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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]

In this paper, the radial basis function neural network based on the orthogonal least squares (OLS-RBFNN) method is proposed for the first time for the calibration of the six-port receiver, which realises six-port accurate reception in the true sense. This algorithm overcomes the problems of an ageing circuit, narrow working frequency band and nonlinearity of the six-port receiver hardware. The RBFNN is a three-layer forward network, including an input layer, a hidden layer and an output layer. The transformation function of the hidden layer is the radial basis function. A six-port receiver test platform with an operating frequency band of 2.0–8.0 GHz is built to verify the performance of the six-port receiver based on OLS-RBFNN. The test signal is a 16-QAM signal with different bandwidths and different frequency bands. The test indicator uses error vector magnitude (EVM)， which represents the error between the received signal and the ideal signal sent. At last, the EVM of all tested data is less than 1.07%, and the best EVM is 0.57%. Therefore, the calibration accuracy of the OLS-RBFNN proposed in this paper is very high. [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]

In real urban environments, wind velocities often exhibit significant time-dependent variations. However, the impact of these fluctuations on ventilation performance is not frequently addressed in the literature. To accurately predict the building ventilation performance, this paper intends to elucidate the influence of the unsteady approaching flow. Four distinct inflows, characterized by periodic sinusoidal fluctuations, are evaluated using large-eddy simulation (LES). A single-sided building model with natural ventilation is constructed and validated through a wind tunnel experiment. Four periodic inflows consist of combinations of two frequencies (f = 0.1 and 0.3 Hz) and two amplitudes (A = 0.06 and 0.09). This paper mainly compares the results of airflow patterns and ventilation performance with those of stable inflows. The results indicate that periodic inflows cause greater fluctuations in the velocity fields on the leeward side. At higher frequencies and lower amplitudes, the vertical distribution of indoor bottom vortices is relatively dispersed. It shows that periodic flows lead to noticeable changes in the ventilation rate, with a deviation between instantaneous and average Q* ranging from 13.8% to 16.5%. Compared to the stable inflow, the impacts of periodic inflows on ventilation performance vary over time; 6% and 9% variations in incoming velocities result in deviations of up to 17.5% in ventilation rates. The frequencies and amplitudes of the periodic inflows do not exhibit a linear relation with ventilation rate. Therefore, it is prudent to be cautious about adopting averaged incoming wind velocities in predicting ventilation performance. This study helps in the more accurate prediction of single-sided natural ventilation. [ABSTRACT FROM AUTHOR]

Traditional buildings have a central role to play in Europe's plans to be the first climate-neutral continent by 2050, with the building sector being the single largest energy consumer in the EU with 40 per cent of total energy use. Seventy-five per cent of the existing housing stock is energy inefficient with minute numbers renovated each year. Existing buildings offer huge potential for energy use reduction. Research has shown that a historic retrofit can emit less carbon compared to demolishment and rebuilding. Reductions in carbon emissions of up to 62 per cent were observed across a body of case studies by Historic England, including a 95 per cent reduction in the mews case study described in this paper. Moreover, carefully managed thermal retrofits, taking a 'whole house approach', have many benefits, including improved indoor comfort and air quality as well as reduced operational costs. These are factors which are fundamental in ensuring the continued use of historic constructions while improving their standards of preservation and durability. Thermally upgrading traditional building is still a much-debated topic. Inappropriate thermal interventions with unsuitable materials and finishes can have unforeseen consequences, resulting in building damage and a reduction in internal comfort, health and well-being. This paper aims to contribute to this discussion by providing an overview of key considerations when thermally upgrading the walls of traditional buildings while preserving unique characteristics and functionality. Advantages of adopting thermal mortars such as cork lime are put forward, and their potential efficacy is discussed, concluding with a case study focusing on the deep retrofit of a London mews within a conservation area. [ABSTRACT FROM AUTHOR]

