Advanced Building Research Articles

Quantifying and simulating the weather forecast uncertainty for advanced building control

Weather forecast uncertainty is unavoidable despite technological advancements. Accurately quantifying and modelling this uncertainty is essential for developing and comparing advanced building controllers. In this study, we present a structured approach using a first-order autoregressive model (AR(1)) to model uncertainty in ambient temperature and global solar irradiation (GHI) forecasts. We analyzed weather data from four cities and employed Jensen–Shannon divergence (JSD) to evaluate the similarity between synthetic and actual forecast errors. The average JSD values for temperature are 0.027 (Berkeley), 0.021 (Leuven), 0.018 (Berlin), and 0.008 (Oslo), and for GHI, the average JSD values are 0.016 (Berkeley), 0.058 (Leuven), and 0.013 (Berlin). The low JSD values indicate a high similarity between the synthetic and real forecast error distributions. Our approach successfully generates synthetic weather forecasts that mirror the statistical properties of actual forecasts. The implementation of our method for uncertain forecast generation is being added to the BOPTEST framework.

The Development of an Advanced Facade Map: An Evolving Resource for Documenting Case Studies

This paper describes the creation and the potentials of an online tool to identify and document case studies that demonstrate perceived best practices in the design and implementation of advanced, sustainable, and climate-responsive integrated buildings facades. The project was created to catalog these projects in sufficient detail to allow users—expected to include design professionals, students, and faculty—to discover and study relevant examples, based on key project features, defined by the authors as technologically advanced and worthy of relevance: daylight and solar control, natural ventilation, noise control, embodied carbon, energy generation, and innovative insulation systems. The website documenting 44 case study buildings and this paper provides a preliminary overview about how it was made, what it is, and what some potential uses of the tool might be. This study emphasizes adaptability across climates, showcasing sustainable facades designed to balance energy efficiency with occupant comfort. This study also shows how data can be analyzed through the Map, based on four case studies. Presenting these statistics, the resource offers a foundation for exploring facade technologies that support sustainable building practices and respond effectively to climate-specific challenges. In doing so, the authors aim to inspire further exploration of innovative facade solutions within the context of sustainable building practices.

Physics-consistent input convex neural network-driven reinforcement learning control for multi-zone radiant ceiling heating and cooling systems: An experimental study

Radiant ceiling heating and cooling system is a technology used for space heating and cooling. Owing to the variable weather conditions, occupant behavior, and thermal lag of the system, it is challenging to design a control strategy to reduce air-conditioning energy consumption while maintaining the thermal environment. This study is the first pilot implementation of a physics-consistent input convex neural network (PCICNN)-driven reinforcement learning (RL) approach for real-world multi-zone radiant ceiling heating and cooling systems. A multi-zone PCICNN based on a graph neural network (GNN) was developed to simulate the zone temperature. The radiant panel load was simulated using the physics-based ε-NTU method. The PCICNN-driven RL agent was based on the soft actor-critic algorithm and trained in an environment model comprising the PCICNN and ε-NTU models. The proposed controller was deployed in real-time on one floor of an office building for one month. The real-world implementation showed that the proposed PCICNN-driven RL control can reduce the radiant panel cooling load by up to 33% compared with the inherited baseline control strategy under similar weather conditions. This study provides a comprehensive demonstration of real-world data-driven building controls and leverages future research on advanced building control.

Palladium‐Catalyzed Four‐Component Carbonylation Reactions of Acetylene: Synthesis of β‐Perfluoroalkyl Acrylamides

AbstractThe simplest alkyne, acetylene, is a suitable C2 linker synthon that enables the connection of organic molecules with different functional groups. However, reactions of acetylene with advanced organic building blocks are much less explored compared to substituted alkynes. In this article, we present a straightforward palladium‐catalyzed four‐component carbonylation reaction with acetylene, CO, amines, and perfluoroalkyl halides that enables the preparation of various β‐perfluoroalkyl acrylamides in one step. This protocol also provides a new strategy for the late‐stage modification of bioactive molecules, as demonstrated by the construction of β‐perfluoroalkyl ibrutinib derivatives.

