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Earthquakes could be a hazard to human lives and the economy, leading to catastrophic disaster unless proper preparation is not made for buildings and infrastructures. To prevent catastrophes from earthquakes, new engineering technologies and seismic evaluation methods have been being developed [[1]]. This Special Issue also included three important topics on seismic performance evaluation of building structures: (1) novel seismic performance evaluation methods, (2) advanced numerical model and analysis methods to conduct seismic performance evaluation, (3) seismic hazard estimation methods, and (4) seismic loss estimation. [Extracted from the article]

The global navigation satellite system radio occultation (GNSS-RO) is an important means of space-based meteorological observation. It is necessary to test the Global Navigation Satellite System Occultation signal receiver on the ground before the deployment of space-based occultation detection systems. The current approach of testing the GNSS signal receiver on the ground is mainly the mountaintop-based testing approach, which has problems such as high cost and large simulation error. In order to overcome the limitations of the mountaintop-based test approach, this paper proposes an accurate, repeatable, and controllable GNSS atmospheric occultation simulation system and builds a load performance evaluation approach based on the ground-based GNSS atmospheric occultation simulation system on the basis of it. The GNSS atmospheric occultation simulation system consists of the visualization and interaction module, the GNSS-RO simulation signal generation module, the GNSS-RO simulator module, the GNSS-RO signal receiver module, and the GNSS-RO inversion and evaluation module, combined with the preset atmospheric model to generate GNSS-RO simulation signals with a high degree of simulation, and comparing the atmospheric parameters of the inversion performance of the GNSS-RO signal receiver with the parameters of the preset atmospheric model to obtain the error data. The overall performance of the GNSS-RO signal receiver can be evaluated based on the error information. The novel approach to evaluate the GNSS-RO signal receiver performance proposed in this paper is validated by using the FY-3E (FengYun-3E) receiver qualification parts that have been verified in orbit, and the results confirm that the approach can meet the requirements of the GNSS-RO receiver performance test. This study shows that the novel approach to evaluate the GNSS-RO signal receiver performance in terms of the ground-based atmospheric occultation simulation system can efficiently and accurately be used to carry out the receiver test and provides an effective solution for the ground-based test of GNSS-RO signal receivers. [ABSTRACT FROM AUTHOR]

Assessment methodologies such as BREEAM and LEED allocate points based on prescribed interventions that promote design features or strategies considered to be more sustainable than others. A focus on accumulating numerical scores, however, often fails to address pertinent contextual issues, particularly within developing countries. This paper examines the use of four assessment systems in Kenya—two international systems, LEED-US and Green Star SA-Kenya; and two locally developed systems, Green Mark Kenya and the Safari Green Building Index. The paper compares the relative weighting of different categories under each system, and assesses their appropriateness to a Kenyan context, with reference to the suitability of active technology versus passive design approaches. The paper examines selected examples of 'green' buildings in Nairobi, reflecting on the influence of different methods of assessment on the adopted design approaches. The paper argues that international rating systems, such as LEED, often focus on a Western construct of sustainability featuring a systematic bias towards global rather than local perspectives, with an emphasis on physical environmental factors. In pursuit of objectivity, the measurement of non-contextual parameters untailored to local circumstances (e.g., energy performance) is prioritised at the expense of those contingent on local conditions or climate. [ABSTRACT FROM AUTHOR]

Energy-efficient and sustainable building management has always been a key concern surrounding buildings. The rise of environmental and social concern in today's world has brought more attention to the issue of sustainable and smart building management. This paper aims to review the state-of-the-art research and performance on building management that aims to make more sustainable and energy-efficient decisions. This paper classifies building management based on technologies utilized for management and different aspects of management that should be considered when regarding the larger picture of "sustainability". Additionally, while keeping in mind that long-term sustainability cannot be achieved through energy management alone, this research investigates previous works that also mention diverse aspects that must be taken into consideration when creating a truly successful smart building environment: costs, occupant comfort, and security. Of course, each field deserves an extensive analysis, but the purpose of this review paper is to deliver current research that has brought attention to the rapidly shifting and developing field of smart buildings to provide a macro-level holistic viewpoint on how smart buildings and homes should be approached from a sustainability viewpoint. [ABSTRACT FROM AUTHOR]

China is in the transition period of urbanization, and the imbalance of land use generated during the original rapid development of megacities still exists, resulting in the preservation of a large number of urban villages in the peripheral areas of cities. Unlike formal housing, these villages are heavily influenced by the confrontation and interweaving between top-down policies of local governments and bottom-up spontaneous construction by local villagers, resulting in a diverse and complex status quo. At the same time, self-built housing in urban villages has become a gathering place for a large number of low-income migrants due to its relatively low rental prices. In terms of the research of urban villages on the current situation of housing, the real needs of users and housing performance are mostly subjective judgments and conclusions. Therefore, the entry point of this paper is the leased space and the leasers, taking Laojuntang Village in Chaoyang District of Beijing as the research object and analyzing its current problems and the real needs of users. This paper adopts an integrated approach of fuzzy Kano model evaluation and building performance simulation. After analyzing the data and discussing the influencing factors, this paper identifies the specific problems and user needs that exist in leased spaces in Laojuntang Village: (1) The architectural performance of leased space is a Must-be Quality, which is the bottom-line requirement and must be improved. (2) The quality of the basic facilities and equipment of the leased space is mostly a One-dimensional Quality, Attractive Quality, and the degree of importance is lower than that of the building performance. (3) The external activity facilities of the leased space are mostly undifferentiated needs, for which the local users are not particularly prominent. (4) The scale and plot ratio of local housing is relatively high, and users are dissatisfied and complaining, which falls under the category of Reverse Quality. [ABSTRACT FROM AUTHOR]

Due to climate change, Egypt has recently suffered from recurring electricity crises. Despite efforts made to increase electricity production in Egypt, recently, in the summer months, the energy demand has increased at unprecedented rates, especially in the housing sector. Therefore, the government and homeowners should work together to improve the energy performance of residential buildings. This paper aimed to develop a decision-making tool that helps homeowners choose optimal energy retrofit measures that suit their priorities. The study began with the data-collection and case study selection. Then, the thermal evaluation of the base case for dwellings in the case study was conducted through simulation runs using the DesignBuilder v7.1 software. Then, the optimal envelope energy retrofitting measures were determined, followed by a retrofitting-measure scenario simulation process. Then, the payback periods were calculated for all scenarios, and the tool database was developed using an Excel spreadsheet. Finally, the user interface for envelope energy retrofitting measures for gated communities (EERMGCs) tool was designed by Visual Basic for Applications. EERMGCs, the tool developed in this paper, is a simple, multi-objective and interactive tool that provides the optimal envelope retrofit measures according to user priorities, either a specific budget, the shortest payback period, the lowest possible costs, or the highest energy saving rate. The outcome of this research is developing a framework that can be considered a basis for developing decision-making tools for gated community housing in Egypt. [ABSTRACT FROM AUTHOR]