This paper critically reviews the current building seismic design approach based on the observed performance of modern building stock in recent earthquakes, highlights the inability of the current design approach in controlling seismic damage and losses, and proposes a conceptual framework for next generation seismic design codes that is likely to meet public expectations. In addition to ensuring life-safety in rare earthquakes, the proposed loss-optimization seismic design approach also aims to ensure quick functional recovery and minimum loss (i.e. repair, downtime, and injury/fatality) in moderate-strong earthquakes by limiting damage to building's structural and non-structural components. Based on comparison of performances of building stock in some recent major earthquakes in different countries, the paper presents some simple strategies to render buildings more resilient and suffer significantly less seismic damage (and consequentially incur less loss). Finally, the paper scrutinizes the efficacy of some commonly used low-damage technologies in minimizing building seismic losses. [ABSTRACT FROM AUTHOR]

Due to the continuous introduction of new building technologies, research results, and technical specifications, the design and construction process of buildings has become extremely complex, paying more attention to the sustainable development of the building itself; it not only requires close communication between project participants, but also an in-depth analysis of building performance and structure is required during the design phase. Building Information Modeling (BIM) technology has been widely used in the development of the construction industry in recent years. It can effectively realize the integration of building information, so that all parties involved in the project can work together based on the same platform, which can more effectively improve work efficiency and save resource input, lower construction costs, and ultimately achieve the goal of sustainable development. On the basis of summarizing the previous research results, this paper uses BIM technology to analyze the optimization effect of large-scale construction engineering from the aspects of information model, resource allocation and technology optimization. The analysis results provide reference for subsequent in-depth research. [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]

The implementation of fluid viscous dampers has been proven as a feasible method to improve the seismic performance of buildings. However, the different variables involved in the damper's design affect the structural response, especially in terms of seismic demand on non-structural elements. This paper presents a parametric study on the floor acceleration response of three case study buildings equipped with fluid viscous dampers through nonlinear time-history analysis. The results show that the different design parameters of fluid viscous dampers significantly modify the floor acceleration response, with some damper configurations yielding floor accelerations larger than those of the buildings without dampers. [ABSTRACT FROM AUTHOR]

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]

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]

This paper evaluates the effectiveness of a novel capacity-design based retrofit for improving both the local and global behaviour of existing reinforced concrete buildings using fibre-reinforced polymers. The approach was previously tested on beam-column joints and is here extended to the building level. A new fibre-element model for the global retrofit is proposed and validated against full-scale experiments. Non-linear push-over analyses confirm the adequacy of the retrofit and fragility analyses are used to compare it to a simpler local retrofit. Finally, cost-benefit analyses for three levels of seismicity highlight the cost-effectiveness of the global retrofit for moderate to high seismicity. [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]

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]

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]

Although the emergence of deep learning has improved the performance of automatic building extraction, there is still a long way to go before it can completely replace the labour-intensive manual delineation of building contours. To narrow the gap between building extraction results of deep learning methods and manual-level annotations, this paper introduces an interactive semantic segmentation framework that uses manual clicks as interactive information to guide the process of semantic segmentation towards the manual annotation level. In our framework, we first use an interactive semantic segmentation network for coarse building extraction from high resolution remote sensing images. Then, we use an optimization network to further refine the extraction results. We comprehensively compare the automatic deep learning methods, the proposed interactive building extraction framework, and the full manual delineation in practical experimental settings. First, our interactive method can significantly improve the performance of automatic building extraction. Second, by comparing the efficiency of manual annotation using the ArcGIS software and our interactive method, it is found that the proposed method can save more than half of the time. The above results show the potential of the interactive method in improving the efficiency of contour annotation and reducing the cost of manual annotation. [ABSTRACT FROM AUTHOR]

