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This position paper aims to pave the way for a debate on a few under-explored, at least, perhaps even neglected, challenges we face when trying to improve building overall performance. Specifically, we suggest focusing on how the efforts to increase building energy efficiency, building safety, the home and workplace healthiness, and the comfort perceived by the users can be impaired by budget constraints, especially while operating in critical scenarios. On the one hand, restraints on capital expenditures by property owners and other investors affect the decision-making processes for the construction of new buildings or the renovation of existing ones. More beyond, rapidly developing demographic and other anthropological changes, as well as frequently occurred natural disasters, pose extra burdens on the players in the building industry and the real estate market. It has been a fact that the need to adapt to both budget constraints and challenging situations is seldom fully embedded in the studies focusing on improving building performance. Therefore, we call for attentions in research and publications to advocate for complementing the need. [ABSTRACT FROM AUTHOR]

Soft-switching technologies can effectively solve the problem of switching losses caused by increasing switching frequency of grid-connected inverters. As a branch of soft-switching technologies, load-side resonant soft-switching is a hotspot for applications of high-frequency inverters, because it has the advantage of achieving soft-switching without using additional components. However, the traditional PI control strategy based on the linear model is prone to destabilization and non-robust dynamic performance when large signal perturbation occurs. In this paper, a novel Passivity-Based Control (PBC) method is proposed to improve the dynamic performance of load-side resonant soft-switching grid-connected inverter. Besides, the model based on the Port Controlled Hamiltonian (PCH) model of the soft switching inverter is carried out, and the passivity-based controller is designed based on the established model using the way of interconnection and damping assignmentpassivity based control (IDA-PBC). Both stable performance and dynamic performance of the load-side resonant soft-switching inverter can be improved over the whole operating range. Finally, a 750 W load-side resonant soft-switching inverter simulation model is built and the output performance is compared with the traditional PI control strategy under stable and dynamic conditions. The simulation results show that the proposed control strategy reduces the harmonic distortion rate and improves the quality of the output waveforms. [ABSTRACT FROM AUTHOR]

The Jordan Museum is the most important modern archeological museum in Jordan. This paper assesses the quality of design and the visitors' satisfaction with the Jordan Museum. Also, it investigates which of the selected performance elements contribute most to visitors' satisfaction. Investigative Post-Occupancy Evaluation (POE) was used within a mixed methodology approach. A questionnaire was used for the quantitative part, complemented by other qualitative methods. Descriptive statistics were used to determine the design quality and visitors' satisfaction. Stepwise regression was used to detect which performance elements predict overall visitors' satisfaction. The design quality of the museum was rated as good, and its permanent galleries were exceptionally rated as excellent. The visitors were generally satisfied with the museum. Visitors were satisfied with the interior and exterior finish but were not impressed with the heavy use of stone in the façade. The simplicity of the museum layout made navigation easy, and visitors were satisfied with wayfinding. This paper is the first to use POE to assess the Jordan Museum. The findings can be used to enhance the management and operation of the Jordan Museum. Also, important performance elements can be developed into guidelines to guarantee effective museum designs in the future. [ABSTRACT FROM AUTHOR]

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]

The special collection on Twenty Years of Advances in Disproportionate Collapse Research and Best Practices since 9/11/2001 is available in the ASCE Library (https://ascelibrary.org/jsendh/twenty years disproportionate collapse). Kong et al. ([8]) report their experimental and numerical findings on the progressive collapse behavior of scaled six-column subframe composite floor systems subjected to the removal of a side column. Beck et al. ([2]) target optimal required ratios of column-to-beam strengths in column loss situations, considering several failure modes and providing conceptual design recommendations to mitigate disproportionate collapse of frame structures. Three papers focus on advances in analysis and design methods for disproportionate collapse ([2]; [7]; [18]) and show that their limitations no longer are a barrier to disproportionate collapse-resistant design that they once were. [Extracted from the article]

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]