Recent destructive earthquakes in Turkey (2023) with devastating effects have reawakened the discussion about seismic activity in Brazil and the vulnerability of the Brazilian buildings. Despite the lower magnitudes, national records should not be disregarded, as earthquakes with a magnitude below five may still cause loss of life and property damage. The assessment of existing RC buildings allows us to understand the performance of a structure during an earthquake and the adoption of mitigating measures. This paper preliminarily investigates the seismic vulnerability of the Metropolitan Cathedral in Brasilia, which was recognized as a World Heritage Site by ONU in 1987. The Rapid Visual Screening (RVS) procedure has been adopted for qualitative vulnerability assessment through the guidelines of the Japan Building Disaster Prevention Association at the procedure updated in accordance with the Brazilian reality. This research examined the structure under horizontal acceleration defined by national seismic code, and various scenarios are assumed as time aging, structural irregularities, and the level of importance of the structure. In this study, the Monte Carlo simulation is carried out to evaluate the level of safety. A performed analysis has shown that the examined building possesses the seismic capacity required against the expected earthquake motions. Even with reliability analysis, the structure achieves an acceptable level of safety. The results highlight the importance of a probabilistic approach to make reliable decisions on the conservation and rehabilitation of architectural heritage buildings. [ABSTRACT FROM AUTHOR]

An innovative strategy for organizations to obtain value from their large datasets, allowing them to guide future strategic actions and improve their initiatives, is the use of machine learning algorithms. This has led to a growing and rapid application of various machine learning algorithms with a predominant focus on building and improving the performance of these models. However, this data-centric approach ignores the fact that data quality is crucial for building robust and accurate models. Several dataset issues, such as class imbalance, high dimensionality, and class overlapping, affect data quality, introducing bias to machine learning models. Therefore, adopting a data-centric approach is essential to constructing better datasets and producing effective models. Besides data issues, Big Data imposes new challenges, such as the scalability of algorithms. This paper proposes a scalable hybrid approach to jointly addressing class imbalance, high dimensionality, and class overlapping in Big Data domains. The proposal is based on well-known data-level solutions whose main operation is calculating the nearest neighbor using the Euclidean distance as a similarity metric. However, these strategies may lose their effectiveness on datasets with high dimensionality. Hence, the data quality is achieved by combining a data transformation approach using fractional norms and SMOTE to obtain a balanced and reduced dataset. Experiments carried out on nine two-class imbalanced and high-dimensional large datasets showed that our scalable methodology implemented in Spark outperforms the traditional approach. [ABSTRACT FROM AUTHOR]

This research paper analytically evaluates the project control practice levels used by the building construction companies within Kampala, Uganda. The research also assesses the impact of project control practices on the productivity of companies. The research was performed to ascertain the current control practices among 160 respondents from various construction companies registered with the Uganda Registration Services Bureau. This research used amalgamation from multiple studies in the literature to obtain the variables. This research adopts 34 standard control practices from 4 vital project control duties: planning, monitoring, analyzing, and reporting. These project control tasks were organized using mean response ratings grounded in their relevance to the construction companies. This process authorized researchers to prioritize tasks based on their perceived importance or relevance to the construction companies involved. Results showed that evaluating performance with the use of S-curves (4.32), timely access to information and encouragement (4.55), report representation using quantitative tools 4.75, and cost value comparison application during analysis (4.76) were rated least among the control practices. On the other hand, the top project control practices included formulation of the project schedule (8.88), project feasibility validation (8.86), budgeting for each activity (8.84), key project route definition (8.81), team awareness of the budget (8.77), setting realistic targets for projects (8.50), and consultation from subcontractors (8.74). From the results obtained by the sample respondents specified, it can be concluded that planning is the most vital project control task practiced in the building construction industry in Uganda. In addition, this research ascertained a substantial relationship between project control practices and the performance of building construction companies. Accordingly, this research recommends that project control practices be effectively observed by both contracting and consulting companies to enhance their overall performance and governance. [ABSTRACT FROM AUTHOR]

The impact of global climate change on the built environment emphasizes the need for sustainable development goals (SDGs) using technological solutions, such as the Internet of Things (IoT). The significance of innovative building assessment (BA) tools plays a pivotal role in bridging the existing gap between the theoretical and actual operational performance of buildings. The main research question is how can a new generation of BA tools leverage the IoT to optimize occupant well-being and achieve SDGs' targets. This article delves into the pivotal role played by the IoT and occupant-centric concepts in advancing sustainability initiatives and facilitating the achievement of SDGs. The novelty of this paper lies in its exploration of the current state of IoT integration as a strategic imperative for SDGs' achievement and climate change mitigation. Consequently, a paradigm shift is evident in this work, showcasing a comprehensive comparison between conventional and IoT occupant-centric BA tools and introducing a correlation study between IoT occupant-centric systems and future SDGs' targets. Lastly, current gaps and valuable insights into future research possibilities are offered. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of the potential benefits and feasibility of integrating Building Energy Management Systems (BEMSs) with Building Information Modeling (BIM) in, but not limited to, the construction and building management sectors. By examining advantages, challenges, and real-world case studies, this study offers valuable insights into the impact of BEMS-BIM integration on building operations. The research methodology includes a literature review and bibliometric analysis to understand the subject domain and identify prevalent keywords. Additionally, case studies demonstrate the effectiveness of BEMS-BIM integration in real-world scenarios. This study investigates the possibilities and challenges of BIM to the BEMS methodology for energy-efficient industrial buildings, emphasizing the importance of addressing uncertainties and enhancing software interoperability. This research highlights the potential of BEMS-BIM integration to revolutionize building performance, enhance sustainability, and contribute to a greener and more efficient future for the construction and building management industries. [ABSTRACT FROM AUTHOR]

Energy consumption plays an important role in contemporary economies. Its significance extends beyond utilitarian value, impacting economic robustness, environmental protection, and residents' well-being. The escalating global energy requisites necessitate efficient energy utilization and a shift towards renewable sources to address climate change and strengthen energy independence. Developing accurate predictive models to forecast long-term energy costs and savings remains a complex problem. This paper aims to provide a methodology to identify the influence of building energy performance on real estate market efficiency, focusing on property maintenance costs. Real estate plays a crucial role in human life, serving both as a fundamental need and as a vehicle for achieving personal aspirations and secure financial investments, particularly during times of economic and social instability. Through interdisciplinary methodological architecture, this study addresses three key issues: the impact of rising energy costs on market efficiency, the responsiveness of the real estate market to energy price fluctuations, and the significance of property maintenance costs on market value. The research approach includes creating and applying AI algorithms capable of evaluating extensive datasets pertaining to real estate features. Utilizing machine learning methods, the algorithm determines the importance of energy efficiency measures as well as various other inherent and external attributes of properties. The suggested methodology provides a novel approach to improve the effectiveness of market efficiency analysis. [ABSTRACT FROM AUTHOR]