Given the growing limelight on digital revolution and sustainability in the maritime industry, this paper aims to investigate the overlaps and interconnectedness of these two significant topics. Anchored on resource-based view and organisational ambidexterity theories, this study investigates the influence of key technical and social digital resources on exploratory and exploitative innovation capabilities and sustainable performance of maritime companies. This study also analyses the direct effects of innovation constructs and the indirect effects of digital resources on sustainable performance. Survey data were collected from 164 maritime professionals from various functions and sectors in the maritime industry. The structural equation modelling analysis revealed that all identified digital resources have a significant, positive correlation with innovation constructs, as well as a positive indirect effect on sustainable performance. Furthermore, as compared to exploitative innovation, exploratory innovation generates a greater positive effect on sustainable performance. This study enriches theoretical research by enhancing the understanding of resource-based view and organisational ambidexterity. It also explores the tensions between exploratory and exploitative innovation, as well as the definition of sustainable performance. It offers managerial implications by recommending how the management can utilise technical and social digital resources and innovation to improve sustainable performance via better long-term planning and corporate decisions. In addition, this study also enables firms to prioritise their allocation of resources where finances or manpower are scarce. • Impact of digital resources on exploratory and exploitative innovation are analysed. • Resource-based view and organisational ambidexterity theories are applied. • Redefines sustainable performance via ESG and triple bottom line frameworks. • Digital culture and digital leadership impact exploratory and exploitative the most. • Exploratory innovation exerts a greater positive effect on sustainable performance. [ABSTRACT FROM AUTHOR]

Purpose: This paper introduced the simple synthesis process of self-cleaning coating with fog-resistance property using hydrophobic polydimethylsiloxane (PDMS) polymer and nano-calcium carbonate (nano-CaCO3) and titanium dioxide (TiO2). Design/methodology/approach: The synthesis method of PDMS/nano-CaCO3-TiO2 is based on sol-gel process. The crosslinking between PDMS and nanoparticles is driven by the covalent bond at temperature of 50°C. The 3-Aminopropyltriethoxysilane is used as binder for nanoparticles attachment in polymer matrix. Two fabrication methods are used, which are dip- and spray-coating methods. Findings: The prepared coated glass fulfilled the requirement of standard self-cleaning and fog-resistance performance. For the self-cleaning test BS EN 1096-5:2016, the coated glasses exhibited the dust haze value around 20%–25% at tilt angle of 10°. For the antifog test, the coated glasses showed the fog haze value were below 2% and the gloss value were above 85%. The obtained results completely achieved the standard antifog value ASTM F659-06 protocol. Research limitations/implications: Findings will provide an infrastructure support for the building glass to enhance building's energy efficiency, cleaning performance and friendly environment. Practical implications: This study proposed the simple synthesis method using hydrophobic polymer and nano-CaCO3 and nano-TiO2, which can achieve optimum self-cleaning property at low tilt angle and fog-resistance performance for building glass. Social implications: The research findings have high potential for building company, cleaning building company and government sector. The proposed project capable to reduces the energy consumption about 20% per annum due to labor cost, time-consuming and safety during manual cleaning. Originality/value: The novel method to develop self-cleaning coating with fog-resistance using simple synthesis process and fabrication method for building glass application. [ABSTRACT FROM AUTHOR]

Automated extraction of polygonal building contours from high-resolution remote sensing images is important for various applications. However, it remains a difficult task to achieve automated extraction of polygonal buildings at the level of human delineation due to diverse building structures and imperfect image conditions. In this paper, we propose Line2Poly, an end-to-end approach that uses feature lines as geometric primitives to achieve polygonal building extraction by recovering topological relationships among these lines within an individual building. To extract building feature lines with precision, we adopt a two-stage strategy that combines Convolutional Neural Network (CNN) and transformer architectures. A CNN-based module extracts preliminary feature lines, which serve as positional priors for initializing positional queries in the subsequent transformer-based module. For polygonal building contour reconstruction, we devise a learnable polygon topology reconstruction module that predicts adjacency relationships among discrete lines, and integrates lines into building polygons. The resultant building polygons, based on feature lines, exhibit inherent regularity that aligns with manual labeling standards. Extensive experiments on the Vectorizing World Buildings dataset, the WHU aerial building dataset and the WHU-Mix (vector) dataset validate Line2Poly's impressive performance in building feature line extraction and instance-level building detection. Moreover, Line2Poly's predictions exhibit the highest level of concurrence with manual delineations, with over 83% agreement on the WHU aerial building test set and 68.7/59.7% on the WHU-Mix (vector) test set I and II, respectively. [ABSTRACT FROM AUTHOR]

