ABSTRACT With the growing demand for smart buildings, optimizing shading adjustments in office environments is crucial for enhancing energy efficiency and occupant comfort. This study presents a dual-layer model combining machine learning and Bayesian methods to predict shading adjustment behavior in office buildings, addressing the complexity often overlooked by previous approaches. Using long-term monitoring data of indoor and outdoor environmental parameters, occupant presence, and shading statuses, the model categorizes windows into three types—far-range, midrange, and close-range—based on the complexity of adjustments, and trains models for each category. Sensitivity analysis identifies key factors influencing model performance. Multiple algorithms, including Decision Tree, Random Forest, Gradient Boosting, Multilayer Perceptron, and K-Nearest Neighbors, were evaluated through ROC curve analysis, with Random Forest emerging as the most suitable. The model’s performance, validated through cross-validation, confusion matrices, and various metrics, demonstrates high accuracy in predicting shading behavior. The findings underscore the potential of integrating machine learning and Bayesian models to improve shading control in smart buildings, offering a robust tool for enhancing energy management and occupant comfort.

ABSTRACT Passive evaporative cooling systems offer a low-cost, energy-efficient solution suited to hot and dry climates. This study integrates evaporative cooling within a shading system, creating an innovative approach to control daylight while enhancing thermal comfort. Silica gels are inserted into the louvers and placed in a water chamber, turning the louvers into wet evaporating plates. This evaporative shading system (ESS) is implemented on the inside-facing window opening, resulting in an integrated system for evaporative cooling and daylight control. A numerical model simulates variations in indoor air temperature and humidity caused by the system, which is further validated through experimental testing. Numerical modeling indicates that after 100 minutes at a height of 70 cm, the shading system successfully reduces the temperature by a range of 3.5 K while concurrently increasing relative humidity by approximately 3.5%. Subsequent experimental tests support these findings, demonstrating that the ESS reduces temperature by about 5 K and increases relative humidity by a range of 2 to 3.5%. This ESS demonstrates potential for thermal comfort enhancement and daylight optimization.

ABSTRACT Office buildings require high-quality indoor environments, but improving indoor environment quality often leads to increased energy consumption, especially in harsh climates. While daylight can enhance lighting quality, relying solely on it may not be practical due to its variability and control difficulties. Integrating daylight with artificial lighting can create a better visual environment and reduce energy consumption. However, selecting the appropriate windows for optimal visual and energy performance remains challenging for designers. This paper proposes a design-aided tool that assists designers in selecting energy-efficient windows by integrating daylighting with artificial lighting in office buildings in hot-humid climates. The tool considers different window areas, heights, and glazing types. It is based on a theoretical modelling approach that formulates an integrated base case model comprising thermal and lighting models using the Design-Builder program. Results showed that integrating daylighting with artificial lighting can lead to 12–19% total energy savings. The design-aided tool is presented in tables and graphs, enabling designers to find the expected energy savings for specific window design parameters or identify the ideal window design with the highest energy savings.

ABSTRACT Buildings play a significant role in global energy consumption, making it crucial to understand the interplay between energy usage and occupant behavior. While behavior-centric approaches to building sustainability are widely advocated, the incorporation of human behavior in Performance-Based Design (PBD) remains underexplored. This study aims to develop a co-simulation model to evaluate the impact of various retrofit strategies for residential building envelopes, taking into account realistic and stochastic occupancy schedules and diverse occupant behavior patterns. To achieve this objective, a hybrid approach was employed, involving surveys, classification, clustering processes, and a co-simulation framework that integrates Rhino Grasshopper (GH) and Python. Within this framework, custom Python components were used to implement agent-based modeling (ABM), enabling the representation of occupant agents with distinct behavioral rules, awareness levels, and interaction patterns. The results reveal significant differences in the influence of customized occupancy schedules on the effectiveness of retrofit strategies, highlighting the variability in energy savings potential based on distinct energy consumption patterns and behaviors. For example, Energy Use Intensity (EUI) analysis showed reductions of 55.40%, 68.90%, and 94.10% compared to the base case (210.368 kWh/m 2 /year) under three representative stochastic occupancy scenarios (A1, A2, and A3). While a particular retrofit strategy may yield positive outcomes under one occupancy scenario, it may be less effective or even counterproductive under another. These findings underscore the importance of incorporating detailed occupancy schedules, energy consumption patterns, behaviors, and awareness rates in retrofitting buildings and conducting passive building designs to maximize energy savings potential. This research contributes to the field by demonstrating the critical role of occupant behavior in optimizing retrofit strategies for residential buildings.