During the design and planning stage of a district energy system, the prediction of the cooling and heating loads is an important step. The accurate estimate of the load pattern can provide a basis for the configuration and optimization of the system. To meet the demand in practical application, this paper proposes a fast load prediction method for district energy systems based on a presimulated forward modelling database and KNN (K-nearest neighbor) algorithm and develops it into a practical tool. Owing to the absence of some design parameters at the planning stage, scenario analysis is also used to determine some input conditions for load prediction. In this paper, the scenarios cover three types of building: office, shopping mall and hotel. To test the performance of this new method, we randomly selected 15 virtual buildings (5 buildings for each type) with different design parameters and took their detailed BPS (building performance simulation) model as a benchmark to assess the prediction results of the new method. The index "ratio of the hours with effective prediction" is defined as the ratio of the hours whose relative error of hourly load prediction is less than 15% to the hours whose load is not 0 in the whole year, and the test result shows that this index is not less than 0.9 (90%) for the predicted cooling load of all 45 test cases and the predicted heating load of 25 of the 45 cases. As a research achievement with practical value, this paper accomplishes the programming work of the tool and makes it into a software. The application of this software in the actual project of district energy system is also presented. The performance of the new load prediction tool was compared with the traditional approach commonly used in engineering—the load estimation based on reference building models—and the result shows that the fast load estimate tool can provide the same level of prediction accuracy as traditional simulation methods. [ABSTRACT FROM AUTHOR]

The proper application of machine learning and genetic algorithms in the early stage of library design can obtain better all-around building performance. The all-around performance of the library, such as indoor temperature, solar radiation, indoor lighting, etc., must be fully considered in the initial design stage. Aiming at building performance optimization and based on the method of "generative design", this paper constructs the library's comprehensive performance evaluation workflow and rapid prediction combined with the LightGBM algorithm. A library in a cold region of China is taken as the research object to verify its application. In this study, 5000 scheme samples generated in the iterative genetic optimization process were taken as data sets. The LightGBM algorithm was used to classify and predict design schemes, with a precision of 0.78, recall rate of 0.93, and F1-Score of 0.851. This method can help architects to fully exploit the optimization potential of the building's all-around performance in the initial stage of library design and ensure the timely interaction and feedback between design decisions and performance evaluation. [ABSTRACT FROM AUTHOR]

With limited investment costs, how to fully utilize the carbon-reduction capacity of a campus in terms of buildings, equipment, and energy is an important issue when realizing the low-carbon retrofit of office parks. To this end, this paper establishes a mathematical optimization model for the decarbonization-based retrofit of existing office parks, based on the genetic algorithm, taking into account the relationship between cost, energy-consumption, and carbon-emissions, and taking the maximum carbon reduction of the park over its whole life as the optimization goal. The validity of the model was verified in conjunction with a case study of an office park in Nanchang, China. The case study shows that, compared with current typical parks, the carbon reduction through an office park's decarbonization retrofit has a non-linear correlation with the investment cost, and when the total investment cost of the park is above CNY 60 million, the increase in carbon reduction with the increase in the investment cost is gradually weakened, and the park achieves the maximum carbon reduction of 236,087 t when the investment cost reaches CNY 103 million. Under the current technical and economic conditions, the investment-cost–carbon-reduction benefits of different carbon-reduction technologies are different, the carbon-reduction benefit of increasing renewable energy utilization is the best, and the carbon-reduction benefit of upgrading the energy efficiency of the park's supply-and-use system is lower than that of renewable energy utilization, but better than that of upgrading the performance of the building envelope system. In addition, the configuration of the parameters of the same low-carbon technology in different forms of buildings varies significantly, due to differences in the building form and daily use. The model established in this paper is able to give a comprehensive optimized building–equipment–energy configuration plan for existing office parks, when maximizing carbon reduction under different investment costs, which guides the park's decarbonization retrofit. [ABSTRACT FROM AUTHOR]

Under the action of an earthquake, the adjacent building structure will have a large displacement and even cause damage to the structure. Structure displacement can be suppressed by adding a tuned viscous mass damper (TVMD) and negative stiffness amplifier damper (NSAD) between adjacent structures. This paper deduces the motion equation of adjacent building structures under earthquake action. The transfer function of the vibration control system is obtained using Laplace transform. The parameter optimization analysis of the structural vibration control system is carried out through H 2 norm theory. The vibration control performance of the TVMD vibration control system and NSAD vibration control system is studied by taking two adjacent building structures composed of single freedom building structures as an example. The results show that the two vibration damping devices proposed in this paper can play the role of vibration control. The influence of the same parameters on the two structures is not completely consistent. Under seismic excitation, the NSAD control system has better displacement control ability than the TVMD control system. [ABSTRACT FROM AUTHOR]

Under the effect of strong earthquakes, collisions or excessive inter-story displacements may occur between adjacent building structures to the extent that the building structure is damaged. The traditional seismic measures for these structures can no longer meet the needs in practical engineering. In this paper, we propose the application of parallel and serial TID-based control systems in adjacent buildings as an example of a single-story adjacent building, and use it to form a new adjacent building seismic reduction structure. In this paper, the dynamic characteristics and design parameter optimization of the vibration control system are investigated by means of the Monte Carlo pattern search method and H2 norm theory. The results show that the introduction of serial and parallel TID in adjacent building structures can effectively improve the seismic resistance of adjacent buildings. The problem of vibration amplification caused by resonance is obviously improved, which is especially evident in the adjacent building structure vibration control system based on parallel TID. The vibration control system of adjacent building structures based on parallel TID is more robust. When optimizing the right building, the damping requirement of the TID decreases for the vibration control system based on parallel TID as the adjacent building mass ratio increases, while the damping requirement of the TID increases for the vibration control system based on serial TID. In both vibration control systems, the difference in the optimal inertial mass ratio is small. In practice, a moderate increase in the difference between adjacent building masses can have a positive effect on the vibration control performance of the systems. The main contribution of this paper is to fill the research gap in parallel and serial TID applications for adjacent building vibration reduction. [ABSTRACT FROM AUTHOR]

Intelligence in Industry 4.0 has led to the development of smart buildings with various control systems for data collection, efficient optimization, and fault detection and diagnosis (FDD). However, buildings, especially with regard to heating, ventilation, and air conditioning (HVAC) systems, are responsible for significant global energy consumption. Digital Twin (DT) technology offers a sustainable solution for facility management. This study comprehensively reviews DT performance evaluation in building life cycle and predictive maintenance. 200 relevant papers were selected using a systematic methodology from Scopus, Web of Science, and Google Scholar, and various FDD methods were reviewed to identify their advantages and limitations. In conclusion, data-driven methods are gaining popularity due to their ability to handle large amounts of data and improve accuracy, flexibility, and adaptability. Unsupervised and semi-supervised learning as data-driven methods are important for FDD in building operations, such as with HVAC systems, as they can handle unlabeled data and identify complex patterns and anomalies. Future studies should focus on developing interpretable models to understand how the models made their predictions. Hybrid methods that combine different approaches show promise as reliable methods for further research. Additionally, deep learning methods can analyze large and complex datasets, indicating a promising area for further investigation. [ABSTRACT FROM AUTHOR]