Building simulation tools can empower architects to evaluate and optimize the performance of their designs. Therefore, it is essential to integrate building performance simulation modeling from an early design stage. A critical part of the simulated environment is the weather file. It is a compilation of 20–30 years of historical weather and hourly data on temperature, solar radiation, wind speed, and wind direction. The accuracy and contextual relevance of said data are of utmost importance to the integrity of the analysis. Current energy modeling weather files are unlikely to include any indication of risk from potential climate change. Hence, there is a need to incorporate climate change projections in energy simulations and prepare architects to understand better the evolution of design strategies echoing such climate change in different climate zones. This paper describes the process of using the CCWorldWeatherGen tool to morph Energy Plus Weather files of 17 North American cities in key climate zones into three simulated scenarios representing 2020, 2050, and 2080, corresponding to a 1.5°C global temperature rise. By simulating the impact of the morphed weather files on five commercial building models, this paper outlines a database echoing the projected climate change impacts within the selected locations and highlights the critical impact of passive design strategies in facilitating indoor thermal comfort while limiting energy consumption. [ABSTRACT FROM AUTHOR]

In developed countries, people spend nearly 90% of their time in buildings or during transportation. Recent research studies demonstrated that occupant behaviors have a significant impact on building performance in relation to the indoor environment and energy use. This paper presents the ASHRAE Global Occupant Behavior Database which aims to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems. This database includes 34 field-measured occupant behavior datasets for both commercial and residential buildings, contributed by researchers from 15 countries and 39 institutions covering 10 different climate zones. It includes occupancy patterns, occupant behaviors, indoor and outdoor environment measurements. The database is open source, a public website was developed for the users to interactively explore, query, and download datasets. This paper focuses on a detailed data analysis to investigate patterns of nine occupant behavior types, examining impacted factors such as building type, country, and climate zone. EnergyPlus simulations have been implemented based on the occupancy profiles derived from this database, and results showed overall building electricity consumption can be reduced up to around 27% in Summer and around 10% in Winter. [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]

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 wake of increasing global concern over environmental change and energy consumption, building energy conservation has emerged as a pivotal issue worldwide. The primary energy consumption in buildings stems largely from air conditioning and heating systems. The efficiency of these systems is significantly influenced by the thermal performance of the building's exterior wall insulation layers. In-depth research and optimized design of these insulation layers' thermal performance are thus critical topics in the field of building energy conservation. However, current research methods encounter limitations in addressing these issues, notably neglecting the impact of thermal bridges on the insulation layers' thermal performance and focusing solely on single parameters in insulation optimization design. This study addresses these challenges by conducting a comprehensive analysis and optimization design of the thermal performance of building exterior wall insulation layers. It includes an analysis of heat transfer through thermal bridges and the optimization of insulation parameters, aiming to provide a more holistic and systematic approach to optimizing the thermal performance of building exterior wall insulation layers. Specifically, this study uses thermal simulation software to conduct three-dimensional simulation of thermal bridges, analyzing the temperature distribution and heat flow in the thermal bridge area. According to international or domestic standards, the linear heat transfer coefficient of the thermal bridge is determined through calculation. By using optimization algorithms, the thermal performance parameters of the insulation layer are optimized in design. Through experiments, specific linear heat transfer coefficient and equivalent heat transfer coefficient of thermal bridges are obtained, as well as the extent of the impact of thermal bridges on overall thermal performance. Meanwhile, based on optimization algorithms, a set of materials and structural parameters that enable the building's external wall insulation layer to achieve higher thermal efficiency is obtained. After optimization, a lower heat transfer coefficient of the external wall insulation layer is achieved, improving the insulation performance of the wall. This paper provides a technical method for the analysis of heat transfer in thermal bridges and the optimization of insulation parameters. It helps architects more accurately assess and improve the thermal performance of building exteriors, which is significant for reducing energy loss and lowering operational costs of buildings. The research results can provide scientific basis for the formulation of building energy-saving standards, especially in terms of thermal bridge effects and external wall insulation performance. These standards are crucial in driving the industry towards higher energy-saving goals. [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]