Enhanced Hybrid U-Net Framework for Sophisticated Building Automation Extraction Utilizing Decay Matrix

Automatically extracting buildings from remote sensing imagery using deep learning techniques has become essential for various real-world applications. However, mainstream methods often encounter difficulties in accurately extracting and reconstructing fine-grained features due to the heterogeneity and scale variations in building appearances. To address these challenges, we propose LDFormer, an advanced building segmentation model based on linear decay. LDFormer introduces a multi-scale detail fusion bridge (MDFB), which dynamically integrates shallow features to enhance the representation of local details and capture fine-grained local features effectively. To improve global feature extraction, the model incorporates linear decay self-attention (LDSA) and depthwise large separable kernel multi-layer perceptron (DWLSK-MLP) optimizations in the decoder. Specifically, LDSA employs a linear decay matrix within the self-attention mechanism to address long-distance dependency issues, while DWLSK-MLP utilizes step-wise convolutions to achieve a large receptive field. The proposed method has been evaluated on the Massachusetts, Inria, and WHU building datasets, achieving IoU scores of 76.10%, 82.87%, and 91.86%, respectively. LDFormer demonstrates superior performance compared to existing state-of-the-art methods in building segmentation tasks, showcasing its significant potential for building automation extraction.

Comprehensive Review of the Advancements, Benefits, Challenges, and Design Integration of Energy-Efficient Materials for Sustainable Buildings

Energy-efficient materials are essential in buildings to reduce energy consumption, lower greenhouse gas emissions, and enhance indoor comfort. These materials help address the increasing energy demand and environmental impact of traditional construction methods. This paper presents a comprehensive literature review that explores advanced materials and technologies for improving building energy efficiency, sustainability, and occupant comfort. The study applies a comparative analysis of peer-reviewed research to examine key technologies analyzed include building-integrated photovoltaics, advanced insulating materials, reflective and thermal coatings, glazing systems, phase-change materials, and green roofs and walls. The study highlights the significant energy savings, thermal performance, and environmental benefits of these materials. By integrating these technologies, buildings can achieve enhanced energy efficiency, reduced carbon footprints, and improved indoor comfort. The findings underscore the potential of advanced building materials in fostering sustainable construction practices. The methodology of this review involves collecting, analyzing, summarizing, comparing and synthesizing existing research to draw conclusions on the performance and efficiency of these technologies.

Navigating the transition: Modelling the path for net-zero European building sector

Transition towards a net-zero building sector offers substantial potential to mitigate climate change impacts and thus plays a pivotal role in meeting the goals set forth in the Paris Agreement. Stakeholders often use mathematical models to understand the true potential for mitigating emissions by reducing the energy demand of the building sector. However, the existing models rarely provide insights into net-zero pathways for both the residential and tertiary buildings as this is a complex sector to calculate the future pathways towards net-zero. Hence, uncertainties in the associated policy decisions persist. Therefore, this study uses the High-Efficiency Building (HEB) energy model to calculate the energy demand reduction potential as well as to analyze the net-zero feasibility of the building sector for each of the European Union Member States. This research uses four scenarios, which are designed to represent scenario-specific levels of commitment the European Union Member States regarding the application of different energy efficiency measures, to provide insights into the consequences of today's policy decisions by 2060. The findings of this study show that energy demand of the building sector can be reduced substantially and can even become nearly net-zero by 2060. Based on the demand reduction potential of the building sector, it can be concluded that the building renovation rate and construction of new advanced buildings not only provides unprecedented potential for reducing energy demand but also paves the way for reaching climate neutrality goals.