Technological improvements are crucial for achieving decarbonisation targets and addressing the impacts of climate change in the built environment via mitigation and adaptation measures. Data-driven methods for building performance prediction are particularly important in this regard. Nevertheless, the deployment of these technologies faces challenges, particularly in the domains of artificial intelligence (AI) ethics, interpretability and explainability of machine learning (ML) algorithms. The challenges encountered in applications for the built environment are amplified, particularly when data-driven solutions need to be applied throughout all the stages of the building life cycle and to address problems from a socio-technical perspective, where human behaviour needs to be considered. This requires a consistent use of analytics to assess the performance of a building, ideally by employing a digital twin (DT) approach, which involves the creation of a digital counterpart of the building for continuous analysis and improvement. This paper presents an in-depth review of the critical connections between data-driven methods, AI ethics, interpretability and their implementation in the built environment, acknowledging the complex and interconnected nature of these topics. The review is organised into three distinct analytical levels: The first level explores key issues of the current research on the interpretability of machine learning methods. The second level considers the adoption of interpretable data-driven methods for building energy modelling and the problem of establishing a link with the third level, which examines physics-driven grey-box modelling techniques, in order to provide integrated modelling solutions. The review's findings highlight how the interpretability concept is relevant in multiple contexts pertaining to energy and the built environment and how some of the current knowledge gaps can be addressed by further research in the broad area of data-driven methods. [ABSTRACT FROM AUTHOR]

The current goal of the European Commission, which aims to reduce CO2 by 90% compared to values estimated in 1980, and the ever-increasing sensitivity to environmental sustainability, fully involve the construction sector, which, according to the OECD (Organization for Economic Co-Operation and Development) is responsible for over one-third of the world's energy requirement. In this frame, numerous researchers and companies are focusing on ecologically sustainable building materials, to be used in new and existing buildings, that are able to simultaneously fulfill the constructive function and improve the energy behavior of the building envelope. The goal of the present paper is the analysis of the energy performance of some innovative locally produced natural building materials (timber, sheep wool, rammed earth, lime-based plaster, natural fibers) used in multilayer vertical closures, compared to that of more common building materials (bricks, concrete, synthetic insulation). First, the physical-mechanical characterization of the local natural materials was carried out, then the model of a building was implemented, whose energetic performance was simulated by varying the type of stratigraphy of the walls, including the use of both innovative and common materials. The building chosen for the simulation consists of one of the BESTEST ANSI/ASHRAE reported in the 140-2017 standard using the climatic data of the Mediterranean area. The results of the simulation have been presented and discussed. [ABSTRACT FROM AUTHOR]

In the cold regions of China, the existing rural houses are widely distributed and in large numbers. There are widespread problems such as low thermal performance of building envelopes, high building energy consumption, and poor indoor thermal environments. Reducing the energy consumption of building heating by reforming the envelope structure can reduce the environmental pollution caused by heating. In this paper, the existing rural houses in Tongchuan City, Shaanxi Province are taken as the research object, and EnergyPlus software is used to calculate building heating energy consumption, and the schemes are compared and selected via the entropy value method. Based on a comprehensive benefit evaluation, the best scheme for the renovation of building envelopes of rural houses in Tongchuan City is put forward. The research results show that the energy saving rate of buildings can reach more than 50% after renovation. In the evaluation of energy saving, incremental cost, return on investment, carbon emission reduction and unguaranteed hours, the weights are 0.1915, 0.2104, 0.2312, 1755, and 0.187, respectively. The best renovation scheme for rural housing is as follows: the thickness of the XPS board is 90 mm for exterior wall insulation; the thickness of the XPS board is 80 mm for roof insulation; the window-to-wall ratio of additional sunspace is 0.6; and the type of exterior windows is a broken-bridge hollow aluminum window of 6 + 12A + 6 (mm). [ABSTRACT FROM AUTHOR]

As buildings are one of the major contributors to greenhouse gas emissions and energy consumption, they have a key potential for energy efficiency and indoor environmental quality improvement. Therefore, the development of nearly Zero-Energy Buildings (nZEBs) is strategic to respond to these challenges and to design and retrofit sustainable highly performing buildings. Actually, the nZEB target can also be reached with highly insulated wooden technologies. However, they must be critically revised and adapted when taking into account the warm climate peculiarities. The paper contributes to this attempt by dealing with the implementation of a methodology specifically focused on the long-term assessment of the real building envelope performance. The methodology is applied to a recently built wooden nZEB detached single-story dwelling constructed in 2017 in central Italy. One year monitoring data were collected about the envelope in-field dynamic performance and the indoor microclimate and well-being conditions. The theoretical design-stage data and the monitored data were compared. The positive aspects as well as the critical issues of nZEB target in the Mediterranean climate context and the performance gap were underlined. Accordingly, the main aspects to be considered in the design of nZEBs envelope were highlighted. [ABSTRACT FROM AUTHOR]

According to the latest energy efficiency European directive (EED 2023/1791/EU), the expected energy renovation rate of at least 3% of the buildings' floor area each year towards nearly zero-energy buildings (nZEBs) is extended to include public buildings not only of the central government (as per the first EED 2012/27/EU) but also of regional and local authorities. This poses a great challenge, especially for Municipalities that often manage large building stocks with high energy demands. In response to this challenge, this paper presents the application of the so-called "typology approach" for conducting public buildings' energy renovation plans at the local level. A computational survey is initially introduced to decide the optimal set of building-stock clustering criteria among all possible combinations, involving the minimization of the RMSE index regarding the primary energy consumption of each building. For a representative building from each identified typology, the key performance indicators (KPIs) are computed for alternative energy-upgrading scenarios. Exploiting the IMPULSE Interreg-MED project tools, the KPIs from each representative building are at first extrapolated to all buildings of the examined stock and, finally, a gradual energy renovation plan is automatically produced based on user-defined decision parameters including the required annual renovation rate. The methodology is applied for the case of the Municipality of Hersonissos in Greece. For the specific 44-buildings' stock it was found that the optimal clustering set included four criteria, building use, construction year, heating, and a cooling system, leading to 15 building typologies. Finally, assuming a 7% renovation rate per year, a 12-year gradual renovation (nZEB transformation) plan is obtained foreseeing an 85% CO2 emissions' reduction. [ABSTRACT FROM AUTHOR]