The 2010–2011 Canterbury earthquake sequence highlighted the vulnerability of New Zealand light-frame wood buildings that are the predominant typology in residential construction. While life-safety performance was excellent, many buildings experienced widespread cracking of internal wall linings that resulted in costly repairs and disruption. Despite the widespread use of light-frame wood construction in New Zealand, no known shake table tests have been documented for this construction typology. Additionally, New Zealand light-frame wood building performance cannot simply be inferred from international testing because New Zealand adopts a different design philosophy from those of the US and Canada by relying on gypsum sheathing as the primary lateral resisting element. To further investigate New Zealand light-frame wood building performance, this paper discusses shake table testing of a light-frame wood building specimen, constructed on the University of Canterbury unidirectional shake table using modern New Zealand design and construction approaches. The observed damages during the tests were consistent with those observed in similarly built structures that were investigated after the 2010–2011 Canterbury earthquake sequence. This paper also presents a modeling approach using the state-of-the-art software Timber3D, which was verified by simulating the shake table specimen and comparing the observed and predicted dynamic response from the test specimen and model, respectively. The Timber3D modeling approach included analysis and parameterization of screw-slip data using the Evolutionary Parameter Hysteretic Model, formulation of two-dimensional wall component models, and nonlinear time history analysis of a three-dimensional model of the shake table specimen. The model was shown to reasonably predict the displacement response of the shake table specimen and the strength degradation from cracking damage through repeated earthquakes. The modeling approach described is intended to further the development of residential building models toward the performance-based seismic design of light-frame wood buildings in New Zealand. [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]

With the intensifying global energy crisis and environmental issues, the energy consumption and environmental impact of the construction sector have garnered significant attention. The development and application of green building materials have become crucial research areas. Traditional building materials cause substantial resource waste and environmental pollution during production, usage, and disposal. In contrast, green building materials based on thermodynamic principles, owing to their superior thermal performance and energy storage capabilities, have gradually gained attention. This paper aims to systematically optimize the thermal performance of green building materials through a research approach grounded in thermodynamic principles. The study consists of two main parts: first, constructing the control equations for the phase change thermal storage process of green building materials, and analyzing the thermodynamic characteristics during the phase change process in detail; second, conducting an in-depth analysis of the heat transfer process in phase change thermal storage green building materials to explore their thermal performance in practical applications. Despite extensive research on the thermal performance of green building materials, there are still shortcomings in the systematic analysis and optimization of material thermal performance, and existing research methods often fail to comprehensively and accurately reflect the actual thermal behavior of materials. This paper aims to provide a scientific basis for the optimized design of green building materials, further promoting their application and development in the construction field. [ABSTRACT FROM AUTHOR]

Climate significantly affects the performance of building materials and its changing patterns are posed to increasingly exacerbate the projected impacts. Prevention strategies are indeed necessary to ensure avoiding excessive degradation and serious damage to the built environment. In this context, innovative and accessible methodologies and tools are required to investigate and characterize the interaction between material properties and climatic factors. This paper presents an innovative device for the simulation of natural ventilation, relative humidity and temperature fluctuations and for evaluating the performance of building materials subjected to different environmental scenarios. The presented results include the design, construction and validation of a small-scale wind tunnel (2 m × 1.8 m ca. vertical orientation). Key findings outline the adequacy of the tool in reproducing a stable, quality airflow with the following characteristics: achievable operational airflow speed ranges between 0.2 and 0.7 m/s, safe operational temperature is included between 10℃ and 35℃ and allowable operational relative humidity varies between 30 and 99%. Advantages and limitations for laboratory applications are outlined in the paper and future work is also suggested. [ABSTRACT FROM AUTHOR]

Interstory drift response is one of most important quantities to quickly assess performance and damages in buildings. Nevertheless, direct measurement of interstory drift is difficult and expensive, because a stationary reference is required to attach measurement devices. With the goal of accurate and fast reference-free estimation, this paper proposes a new strategy to determine dynamic interstory drifts using accelerations. In particular, a Tikhonov regularization is adopted in a generalized minimization problem to achieve an efficient and stable finite impulse response (FIR) filter. Furthermore, due to independent clocks in wireless sensors, accurate time synchronization of the records is critical; consequently, a strategy for accurate synchronization is also presented. Finally, the proposed strategy has been deployed on edge devices for onboard real-time interstory drift estimation. The proposed method for dynamic interstory drift estimation is validated, first, by numerical simulation using earthquake records as base excitation of linear and nonlinear buildings, as well as through laboratory shake table experiments. Both numerical and lab test results show good agreement of dynamic interstory drifts between the measured value and estimated results, demonstrating the efficacy of the proposed method to estimate the dynamic displacements of seismically excited structures. [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]

