the future. A quantitative research technique is utilized in this study, using VOSviewer and CiteSpace software, and the Web of Science Core Collection (WoSCC) database. The study findings suggest that the hot keywords for the integration of BIM and T and Fs are predominantly concentrated on construction, framework, system, design, and management. In addition, over the previous 34 years (years 1989 to 2023), the applications of point cloud, digital twin, and life cycle assessment have been the current hot topics, and these emerging technologies can offer more innovative breakthroughs for the future. Furthermore, the integration of BIM and T and Fs represents an important trend that is essential for improving the efficiency, sustainability, and intelligence of buildings and infrastructure, from which the three domains can create synergies that contribute to the better planning, construction, and management of building and infrastructure projects.

Indoor comfort management and energy consumption in Arctic buildings: Occupant perceptions in an indigenous community of Canada

Arctic and subarctic communities currently face major and unique energy and housing challenges. However, the perceptions, needs, and adaptive strategies of occupants related to energy consumption and comfort in their dwellings are not well known. In this study, 16 semi-structured interviews were conducted in the Inuit community of Quaqtaq (Nunavik, Quebec, Canada) to investigate occupants’ experiences, observations, and perceptions on these topics. The topics covered comfort, window opening, use of curtains, and usage of thermostats, water, and lighting. Furthermore, awareness of energy consumption and renewable technologies was investigated, and occupants shared their vision of the ideal home. It was found that windows and thermostats are often used simultaneously to regulate temperature and that non-uniform temperature distribution and cold floors were often noted by occupants. Due to the limited amount of water available, coordination with neighbors was needed for activities requiring water. Awareness and interest in energy savings and renewable energy varied significantly among participants. Participants mentioned that their ideal home would have better insulation, larger water tanks, and more storage space. These results help to understand occupant behavior, energy consumption patterns, and well-being in houses of northern communities and can guide the development of future housing units.

Enhancing seismic assessment and risk management of buildings: A neural network-based rapid visual screening method development

Some of the existing buildings are designed based on lower design standards or even without considering seismic design standards. Recent earthquakes have further highlighted the vulnerability of these buildings when subjected to severe seismic activity. Consequently, it has become imperative to conduct seismic vulnerability assessments of the existing building stock. Therefore, the assessment of the existing building stock is required through the utilization of Rapid Visual Screening (RVS) methods. However, the existing conventional RVS methods used in seismic building assessments have shown limited accuracy. Furthermore, because these methods were developed based on expert opinions and/or due to access limitations to detailed assessment-based generated data used for their development, further enhancing them is challenging. To address these limitations, a new RVS method, which leverages Neural Networks (NN) and building-specific parameters, for reinforced concrete, adobe mud, bamboo, brick, stone, and timber buildings has been proposed in this study. Unlike conventional methods that rely on site seismicity class, the developed data-driven approach incorporates building-specific parameters such as the fundamental structural period and building spectral acceleration. The developed RVS method is specifically tailored to analyze diverse types of buildings in regions with varying seismicity risks, all in preparation for an impending earthquake. In this study, the developed RVS method demonstrated a promising 68% test accuracy, effectively representing the building performance against earthquakes. These findings illustrate the potential of the developed NN based RVS method in assessing existing buildings, thereby mitigating potential loss of life and property during imminent earthquake and alleviating the associated economic burden. Furthermore, this study introduces a new RVS method that can pave the way for future advancements in the field of seismic vulnerability assessment of existing buildings.