The uncertainty in the initial condition seriously affects the forecasting skill of numerical models. Targeted observations play an important role in reducing uncertainty in numerical prediction. The conditional nonlinear optimal perturbation (CNOP) method is a useful tool for studying adaptive observation. However, the traditional CNOP method highly relies on the adjoint model, and it is difficult to find the global optimal solution. In this paper, a pre-screening and ensemble CNOP hybrid method called PECNOP is proposed to identify optimal sensitive areas in targeted observations. PECNOP is an adjoint-free method that captures global CNOP with high probability, which can effectively solve the two major problems faced by traditional CNOP methods. We evaluated the performance of PECNOP by building an observation simulation system consisting of an ocean model and data assimilation. One of the assimilation experiments was dedicated to evaluating the stability and effectiveness of PECNOP in extreme events. The results show that, compared with traditional methods, PECNOP can stably capture the global CNOP. Extra observations and assimilation in the optimal sensitive areas identified by PECNOP can effectively improve forecasting by about 20% within 30 days. Therefore, PECNOP has potential to reduce the initial error of numerical models, which is important for improving forecasting. [ABSTRACT FROM AUTHOR]

Although eccentrically braced frames (EBFs) can be used with different configurations according to architectural requirements, it has not yet been indicated which configuration has a better seismic performance; therefore, this paper presents an analytical study focused on evaluating the seismic behavior of various steel buildings with EBF systems, factoring in different configurations. Furthermore, the objective is to compare the performances of EBF systems with one another, to learn more about their structural efficiency. The results obtained indicate that seismic response, in terms of peak interstory drifts, depends on the structural period and hysteretic behavior of the links, because high levels of plastic rotation increase lateral displacement. In addition, it was observed that maximum drift demands are concentrated in the lower floors where the links exhibit inelastic behavior, while the level of interstory drift decreases as height increases. [ABSTRACT FROM AUTHOR]

This paper focuses on the seismic vulnerabilities of multi-storey buildings in hilly regions like Sikkim and Uttarakhand, where rapid construction is driven by population growth and tourism. The study particularly evaluates step-back buildings on hilly slopes, comparing their vulnerability to standard buildings on flat terrain. Using non-linear analysis to assess structural aspects like displacement and storey drift ratio, the research examines the performance of these buildings in both uphill and downhill orientations against typical three-storey and six-storey structures, respectively. The findings indicate that step-back buildings, especially those without infill walls, are more susceptible to seismic damage. For instance, on the uphill side, a step-back building shows a mean drift ratio 15.11% greater in the X direction and 4.57% greater in the Y direction compared to a three-storey regular building (3SR). This vulnerability is exacerbated when infill walls are absent, with mean drift ratios in step-back buildings being 74.75% and 33% higher in the X and Y directions, respectively. Moreover, at a seismic acceleration of 0.36 g, the mean displacement of a step-back building is 83% greater in the X direction and 51% greater in the Y direction than those with infill walls (SBIN), underscoring the significant role of infill walls in enhancing earthquake resilience. The study also highlights that short columns in step-back buildings are particularly prone to severe damage, especially just above the uppermost foundation level. While infill walls offer substantial mitigation of damage at the Design Basis Earthquake (DBE) level, at the Maximum Considered Earthquake (MCE) level, step-back buildings still endure severe damage compared to regular buildings with infill walls. Consequently, the research establishes that step-back buildings demonstrate greater vulnerability at DBE levels without infill walls and are more susceptible to damage than flat terrain buildings at MCE levels, emphasizing the need for careful design and reinforcement strategies in earthquake-prone hilly areas. [ABSTRACT FROM AUTHOR]

The climate change–built environment nexus is complex and intertwined. Recognizing the rising air temperatures and solar radiations owing to climate-induced global warming, it is critical to manage the increased building energy and cooling loads in the Middle East Gulf states' hot desert climates (Bwh). One of the top climate priorities is to promote climate resilience by reducing risks and enhancing adaptation options. This study aims to systematically review the existing literature to document building energy performances in and the associated adaptation measures of the Middle East Gulf states, regarding the implications of climate change. It is accomplished by answering the following questions: 'How well do we understand the effects of climate change on building energy use in hot urban deserts?' and 'What are the most appropriate adaptation strategies to reduce energy use in hot urban deserts?'. Using the Preferred Reporting Items for Systematic review and Meta-Analysis protocols (PRISMA), 17 studies on the influence of present and future weather scenarios on building performance are examined, considering variations in typology, methods, and input variables. Finally, the paper identifies the preferred methods and input variables for modelling building energy performance under predicted climatic changes. Passive design considerations are considered highly effective in mitigating and adapting to climate change implications. Thermal insulation and efficient window glazing are identified as the best-performing strategies, while the use of solar Photovoltaic (PV) is considered efficient in meeting the primary energy demands. The study's findings can assist planners and designers in projecting future climatic influences on the energy usage of existing buildings. [ABSTRACT FROM AUTHOR]

The current practice of building thermal retrofitting is based on the outcomes of energy audits that make use of standardized tabulated information mapped on a structure or structural elements under inherently very specific conditions. Therefore, it provides very limited outcomes for further analysis, especially when decision making procedures are required to particularize the retrofitting strategy. This paper introduces a novel fuzzy logic approach for predicting the thermal performance of building walls that can be used in practice to partially substitute time-consuming and costly energy audits or complex computational analyses. The objective of this concept is to forecast the annual heating energy demands of buildings and to identify the potential energy savings that could be achieved by applying thermal retrofitting measures based on limited resources and information obtained from maps, blueprints, and/or a simple site inspection. For this purpose, a sample knowledge base was created using a validated computational model of the heat and moisture transport in the multilayered wall assemblies. Then, the fuzzy logic model was introduced to predict the thermal performance of selected walls. Our comparison of the fuzzy model outputs with simulated data proved the potential of using the proposed concept as an efficient and straightforward tool for predicting the seasonal thermal performance of building envelopes and to partially replace the current practice, which requires the utilization of building energy audits. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate the fire performance in a multi-storey cross-laminated timber (CLT) structure by the computational fluid dynamics (CFD) technique using the Fire Dynamics Simulator (FDS v.6.7). The study investigates fire temperature, heat release rate (HRR), and gas concentration ( O 2 , CO 2 ). The importance of this research is to ensure that the fire performance of timber buildings is adequate for occupant safety and property protection. Moreover, the proposed technique provides safety measures in advance for engineers when designing buildings with sufficient fire protection by predicting the fire temperature, time to flashover and fire behaviour. The present numerical modelling is designed to represent a 10-storey CLT residential building where each floor has an apartment with 9.14 m length by 9.14 width dimensions. The pyrolysis model was performed with thermal and kinetic parameters where the furniture, wood cribs and CLT were allowed to burn by themselves in simulation. This research is based on a full-scale experiment of a two-storey CLT building. The present results were validated by comparing them with the experimental data. Numerical simulation of CLT building models show a very close accuracy to the experiment performed in the benchmark paper. The results show that the CFD tools such as FDS can be used for predicting fire scenarios in multi-storey CLT buildings. [ABSTRACT FROM AUTHOR]