In order to fully implement systematic, continuous and effective supervision of financial institutions and promote the safe, steady, and efficient operation of China's financial system, this research needs to develop a fully intelligent financial supervision information system, so as to take measures to effectively prevent and resolve financial risks. In this paper, based on ML (machine learning), an LSTM (long short-term memory) model with good comprehensive performance is built. This model is different from the existing scorecard model which relies on statistical learning. It not only further reduces the dependence on financial experts, but also has the ability of rapid iteration. The post-loan risk early warning model based on RF (random forest) algorithm is designed. The model parameters are optimized, which makes the risk early warning model have higher accuracy. The results show that when the data amount is 10,000 to 50,000, the accuracy of the model is relatively high when it is input into the model. When the amount of data is low, the overall throughput of the model is still very high. It shows that the pre-warning model of post-lending risk constructed in this paper has a strong risk prediction ability. [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]

This paper presents an extensive numerical study on the seismic performance of steel eccentrically braced frames (EBFs) designed according to the ASCE 7-16 standard. A series of EBF building structures ranging from 2 to 12 stories located in downtown Los Angeles are designed with different yielding links, including conventional and replaceable links. The performance of the structures is investigated through nonlinear time-history analyses (NLTHAs) under a suite of 40 ground motions, selected and scaled to match the target uniform hazard spectrum. The peak deformations are first examined to assess the performance of the buildings as well as the design procedure. Residual deformations are also evaluated and critically compared with acceptable limits, to provide insight into the downtime and recovery time of EBFs after major earthquakes. Previous studies have demonstrated that the response of stable yielding systems to major earthquakes is often accompanied by significant residual deformations, and that residual drifts exceeding 0.5% can cause hindrance to the buildings occupants. In EBFs, in addition to residual drifts, residual link rotations are also relevant and expected to be more severe due to the localization of inelastic deformations in the yielding link. For replaceable modular yielding links, significant residual link rotations after major earthquakes will hinder repairs and the link replacement process, which is one of the important design objectives. The performance of EBFs is also compared with what was observed in previous studies for buckling restrained braced frames and special moment resisting frames. In the end, the results from the extensive NLTHAs are used to establish a relationship between peak drifts and peak link rotations in EBFs. [ABSTRACT FROM AUTHOR]

Mitigating tornado damage remains a critical challenge for communities throughout much of the United States. Enhanced construction techniques typically used in hurricane-prone regions are often recommended as at least part of the solution, but the effectiveness of these techniques lacks empirical evaluation. This study investigates the performance of modern wood-frame residential structures in Arabi, LA, constructed to hurricane-resistant standards, that were impacted by a strong tornado on March 22, 2022. Field teams deployed beginning March 25, 2022, just three days after the tornado touched down, capturing perishable data on the building performance using vehicle-mounted surface-level panoramic cameras, forensic engineering investigations, and unmanned aerial systems. This paper describes the field deployments and identifies common failure mechanisms observed by the field teams. Analytical fragility functions are developed based on the load path observations and compared to empirical fragility functions generated from linking the building performance observations with wind speed estimates at each building derived from a parametric wind field model conditioned to tree-fall patterns. The study finds that despite the frequent use of hurricane-resistant hardware, such as hurricane straps and anchor bolts, key weak links elsewhere in the load path compromised the resistance and led to premature failures. The agreement between the empirically- and analytically-derived fragility functions was lacking, demonstrating the challenges that remain in understanding near-surface tornado wind loading and structural response. Nonetheless, the study provides a consensus framework for future tornado assessments that can help improve our understanding of tornado loading through the utilization of reconnaissance data. Further, the study findings on deficiencies in the load paths of homes in hurricane-prone regions have practical value to risk assessments and future construction practices. [ABSTRACT FROM AUTHOR]