Advancing Thermochromic Glass Durability: Reinforcing Thermosensitive Hydrogels with Enhanced Adhesion Techniques

The growing use of glass in architecture has driven research into reducing its energy consumption. Thermochromic (TC) glass technology shows promise for enhancing building energy efficiency by regulating solar heat dynamically. Although TC glass helps reduce heat radiation, additional solutions like Low-E or vacuum glass are needed to control heat convection and conduction. Low-E glass, while effective in lowering heat transfer, may increase surface temperature. Thermo-sensitive hydrogels, known for their light-scattering properties at high temperatures, have been explored to complement TC glass. However, their stability at elevated temperatures remains a challenge, especially for applications requiring durability under varying weather conditions. This study proposes enhancing the adhesion between hydrogel and glass by introducing silica–oxygen bonds. As a result, TC glass demonstrates stable performance over 100 cycles within temperature ranges from 85 °C to 30 °C in summer and 40 °C to −20 °C in winter. Furthermore, by incorporating ethylene glycol, the freezing point of TC glass is reduced to −26 °C, rendering it suitable for use in colder regions. The implementation of TC glass effectively addresses the dual requirements of summer shading and winter heating in areas with both cold winters and hot summers, significantly reducing building energy consumption. This study contributes substantially to developing advanced intelligent building materials, paving the way for more sustainable architectural designs.

Occupational Heat Exposure-related Symptoms Prevalence and Associated Factors Among Hospitality Industry Kitchen Workers in Ethiopia: Wet Bulb Globe Temperature

BackgroundOccupational heat stress caused by excessive environmental heat gain disrupts thermoregulatory mechanisms and harm workers' health and productivity. Kitchens are known for their thermal risks; however, research on heat stress in kitchen is limited. This study aimed to bridge this knowledge gap by assessing, the prevalence of heat-stress-related symptoms, and associated factors among kitchen workers in Gondar City, Ethiopia. MethodsThis institutional-based cross-sectional study (April to June 2023) evaluated heat stress among hospitality kitchen workers in Ethiopia. Heat exposure was measured using hygrometers. A simple random sample of 605 participants completed a survey and data was exported to Statistical Package for Social Science version 26. To assess strength and direction an adjusted odds ratio with 95% confidence interval (CI) was employed. A p-value of less than 0.05 was utilized to identify significant associations. ResultsOver the last 6 months 67.1% (95% CI: 63.0, 71.1), of the participants reported heat-stress symptoms. Multivariable analysis revealed that age ≥40 years [AOR: 2.28; 95% CI (1.08, 4.82)], high workload [AOR: 1.89; 95% CI (1.04, 3.49)], poor heat mitigation practice [AOR: 2.39; 95% CI (1.58, 3.59), wood fuel [AOR: 2.60; 95% CI (1.54, 4.40)], improper ventilation [AOR: 3.28; 95% CI (1.56, 6.87)], and higher heat index value [AOR: 2.15; 95% CI (1.35, 3.42)] were factors significatly associated with heat stress related symptoms. ConclusionThis study identified a high prevalence of heat–stress-related symptoms among kitchen workers. Mitigation strategies include improved ventilation, cooling, advanced building designs, and heat reduction technologies. Future research should utilize standard heat-stress assessment tools.

Coupling parameter analysis of photovoltaic double skin façade targeting photovoltaic etching ratio and cavity depth

The photovoltaic double skin façade (PV-DSF) represents the frontier of the advanced building envelope, whereas the prior parameter analysis had predominantly focused on the influence of individual factors. As two essential factors of PV-DSF, the photovoltaic etching ratio ϑ and cavity depth Dcav have garnered considerable academic attention, with little of them focusing on their coupling effect to energy performance. This paper investigates the impact of ϑ and Dcav using a spectrum-resolution numerical model, incorporating the concept of elementary effects derived from Morris method to quantify their interaction. The findings reveal that ϑ exhibits a non-monotonic impact, whereas the influence of Dcav is monotonic but non-linear. The optimal ϑ is found to be within the range of 30–50 %, while the lowest Dcav mostly outperforms other values, contrary to previous studies. This deviation is speculated to arise from the consideration of hot air generation energy. Furthermore, the variation of one factor can potentially alter the optimal value of the other. The second-order elementary effect between them demonstrates that their coupling effect is also non-linear and non-monotonic, with an impact as significant as that of Dcav alone. Overall, the correlations between ϑ and Dcav should be thoroughly considered during the design period.