This paper proposes a critical analysis of the intervention, over time, in the housing estates built under the Improvement Plan for the city of Porto. This plan enabled the construction of a significant number of dwellings within a limited period of time, promoting a broad and impactful urban and social restructuring of the city in the mid-twentieth century. Several post-construction interventions over the last decades were important opportunities to improve the quality of life of the inhabitants and to enhance the energy performance of the buildings. However, these buildings are still subject to architectural and construction challenges in terms of 'energy poverty' and the adaptation of the domestic comfort parameters. Thus, this research analyses the intervention strategies used in these buildings and the constraints resulting from current legislation and funding conditions that significantly impact design decisions. The study points to significant changes in the implementation of measures to improve the state of buildings, becoming progressively more concerned with energy consumption and thermal comfort. The discussion also highlights the impact of current measures on the comfort of interior spaces, current problems, and alternative means of balancing energy and comfort. The relevance of this research lies in the joint analysis of the interventions and frequent efficiency and comfort problems as a motto to improve the implementation of future strategies in developing a more energetically balanced housing stock. The paper also aims to deconstruct the preconceptions often associated with interventions geared towards thermal comfort, especially in social housing. [ABSTRACT FROM AUTHOR]

Energy used in buildings is mainly attributed to provide the desired thermal comfort, which could result in an increase in carbon emission and, in turn, lead to further environmental degradation. A Building-Integrated Photovoltaic Double-Skin Façade (BIPV-DSF) is a promising way to maintain indoor thermal comfort, obtained with low environmental impact and energy consumption. The appropriate design of BIPV-DSFs can maximise indoor thermal comfort and energy efficiency for buildings. This paper presents optimal BIPV-DSF design solutions, which are dedicated to offering comfortable and energy-efficient buildings, through optimisation of the most important design parameters of a BIPV-DSF under three different climate conditions in Australia. The results illustrate how thermal transmittance (U-value) and solar heat gain coefficient (SHGC) of windows of the BIPV-DSF, as the most important design parameters, were optimised for application in the context of different climates, operation modes, and orientations. The paper contributes to the matters concerning the integrated effect of BIPV-DSFs on thermal comfort and energy performance in buildings. [ABSTRACT FROM AUTHOR]

Improving the energy performance of buildings in the context of external climatic conditions and the requirements of indoor environments is a hot topic in the construction industry. It primarily concerns reducing the energy used for heating and cooling in buildings. In the EU sector, this is addressed by the Energy Performance Directive (EPBD), which is followed by relevant national standards. The energy performance of buildings is strongly influenced by the window structures that are part of the building envelope. Their influence on energy performance is represented by the heat transfer coefficient, which differs in the actual built-in window construction from the design value given by the manufacturer. In this paper, the authors deal with its measurement in situ using the heat flux measurement method. The measurement was carried out in the pavilion laboratory of the Department of Building Engineering and Urban Planning (DBEUP), Faculty of Civil Engineering (FCE), University of Zilina (UNIZA), on three window constructions of different material bases. During the measurements, surface temperatures on the glazing, heat flux density, and air temperatures were recorded in minute increments. The influence of the year-round cycle of the outdoor environment on the embedded window structures is presented and the results are presented in the conclusion of the paper. [ABSTRACT FROM AUTHOR]

The commitment of electrification and decarbonization goals in the United States (U.S.) will significantly change the performance of future buildings. To meet these goals, it is critical to summarize the existing research related to building electrification and decarbonization and discuss future research pathways. This paper provides a perspective on decarbonization pathways of future buildings in the U.S. A critical review of the existing research was conducted, which is divided into three closely linked categories: technologies, economic impacts, and code regulations. Technologies support investments and code regulations while marketing affects the design of building codes and standards. In the meantime, code regulations guide the development of technologies and marketing. Based on the review, future potential research directions for building decarbonization are then discussed. Due to the needs of building decarbonization, future research will be multidisciplinary, conducted at a large geographic scale, and involve a multitude of metrics, which will undoubtedly introduce new challenges. The perspective presented in this paper will provide policy-makers, researchers, building owners, and other stakeholders with a way to understand the impact of electrification and decarbonization of future buildings in the U.S. [ABSTRACT FROM AUTHOR]

The evidence of climate change has increased the necessity for actions to avoid severe consequences for future generations. Recognising the benefits of sustainable and energy-efficient buildings has led to the development of several methods to estimate and rate the performance of building. Healthcare buildings are functional and dynamic structures that support their occupants' healing processes and comfort. They have a significant role in society and specific goals in merging their strategic planning requirements (cost reduction, regulatory compliance, social responsibility, and performance improvement) with Sustainable Development Goals. Therefore, this paper addresses critical issues regarding the sustainability of the Brazilian healthcare sector by analysing the suitability of the most common international healthcare building sustainability assessment methods to the specific social, economic, and environmental contexts of the Brazilian healthcare sector. The paper analyses the HBSAtool-PT, a sustainability assessment method developed for this type of building, and identifies the possibilities for adapting to the Brazilian context. As a result, this research proposes a framework to assess the sustainability of healthcare buildings in Brazil. In the framework's definition, a survey was conducted to determine the opinion of building stakeholders regarding the importance of indicators. A weighing system for the proposed sustainability indicators was developed using the AHP method. [ABSTRACT FROM AUTHOR]

Block morphology refers to critical parameters influencing building energy performance on the block scale. However, analysis of the combined effect of block morphological parameters on building energy consumption with real blocks is lacking. In this paper, the aim is to evaluate the combined effect of office block morphology on building energy consumption in the context of the Hot-summer and Cold-winter zone in China. First, a workflow for the energy assessment of office buildings with the coupled block morphology on the block scale was proposed with evaluation tools. Seventy office blocks in Wuhan were taken as examples and then classified based on building layout typology and building height. Afterwards, the morphological parameters and building energy use intensity (EUI) for different blocks were calculated. Then, the combined effect of block morphology on the buildings' energy consumption was evaluated and the model on predicting the building energy consumption of office blocks was proposed. Finally, based on the results, low-energy design strategies were projected for office blocks. The results illustrated that the effect of block morphology on building cooling, heating, and lighting is EUI 28.83%, 28.56%, and 23.23%, respectively. Building shape factor (BSF), floor area ratio (FAR), average building height of block (BH), and average building depth of block (BD) are effective block morphological parameters. The key morphological parameters which combined affect the building energy consumption of office blocks are BSF and FAR; BSF has 1.24 times the effect on building energy consumption than FAR. The workflow built in this paper can be applied to other cities around the world for promoting sustainable cities. [ABSTRACT FROM AUTHOR]

When an earthquake occurs, it causes great damage to a large area. Although seismic engineering continues to develop, it is reported that recently occurred earthquakes inflicted major damage to various structures and loss of human lives. Such earthquake damage occurs in high seismic regions as well as low to moderate seismic regions. This special issue contains topics on newly developed technologies and methods for seismic performance evaluation and seismic design of building structures. [ABSTRACT FROM AUTHOR]