Building thermal performance testing requires in situ measurement techniques that are well supported and validated by simulation with statistics to improve the accuracy of the results. Local on-site performance of building components is different from the theoretical one, influenced by factors affecting the building's thermal conditions. The current paper reviews the factors influencing the measured U-value results in the heat flux method based on quantitative findings of other studies through regression and correlation statistics. The findings regarding the current status of knowledge are limited to in situ methods without detailed insights of response time, sensitivity analysis, and thermal boundary conditions in the local context. Regression analysis between wall characteristics, time duration, temperature difference, and the measured U-value shows a very strong and statistically significant impact of these variables on the accuracy of the measured U-value of low transmittance walls. The R2 value indicates that three variables can collectively explain 91% of the variance in the measured U-value. There is a linear correlation between the wall characteristics and the measured U-value and a non-linear correlation between the time duration, temperature difference, and the measured U-value. Future work will focus on developing a measurement framework that considers time-dependent variables, dynamic weather, and uncertainty with high accuracy for different boundary conditions. [ABSTRACT FROM AUTHOR]

3D building models is crucial for applications in smart cities. Automatic reconstruction of 3D buildings has been investigated based on various data sources. Point clouds from airborne LiDAR scanners can be used to extract buildings data due to its high accuracy and point density. In this paper, we present a methodology to segment buildings and corresponding rooftop structure from point clouds. First, RandLA-Net, which is an efficient and lightweight neural network for semantic segmentation of large-scale point clouds, is revised and adopted for building segmentation. By implementing local feature aggregation of each point, RandLA-Net can effectively preserve geometric details in point clouds. Besides 3D coordinates of point clouds, we incorporated point attributes including pulse intensity and return numbers into the network as additional features. Feature normalizations are applied to the input features. To achieve a better result of the local feature aggregation, hyperparameters of the network are fine-tuned according to the density of points and building size. Based on the classified building point clouds, DBSCAN clustering algorithm is implemented for segmenting individual buildings. Elevation histogram analysis is conducted to determine optimal threshold values for delineating candidate rooftop point clouds of individual buildings. For the buildings with multiple rooftops, multiple elevation threshold values are necessary to extract corresponding rooftops or walls. Then DBSCAN is employed again for segmentation of individual rooftops and denoising of point clouds of each building. Finally, Alpha-shape analysis is applied based on adaptive threshold values to build the envelope of each rooftop. Experiments show that our implementation of building segmentation using RandLA-net achieves higher mean IoU (Intersection over Union) and better classification performance in building segmentation. ISPRS benchmark data was used in our experiment and our methodology produce results with accuracy of 90.79%. [ABSTRACT FROM AUTHOR]

Six of these papers focus on ultrahigh-performance concrete (UHPC) while five papers focus on high-strength concrete (HSC) or high-strength reinforcing (HSR) steel materials. Yang and Okumus ([11]) describe a parametric numerical investigation on the seismic behavior of UHPC rectangular hollow bridge columns with HSR steel for confinement. In this paper, the results from seven column test specimens demonstrate that the ductility capacity of UHPC-jacketed columns can be increased through textile reinforcing. [Extracted from the article]

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 financial status of an enterprise is related to its healthy and long-term development, and whether the interests of investors and bank loans can be guaranteed. To improve the prediction accuracy of corporate financial risk, this paper proposes a prediction model for corporate financial risk that integrates GRA-TOPSIS and SMOTE-CNN. First, using GRA-TOPSIS to make a comprehensive evaluation of the financial situation of listed companies. Second, the evaluation results are clustered to obtain the scientific level and interval of financial risk, which lays the foundation for the supervised learning of the convolutional neural network. Then, the SMOTE algorithm is introduced to solve the problem of data imbalance of enterprises at all levels, and the focal loss function is used instead of the cross-entropy loss function to further balance the data. Finally, the listed companies in A shares are randomly selected, and experiments were designed to verify the performance of the model built in this paper. The results show that the prediction accuracy of the financial risk prediction model based on GRA-TOPSIS and SMOTE-CNN is 98.57%, which indicates that the model is feasible and has certain reference value. [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]