Building extraction from remote sensing imagery: advanced squeeze-and-excitation residual network based methodology

Extracting buildings from remote sensing imagery (RSI) is an essential task in a wide range of applications, such as urban and monitoring. Deep learning has emerged as a powerful tool for this purpose, and in this research, we propose an advanced building extraction method based on SE-ResNet18 and SE-ResNet34 architectures. These models were selected through a rigorous comparative analysis of various deep learning models, including variations of residual networks (ResNet), squeeze-and-excitation residual networks (SE-ResNet), and visual geometry group (VGG), for their high performance in all metrics and their computational efficiency. Our proposed methodology outperformed all other models under consideration by a significant margin, demonstrating its robustness and efficiency. It achieved superior results with less computational effort and time, a testament to its potential as a powerful tool for semantic segmentation tasks in remote sensing applications. An extensive comparative evaluation involving a wide range of state-of-the-art works further validated our method’s effectiveness. Our method achieved an unparalleled intersection over union (IoU) score of 88.51%, indicative of its exceptional accuracy in identifying and segmenting buildings within the Wuhan University (WHU) building dataset. The overall performance of our method, which offers an excellent balance between high performance and computational efficiency, makes it a compelling choice for researchers and practitioners in the field.

The co-evolution of sustainable finance stakeholders under the EU taxonomy for sustainable activities: an exploratory study of Irish disclosure experiences

PurposeIn pursuit of objectives, under the European Green Deal, to channel capital flows to sustainable activities, the EU Taxonomy offers clarity, labelling real economic activities as “sustainable”, based on technical screening criteria. This study of disclosure experiences aims to explore the role of co-evolutionary relationships in the Taxonomy’s effectiveness.Design/methodology/approachCo-evolution theory implies a dynamic interplay among sustainable finance stakeholders (SFSs), through adjustment to, impact on and operationalisation of the Taxonomy. Corporate disclosure experiences, including those of financial institutions and related SFS experiences, may reveal co-evolutionary processes. With significant Undertakings for Collective Investment in Transferable Securities (UCITS) and Alternative Investment Funds (AIFs), Irish SFSs provide contextual insight. Semi-structured interviews with a purposive sample of Irish SFSs capture inaugural corporate Taxonomy disclosure experiences.FindingsA thematic analysis reveals six co-evolutionary processes that facilitate Taxonomy implementation in pursuit of policy objectives: [1] cross-functional reporting; [2] iterative pre-empting and addressing compliance issues; [3] regulation as a catalyst for co-evolution; [4] advanced capacity building; [5] stakeholder adaptation and [6] graduated use of ESG data. Implications for sustainability policy development and management are significant.Practical implicationsWhilst limited to just one EU jurisdiction, given limited prior empirical evidence for sustainable finance regulations from co-evolutionary perspectives, this study highlights a catalytic, yet precautionary role for co-evolution in their transformation effectiveness. As such, they must take account of their potential to stimulate co-evolution and to nurture it in pursuit of their policy objectives.Social implicationsThe findings of this study add to a small, but growing body of academic literature on the Taxonomy Regulation, which suggests that a co-evolutionary lens is important for gaining a comprehensive understanding of its early-stage dynamics. From an implementation perspective, the qualitative data reveals actionable implications for regulators and policymakers, such as building capacity, better anticipation of outcomes and investment in data infrastructure.Originality/valueUnlike existing analyses of disclosures, this study offers a co-evolutionary lens on Taxonomy contributions to sustainable development through qualitative accounts.

Adoption of Advanced Building Technologies and Materials in Construction Project Management

Adoption of Advanced Building Technologies and Materials in Construction Project Management

How to improve efficiency for prefabricated component production scheduling with worker allocation and order outsourcing?