Because of their significant energy consumption and its economic and environmental impacts, existing buildings offer decision makers opportunities and challenges at the same time. In fact, there is a worldwide effort to improve the energy performance of the existing buildings as well as the new ones to achieve zero-energy buildings. In this paper, a framework for retrofitting existing buildings to help achieve the goal of zero-energy buildings is presented. The framework details the different steps required to develop and implement a successful retrofitting plan for both residential and commercial buildings. This includes data collection, life cycle cost calculation, building simulation, and multi-criteria decision making using the analytic hierarchy process (AHP). At the end of the paper, a case study is detailed to show the different steps necessary to select a successful retrofitting plan that reflects the decision maker's objectives. The case study resulted in a retrofitting plan that offers a yearly energy savings of 30% and a payback period of 2.2 years. [ABSTRACT FROM AUTHOR]

Climate change and natural disasters have brought threats to humanity greater than ever. The built environment is a critical part of the climate action plan in terms of its resilience and sustainability. The growing awareness of sustainability in the built environment has led to the increased uptake of sustainable buildings and infrastructure projects. However, a growing number of studies reported some forms of performance gaps in achieving sustainability, in which the actual outcomes and performance of buildings were far from the expected goals. Researchers started to question the alleged benefits of the sustainable built environment. Apart from being overoptimistic in setting the environmental goals, some studies suggested that the gaps could arise from a negligence of the interplay between humans and the environment for delivering the sustainability goals. Hence, this paper intends to highlight opportunities in optimizing sustainability performance in the built environment via human-centric approaches. By placing human needs at the focal point, human-centric solutions would empower stakeholders to harness sustainable goals in the built environment effectively. A conceptual framework has been proposed to consolidate the findings and highlight human-centric solutions in between research gaps and opportunities in sustainable built environment research. [ABSTRACT FROM AUTHOR]

Increasing awareness of climate issues in recent decades has led to new policies on buildings' energy consumption and energy performance. The European Union (EU) directive 2010/31/EC, i.e., the energy performance of buildings directive (EPBD), is one of the measures initiated to achieve climate and energy goals by reducing energy use and greenhouse gas emissions in the building sector. The EPBD required all new buildings to be nearly zero-energy buildings (nZEBs) by 2021. Nearly zero-energy buildings (nZEBs) are buildings with a very-high-energy performance and nearly zero or low-energy requirements covered to a very significant extent by energy from renewable sources produced on-site or nearby. The utilisation of solar photovoltaic (PV) panels is a common approach for achieving the nZEB standard. The carbon footprint of PV panels is often not discussed as a parameter. This paper aimed to analyse the environmental performance of an existing nearly zero-energy university building in a Norwegian use case scenario. This analysis is performed by assessing annual electricity and heat consumption from both energetic and environmental perspectives. The energy required for the building during the studied period is then used to analyse the environmental and energy performance of the building. When it comes to the environmental assessment, the commercial software SimaPro was used. The proposed revision EPBD and nZEB definition from 2021 suggests that nZEBs should also consider operational greenhouse gas emissions and life-cycle global warming potential from 2027. The life cycle assessment (LCA) of the building's energy sources looks at the global warming potential (GWP) and greenhouse gas (GHG) emissions, and how they compare to Norwegian grid electricity. The results of the analysis highlights potential challenges to justifying the use of alternative energy sources to fulfil the criteria of nZEBs. When installing solar PV, it is important to consider the energy mix of the country where the solar PVs are produced. To solely consider the energy performance of the building, the installation of solar PV panels in countries with a high share of renewable energy may result in a reduced impact in terms of emission reduction from a life cycle perspective. [ABSTRACT FROM AUTHOR]

The building stock is responsible for 24% of carbon emissions in Switzerland and 44% of the final energy use. Considering that most of the existing stock will still be in place in 2050, it becomes essential to better understand this source of emissions. Although the Swiss Cantonal Energy Certificate for Buildings (CECB) database has been used in previous research, no comprehensive characterization of the buildings can be found. This data paper presents an analysis and classification of the Swiss building stock based on the data found in the database. The objective is to create a knowledge foundation that can be used in future research on the performance of existing buildings. Using a sample of almost 50,000 buildings and a Python script, datasheets were created for single-family houses and multi-family houses for nine construction periods. These archetypes are described through selected available energy-related parameters, such as energy reference area, U-values, and energy source with indicators such as median, 25th percentile, and 75th percentile or distributions. The resulting data can be used for different purposes: (1) to calibrate energy models; (2) for analysis that requires scaling-up strategies to the whole stock; and (3) to identify weak and/or relevant classes of buildings throughout the stock. [ABSTRACT FROM AUTHOR]

Over the past few years, modular buildings have become an important form of environmentally friendly architecture. Prefabricated construction methods have gained a lot of attention because they produce less construction waste and require less labor and water. However, the seismic performance of modular buildings needs to be improved. This paper proposes a prefabricated steel module with a suspended floor, which is based on a multi-tuned mass damped floor system. This paper also derives the form of a motion equation which is unified with the construction process of modular buildings, which can describe the change law of the mass, stiffness, and damping matrix of the structure in the processes of connecting the main structure with the suspended floor slab and of joining different floors. Since the performances of tuned mass damping devices are closely related to the dynamic characteristics of the structure, this paper uses ABAQUS for numerical analysis and mathematical induction (MI) to propose and verify a simplified method for calculating the lateral stiffness of the entire story from a single module's lateral stiffness. Based on the principle of reducing the stiffness difference in the structure along different directions, a standard scheme of the horizontal extension of the module building is also specified. The results show that the simplified calculation method is reasonable and that the lateral stiffness of the structure increases linearly with the number of modules. Finally, the recommended values for the tuned frequency ratio and tuned damping ratio are given by investigating the dynamic response of the structure under Gaussian white noise excitation. The results show that the recommended tuning frequency ratio and damping ratio ranges in modular buildings are close to those for FIS buildings. [ABSTRACT FROM AUTHOR]

Governments throughout the EU and the UK face a persistent challenge of satisfying the continual growth in demand for housing and create zero carbon buildings. This paper presents a novel conceptual framework and empirical results to evaluate the potential for Structurally Insulated Panels (SIPs) to address this challenge. Firstly, we present a comprehensive analysis of the energy performance of a SIPs building in the UK to evaluate the potential for such technologies to satisfy the energy and carbon objectives. Using our unique data set we show that SIPs can exceed the necessary energy efficiency standards and reach Net Zero Carbon. Secondly, we introduce game theory as a novel conceptual framework to understand the incentives of the manufacturers of SIPs and UK/EU authorities. This enables us to identify potential sources of incentive conflict, which inhibit the diffusion of such technologies. We demonstrate that it benefits both parties to engage in medium investment, but the inferior under investment scenario can emerge if the UK/EU does not provide leadership and commitment to SIPs technologies. Regardless of the market design in terms of the timing of decisions, the maximum level of support by both the UK/EU and the SIPs manufacturers cannot be achieved. [ABSTRACT FROM AUTHOR]