In the circumstances of "Carbon Peak," it is imminent to implement green construction technology in the construction industry as it is a major source of CO2 emissions. Nevertheless, little study has been done on the impact of self‐face awareness (SA) on green building project performance (GP). On the basis of a large‐scale questionnaire survey in China and an in‐depth analysis of the four categories (cognition, behavior, satisfaction, and performance), this paper has added an SA dimension, and proposed a new impacting path. Then, based on the improved conceptual model for measuring the impact of green construction behavior on GP, this paper has conducted empirical tests by using a large sample of questionnaire data and partial least square‐structural equation modeling method. The results show that: (1) green construction behavior has a positive impact on GP, and the impact of perceived behavioral control of green construction technology is most significant for promoting green construction in the industry, and SA has a significant positive effect on green construction in the industry; (2) environmental perception and perceived behavioral control for green construction technology play a mediating role; and (3) significant differences indeed exist in the multigroup analysis, and the biggest difference is working experiences in the engineering field. Finally, this paper has proposed corresponding management implications based on the above findings. The main contribution of this study is that this study has developed a new dimension (SA) to the model considering Chinese national and cultural differences, in an attempt to reveal the impact of green construction technology on GP in the Chinese context. In addition, the promotion of green construction (PGC) in the construction industry was also added based on the sustainability perspective, which is the first attempt to explore the role of the PGC. [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]

A complete evaluation of conflicting and nonlinear multiperformance is referred to as an evaluation system. Therefore, multiobjective optimization research for building performance is of great importance. Considering the above problems, this paper proposes a multiobjective performance optimization platform at the architectural design stage. The optimization research is combined with the basic algorithms. It is based on Grasshopper software. It aims to provide a fast and effective method for the optimization design of sustainable buildings. It is important to note that this workflow has no restrictions on what to optimize for or what to target, and can be determined on a case-by-case basis. To evaluate the use of the work platform and make the research results more extensive, the standard model of the inner corridor space building in office buildings in cold areas was extracted. The extracted standard model was taken as the final research object. At the same time, the convergence, distribution, and comprehensive evaluation indexes of the simulation results were analyzed. The quantitative influence parameter system of window parameters on the total building energy consumption, lighting uniformity, and glare probability was established. A series of parametric design models based on the comprehensive optimal solution of building energy consumption and lighting quality was obtained. The main purpose of this paper is to design a green building design method based on the principle of goal-oriented design. This paper, combined with the group search function of nondominated genetic algorithm (NSGA-II), the energy consumption of the building, and the evaluation function of indoor thermal and humid environment, is obtained and linked with MATLAB. The energy consumption of residential buildings and indoor thermal comfort were taken as the optimization objectives of the design scheme. The automatic optimization model of the architectural design scheme was established. Finally, the Pareto solution set of the architectural design scheme is obtained, and the designer can select the appropriate design scheme of the Pareto solution set according to the actual situation of the project, to meet the requirements of green building design. The design scheme optimized by this method can effectively reduce building energy consumption and improve indoor thermal comfort. [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 thermal resistances (R-values) of airspaces depends on the emittance of all surfaces around an airspace, dimensions, heat-flow direction, and the temperatures of the bounding surfaces. Assessing the energy performance of building envelope components and fenestration systems requires accurate results for the R-values of any enclosed spaces. The evaluation of reflective insulation R-values has evolved to include use of computational fluid dynamics and surface-to-surface radiation to quantify convective and radiative contributions to the heat transfer across airspaces of all types. This paper compares an advanced and validated model for calculating enclosed airspace R-values with the widely-used ISO 6946 and airspace R-values in the ASHRAE Handbook-Fundamentals. The impact of construction and installation defects on the thermal performance of reflective insulation has not been previously evaluated. In this research, an advanced model was used to evaluate a construction defect and dimensional aspect ratios that one-dimensional methods do not address. Imperfect installation and defects that result in air movement into or through a reflective insulation assembly reduces the thermal resistance of the assembly. Additionally, the amount of thermal resistance reduction depends on the amount and temperature of invasive air or the size of internal defects that allows natural convection inside the reflective airspace. In this study, these performance issues are evaluated quantitatively using computer simulation techniques. The differences in results obtained using methods that are currently being used to evaluate the R-value and the advantages of the advanced method for evaluating the reflective insulation performance for different applications are discussed. For the case considered in this study, the results showed that the failure to achieve parallel surfaces results in less than a 5% decrease in thermal resistance. Also, the results showed that internal air gaps between airspaces result in negligible loss in R-value unless air gaps that allow circulation between airspaces are created. [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]