Prefabricated building is an advanced building mode that is actively promoted in China owing to its high-efficiency, energy-saving and labour-saving characteristics. However, because of the limited production capacity and production resources of the final assembly parts production plant, it is usually impossible for a factory to accept all orders. Therefore, the factory will consider outsourcing part of the orders to reduce delay penalties. In addition, the allocation of workers in the production process will significantly affect the processing time of orders. Regarding those two issues, a production scheduling integrated optimization model, considering worker allocation and order outsourcing, is established to maximize net profit. To address the complexity of problem solving caused by the NP-hard nature of this problem, a hybrid improved genetic–accelerated iterative greedy search algorithm is developed. Finally, a numerical example is delivered to verify that the proposed algorithm has improved solution quality and convergence.

Long-term experimental evaluation and comparison of advanced controls for HVAC systems

The tremendous energy usage from buildings leads to research studies on their improvement, among which advanced building control plays an important role. In advanced building controls, data-driven predictive control (DDPC), differentiable predictive control (DPC), and reinforcement learning (RL) have shown advantages, but their comparison often lacks in existing studies. The simulation-based prior comparison studies have inconsistent results due to different assumptions and simplifications. Therefore, to comprehensively compare the three advanced strategies for real-time building HVAC controls, we implemented DDPC, specifically, hierarchical DDPC (HDDPC), DPC, and RL in a real building testbed for more than 5 months. The results show that all three advanced controls maintained the indoor environmental quality (IEQ) cost-effectively. Overall, HDDPC outperformed the baseline control with more than 50% energy savings, followed by RL with 48%, and DPC with 30.6%. Most control failures were related to API communication issues. Besides, the information gaps between room and system level controllers and non-optimal control decisions will degrade HDDPC's performance. Such degradation did not happen in DPC and RL, which led to better performance of agent-based control over HDDPC. Moreover, HDDPC needs minutes to make control decisions whereas DPC and RL need milliseconds, indicating higher online computing resources required by HDDPC. For agent training, DPC is faster than RL, as DPC training needs minutes and RL needs hours, but its performance is not as good as RL. This study provides a comprehensive understanding and assessment of the pros and cons of advanced building controls and sheds light on future research on building controls.

Solar-driven adaptive radiative cooling coating with polymer carbon dots-enhanced photoluminescence for urban skin

Adaptive passive daytime radiative cooling coating adjusts cooling capacity on an as-needed basis, making it an advanced zero-energy building cooling technology for urban skin. Currently, research mostly focuses on temperature-driven mechanisms, constrained by the limited availability of phase change materials. To address this challenge, this study pioneers the fabrication of a Solar-driven Adaptive Radiative Cooling (SARC) coating, which can adjust cooling capacity based on solar irradiance. To achieve this objective, the polymer Carbon Dots (CDs), PEI/Poly(St-co-BA)@CDs, are innovatively incorporated in SARC coating as the photoluminescent materials to convert the absorbed solar energy into light energy. Notably, the polymer-based CDs enable the design of Smart Cooling Beads (SCBs) whose surfaces are hydrophilic, making it easier to get dissolved in aqueous solutions. Moreover, the CDs were evenly distributed on the surfaces of SCBs, allowing it to achieve a higher quantum yield, recorded as 33.6 %. Illustrated with an aqueous coating example for demonstration, the effective solar reflectance of developed SARC coating increases from 92.5 % to 95 % under solar radiation. Furthermore, the adaptive cooling performance (1.5 °C to 3.2 °C lower than static coatings) varies with solar radiation (from 220 W. m−2to 880 W. m−2), demonstrating an energy efficiency improvement. These findings validate the SARC coating as a simple, feasible, environmentally friendly, and scalable solution for the advancement of next-generation smart urban skin.