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]

Recent earthquakes clarified that existing a soft/weak-story in a building could completely invert the failure mechanisms of the structure. Several studies were implemented to evaluate the potential risk subjected to the buildings under the earthquake hazard. However, these researchers discarded the effect of soft/weak-story on the demand parameters of their models. This paper presents a rapid demand estimation framework for generic buildings considering the effect of soft/weak-story. In this regard, the simplified model developed according to the HAZUS approach is rectified to apply the effect of soft/weak-story on the structural behavior of the generic buildings. Artificial neural networks are implemented to remove the required time-consuming nonlinear response history analyses from the post-earthquake actions. This research utilized a suite of 111 earthquake records, originally developed by the SAC project. These motions are uniformly scaled from 0:1g to 1:5g to obtain a generalized dataset with different intensities. Bayesian optimization algorithm is conducted to achieve a prediction model with optimized hyperparameters. Results clarify that the proposed method is reliable and computationally cost-effective in predicting the demand parameters of generic buildings. This framework can be used in the body of a risk assessment platform to facilitate the emergency response to the earthquake hazard. [ABSTRACT FROM AUTHOR]

The UK has some of the oldest buildings in Europe. They are also some of the worst performing in terms of energy performance. In tandem with these issues the UK has committed to making a substantial reduction in CO2 emissions. UK homes are currently responsible for almost 20 per cent of CO2 emissions. This leaves little option other than to make considerable progress with the retrofitting of homes to improve their energy performance. This is a technical process, however, and can introduce risks to building and their occupants. Examples exist of homes being retrofitted with disastrous consequences. Some of these issues can be due to the lack of thorough examination of a home before it undergoes a retrofit. This paper proposes a method that provides a detailed pre-retrofit assessment of a home, to fall in line with PAS 2035, a standard that provides guidance around publicly funded retrofit in the UK. [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]

Initial and maintenance costs often prevent dense submeter installations that enable room-level thermal energy monitoring. Previous studies suggested that building automation system (BAS) trend data represents an untapped potential to disaggregate existing meter data for heating and cooling into device- and system-level end-uses. These techniques disaggregate meter data by analyzing trend data that provide contextual information regarding the operating status of energy-consuming equipment. However, the level of submetering required to enable end-use disaggregation has yet to be studied. To this end, this paper investigates the effect of submeter density and configuration on the performance of a regression-based disaggregation strategy using BAS trend data as predictors. The method was evaluated in two steps; first, using synthetic meter and BAS trend data generated by a building performance simulation (BPS) model of a government office building, and second, with submeter data from a real office building. The results highlight the factors affecting the minimum number of heating energy submeters needed to be installed in both buildings for accurate device- and system-level disaggregation. The methodology presented in the paper can also inform changes in building design codes and standards regarding the minimum density and appropriate configuration for submetering. [ABSTRACT FROM AUTHOR]

We investigate a variety of aspects related to the simulation of passenger dynamics on escalators, mainly focusing on the discrepancy between the 'theoretical' and the 'practical' capacity that is observed for these facilities. The structure of the paper is twofold. In the first part, we introduce a space-continuous model to describe the transition of agents from walking on the plain to standing on the escalator. In the second part, we use numerical findings from simulations to study important measures like minimum distances between the standing agents and average occupancies of the escalator steps. One of the most important results obtained in this paper is a generalized analytical formula that describes the escalator capacity. We show that, apart from the conveyor speed, the capacity essentially depends on the time gap between entering passengers which we interpret as human reaction time. Comparing simulation results with corresponding empirical data from field studies and experiments, we deduce a minimum human reaction time in the range of 0.15s–0.30s which is in perfect agreement with results from social psychology. With these findings, it is now possible to determine accurately the relationship between the capacity and the speed of an escalator, allowing a science-based performance evaluation of buildings with escalators. [ABSTRACT FROM AUTHOR]