This paper aims to assess the profitability and employees' productivity of Italian innovative start-ups with a new composite index: the Start-ups Performance score (SP-score). In recent years, the Italian government has made several efforts to foster the economy, establishment, and growth of start-ups. Specifically, the issuing of the Italian Start-up Act in 2012 offers a unique opportunity to examine the Italian setting, given the special conditions that the government granted to the start-ups. The latter obtain generic benefits, tax reductions and hiring facilitations if they meet specific criteria granting the status (label) of "innovative" by law. Based on a sample of Italian start-ups firms (ISPs) and financial statement data for the period 2016–2020, we test whether the performance of the Italian start-ups which are granted the status of "innovative" is higher compared to start-ups without the mentioned status (NISPs). We measure the firm's performance by building a composite index which synthesizes both profitability and employees' productivity (controlling for the firms' size), and find that the ISPs report higher SP-score compared to the NISPs. In addition, we examine whether the SP-score is higher (or lower) among Innovative start-ups located in different regional areas. The derivation of the composite indicator (SP-score) allows a clear and effective evaluation of several characteristics, permitting a more comprehensive understanding of the data that support the strategic choices of management and localization entrepreneurship policies. In addition, our study highlights a certain homogeneity of innovative start-ups' performance among the Italian territories, which overcomes the classic dualistic division between Northern and Southern regions. [ABSTRACT FROM AUTHOR]

Selecting an appropriate ANN model is crucial for speeding up the process of building performance simulation during the design phase of residential building layouts, particularly when evaluating three or more green performance metrics simultaneously. In this study, daylight, visual, and outdoor thermal metrics were selected as main green performance. To find the suitable ANN model, sensitivity analysis was used to obtain a set of proper parameters applied to the ANN structure. To train the ANN model with a higher predicting accuracy, this paper tested four different scenarios of ANN parameter setups to find some general guidelines about how to set up an ANN model to predict DF, sunlight hours, QuVue and UTCI. The results showed that an ANN model with a combined output variable demonstrated better average prediction accuracy than ANN models with a separated output variable. Having two times the number of training samplings compared to the number of input variables can lead to a high accuracy of prediction. The ideal number of neurons in the hidden layer was approximately 1.5 times the number of input variables. These findings of how to improve the ANN model may provide guidance for modeling an ANN for building performance. [ABSTRACT FROM AUTHOR]

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

People tend to spend considerable amounts of time in buildings; thus the issue of providing proper indoor environmental quality is of significant importance. This paper experimentally analyses the subjective sensations of the occupants of intelligent and traditional buildings with the focus on possible differences between these two types of buildings. The study is based on a large database of 1302 questionnaires collected in 92 rooms where simultaneous measurements of the indoor environment physical parameters (air and globe temperature, relative humidity, carbon dioxide concentration, and illuminance) were carried out. Their impact on the subjective assessment of the indoor environment has been presented and analysed. The results show that the occupants seemed to be more favourable towards the indoor conditions in the intelligent building; however, the differences in comparison to the traditional buildings were not considerable. Similarly, self-reported productivity proved to be higher in the intelligent building, while the optimal range of air temperature, which ensured highest productivity, was 22–25 °C. Moreover, a strong correlation between the occupants' overall comfort and their perception of the air quality has been found. [ABSTRACT FROM AUTHOR]

We are very happy that the Special Issue "Advanced BIM Application in Construction and Buildings" of Buildings has been published. The eighteen papers deliver innovative ideas and challenging developments of BIM and/or BIM-based applications conforming to the Construction 4.0 era and smart construction. Ayman et al. [[14]] develop a decision support system (DSS), which combines BIM and EDM to track and monitor construction project activities during construction with better performance and accuracy. [Extracted from the article]

The United States building sector consumed approximately 75% of electricity in 2019. By implementing renewable energy technologies and control strategies into buildings, future buildings will serve as energy generators as well as consumers. To accommodate this transition, communications among buildings and between buildings and the grid could provide more possibilities to optimize the energy performance of buildings. This paper develops a community-scale building energy model tool and conducts a case study adopting behind-the-meter distributed energy resources, sharing energy in different buildings, and using different electricity tariff structures. Three scenarios are studied: (1) electricity only supplied by the grid, (2) photovoltaic (PV) panels installed on and available to some but not all buildings, and (3) a connected community. To consider the impacts of locations and energy tariffs, this paper selects four cities and three electricity tariffs to evaluate the energy and cost performances of these three scenarios. The results show that the PV panels in Scenario 2 reduce 25% to 33% of the community-level electricity consumption and 20% to 30% of the community-level electricity cost compared with Scenario 1 in all studied locations and energy tariffs. By considering power management in the connected community (Scenario 3), the electricity consumption and cost can be further reduced by 6% to 7% and 5% to 11%, respectively, compared with Scenario 2. [ABSTRACT FROM AUTHOR]

In recent years, machine learning has gradually been applied to building energy-saving designs to reduce the time consumption of the optimization screening stage. However, since most of the existing research scholars come from the fields of computers and engineering, the application of machine learning technology mostly involves complex programming as well as software in the field of engineering, which requires multiple software to be coupled to achieve. In view of the differences between disciplines and the high learning threshold, these theories are difficult to apply and promote in practical work in the field of architecture. In this regard, this paper focuses on the improvement of methods, based on the Grasshopper platform, proposes a detached energy-saving residential form generation design method and process, to explore the optimal energy-saving building form in a more concise and efficient way. Based on this new method, on the basis of verifying its feasibility through a residential building case, two machine learning algorithms, neural network (ANN) and support vector machine (SVM), are compared and studied, and the applicability of these two algorithms in different building performance indicators is further discussed. The results show that the ANN model has the highest accuracy and is more suitable for the prediction of building energy consumption; in view of the simple and fast operation of SVM, it is more suitable for comfort prediction with relatively low accuracy requirements. By combining the above two machine learning methods, work efficiency can be improved while satisfying the prediction of relevant performance indicators. This method can help architects quickly search for the best building energy-saving form design scheme in the scheme design stage and provide data support and information feedback for architects in design conception and deepening. [ABSTRACT FROM AUTHOR]

Reducing energy consumption while providing a high-quality environment for building occupants has become an important target worthy of consideration in the pre-design stage. A reasonable design can achieve both better performance and energy conservation. Parametric design tools show potential to integrate performance simulation and control elements into the early design stage. The large number of design scheme iterations, however, increases the computational load and simulation time, hampering the search for optimized solutions. This paper proposes an integration of parametric design and optimization methods with performance simulation, machine learning, and algorithmic generation. Architectural schemes were modeled parametrically, and numerous iterations were generated systematically and imported into neural networks. Generative Adversarial Networks (GANs) were used to predict environmental performance based on the simulation results. Then, multi-object optimization can be achieved through the fast evolution of the genetic algorithm binding with the database. The test case used in this paper demonstrates that this approach can solve the optimization problem with less time and computational cost, and it provides architects with a fast and easily implemented tool to optimize design strategies based on specific environmental objectives. [ABSTRACT FROM AUTHOR]