Tri-phase flame retardant system towards advanced energy-saving building materials with highly efficient fire and smoke toxicity reductions

The extensive usage of rigid polyurethane foam (RPUF) as an energy-saving building materials in construction has raised concerns about fire risks and toxic hazards. To address these challenges, a melamine-derived polyol (MADP) was synthesized using the Mannich reaction, resulting in inherently flame-retardant RPUF materials with the introduction of flame-retardant functional groups. Furthermore, a novel MoS2-Cu2O nanohybrid was prepared and employed to enhance flame retardancy and suppress smoke toxicity in RPUF through a cooperative effect with expandable graphite (EG). The resulting M-RPUF/EG/MoS2-Cu2O nanocomposite exhibited remarkable fire safety, as evidenced by significant reductions in peak heat release rate (PHRR), total heat release (THR), total smoke production (TSP), and peak carbon monoxide (CO) release, amounting to 76.9 %, 64 %, 82 %, and 99 % respectively, compared to pristine RPUF. Furthermore, the nanocomposite effectively addressed interfacial defects and significantly enhanced the mechanical property and thermal stability. The significant improvement in fire safety is mainly ascribed to the catalytic charring effect of MADP and EG, as well as the catalytic oxidation of MoS2-Cu2O nanohybrids, resulting in the formation of a thermally stable protective layer in the condensed phase. Based on these results, the integration of the tri-phase flame retardant system holds great potential for advancing the development of fire-safe polyurethane materials.

Effectiveness evaluation of Construction 4.0 technologies in improving built environment resilience

PurposeBuilt environments are highly vulnerable to climatic disasters such as extreme floods, droughts and storms. Inaccurate decisions in adopting emerging construction technologies can result in missed opportunities to improve the resilience of built environments. Therefore, understanding the effectiveness of emerging construction technologies in improving built environment resilience can help in making better strategic decisions at the national and organizational levels. This study aims to evaluate the effectiveness of Construction 4.0 technologies in improving built environment resilience.Design/methodology/approachA list of Construction 4.0 technologies was adopted from a national strategic plan. Then, the data were collected using the fuzzy technique for order preference by similarity to ideal solution technique from selected built environment experts to determine the relative effectiveness of Construction 4.0 technologies in improving built environment resilience.FindingsSix Construction 4.0 technologies are critical in improving built environment resilience (in rank order): building information modeling, autonomous construction, advanced building materials, big data and predictive analytics, internet of Things and prefabrication and modular construction. In addition, adopting Construction 4.0 technologies collectively is crucial, as moderate to strong connections exist among the technologies in improving built environment resilience.Originality/valueTo the best of the authors’ knowledge, this is one of the first papers that evaluate the effectiveness of Construction 4.0 technologies in improving built environment resilience. Industry professionals, researchers and policymakers can use the study findings to make well-informed decisions on selecting Construction 4.0 technologies that improve built environment resilience to climatic disasters.

High-support Mould Construction and Risk Assessment in Civil Engineering Works

With the rapid development of the construction industry, advanced construction technology and new building materials continue to emerge. High-supporting mould technology, with the advantages of large bearing capacity, convenient erection and strong applicability, has been widely used in the field of construction engineering. However, due to the high height, large span, many rods, complex and variable force and other characteristics of the highly supported mould frame, there is a risk of tilting or collapsing in the process of building construction, which has a very high risk. This paper from the high supporting mould technology, frame erection technology key points, safety monitoring and evaluation of the detailed description, and targeted to put forward the corresponding reasonable suggestions, in order to be able to protect the smooth progress of construction.

Investigating Advanced Building Envelopes for Energy Efficiency in Prefab Temporary Post-Disaster Housing

Prefabricated temporary buildings are a promising solution for post-disaster scenarios for their modularity, sustainability and transportation advantages. However, their low thermal mass building envelope shows a fast response to heat flux excitations. This leads to the risk of not meeting the occupant comfort and HVAC energy-saving requirements. The literature shows different measures implementable in opaque surfaces, like vacuum insulation panels (VIPs), phase change materials (PCMs) and switchable coatings, and in transparent surfaces (switchable glazing) to mitigate thermal issues, like overheating, while preserving the limited available internal space. This paper investigates the energy and overheating performance of the mentioned interventions by using building performance simulation tools to assess their effectiveness. The optimization also looks at the transportation flexibility of each intervention to better support the decision maker for manufacturing innovative temporary units. The most energy-efficient measures turn to be VIPs as a better energy solution for winter and PCMs as a better thermal comfort solution for summer.