ABSTRACT The circular economy (CE) paradigm, with its focus on sustainability and resource efficiency, is increasingly being recognized for its applicability to cultural heritage management. This study bridges the research gap by systematically analyzing 1015 articles from the Web of Science database, revealing the potential of CE principles to enhance the longevity of heritage assets and minimize resource use, particularly through adaptive reuse. The policy integration of cultural heritage within the “Circular City” framework underscores its role in sustainable urban development. Methodologically, adaptive reuse and life-cycle assessment (LCA) are identified as pivotal for assessing environmental and economic benefits within a CE framework. The study also advocates for integrated evaluation systems, such as multi-criteria decision analysis, to navigate the complex objectives of sustainable heritage management. Furthermore, the thematic evolution analysis revealed a clear progression in the research focus from broad concepts such as “landscape” to specific entities such as “buildings,” and a paradigm shift from foundational explorations to a more systematic, policy-driven, and methodologically mature stage. Future research is urged to employ tools like LCA, Life Cycle Costing (LCC), and Social LCA (S-LCA) for a holistic evaluation of cultural heritage projects, fostering interdisciplinary collaboration and new governance models that leverage cultural heritage as a catalyst for circular innovation. Stakeholder engagement is emphasized to integrate diverse perspectives and enable effective decision-making, while the use of emerging technologies, such as digital twins and historical building information modeling, is suggested to preserve cultural heritage sites.

ABSTRACT In the United States, building energy codes (BECs), specifically the International Energy Conservation Code (IECC), are a primary policy tool for limiting the environmental impact of buildings and advancing decarbonization of the building sector. Historically, BECs do this by regulating and limiting operational energy consumption during the use phase, on the assumption that reductions in energy use equate to reductions in carbon emissions. Following years of incremental improvements, the revisions in the 2021 IECC are expected to generate 9–14% reductions in operational energy use. However, the full carbon impact of buildings includes both operational emissions and embodied emissions, and there is clear evidence that embodied emissions comprise a significant proportion of this total carbon impact. This is particularly true for high-performance buildings with reduced energy use. Despite the ostensible goal of reducing carbon emissions, the IECC does little to explicitly address embodied carbon, and there is a comparative lack of research on the embodied carbon impacts of recent revisions to BECs and the IECC. Using a prototypical single-family residence as a case study, this paper aims to address this gap by assessing the potential embodied carbon impact of compliance with contemporary BECs. The paper finds that the material choices in the exterior envelope can have a significant impact on the carbon use intensity of code compliant residential buildings. In some cases, up-front material carbon emissions account for upwards of 40% and 20% of total carbon use intensity across critical 10- and 30-year timeframes, respectively. Moreover, the analysis shows that low-carbon assemblies that do not meet the current BECs can actually result in lower carbon use intensity over these critical time frames. This paper concludes by arguing that there are relatively simple regulatory options for substantially reducing embodied carbon in code compliant buildings.

ABSTRACT The construction sector consumes more than a third of the world's energy, and digital transformation has the potential to increase productivity and reduce energy consumption. Digital Twin (DT) is an important way to reduce the construction industry's huge carbon emissions. However, the current research stays in the application of the whole building industry, and the research of using DT to contribute to energy saving and carbon reduction in buildings is still in the exploratory period. Therefore, this paper proposes a DT-LCR (Digital Twin-enabled Lifecycle Carbon Reduction) conceptual framework by systematically organizing 427 related academic papers to promote a wider implementation of DT for energy saving and carbon reduction in the whole lifecycle of buildings, and clarifies the specific future development directions for stakeholders: “Energy simulation to optimise design decisions,” “Real-time monitoring enables smart construction,” “Real-time detection enables dynamic operation and maintenance,” and “Information integration saves resource scheduling.”

ABSTRACT NaOH was used as a solid alkali initiator for fly ash eco concrete. The porosity, water permeability, compressive strength, pH, vegetative and microscopic properties of eco concrete with different fly ash contents were studied. The results show that the activation effect is best when the solid NaOH doping is 2% of fly ash. Under this condition, the 28d compressive strength of alkali-activated fly ash eco concrete was the highest when the fly ash content was 17%, reaching 19.15 Mpa, and the mechanical properties were effectively improved, its permeability and porosity meet the engineering application standards. The surface porosity of each section is similar to the law of total porosity, and the size and number of pores are suitable for plant growth. Manila grass has better alkali tolerance and is more affected by pore size. At 43d, the plant height, root length, and LRWC grown in FA0% and FA7% were the best, and the plant height could reach up to 23 cm. In addition, through SEM and EDS analysis of the sample, it was found that the amorphous product of the sample was mainly N(C)–(A)-S-H gel, which can form an interconnected spatial network structure and has strong bonding ability.

ABSTRACT As the most popular form of major trans-regional infrastructure, the expansion of high-speed railway (HSR) has resulted in some inequities, such as imbalanced development between regions and apparent gaps between social groups. This paper innovatively proposes the concept of “end-to-end equality (ETEE)” to comprehensively evaluate the equity of the whole process of public access to HSR services from three chronological phases: equality of opportunity, procedural equality, and equality of outcome. Subsequently, the impact pathways between the three are analyzed by means of structural equation modeling, using the Xiamen–Shenzhen high-speed railway (XS HSR) as an example. The following main conclusions are then drawn: For one thing, all three stages have prominent positive influences on the ETEE of HSR, where equality of outcome has the largest influence and equality of opportunity has the smallest. For another, procedural equality influences equality of outcome, whereas equality of opportunity is influenced weakly, if at all. It is revealed that procedural equality and equality of outcome are the key points for realizing end-to-end equality of China's HSR, which makes an important implication for the construction and operation of high-speed railway and provides a comprehensive assessment framework for the ETEE of major trans-regional infrastructures.