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As part of Sweden's commitment to carbon neutrality, various municipalities have established energy efficiency targets. Achieving these targets requires decision-making knowledge at the local level, particularly concerning the energy retrofitting of existing building stocks. It includes understanding of the thermal properties (U-values) of building stock, their impact on energy use, and the potential energy retrofits. Our research focuses on assessing the thermal properties performance and their impact on energy use of residential building stocks in Umeå, Sweden. We employ explainable artificial intelligence (XAI) integrated with machine learning regression framework to elucidate how different building thermal features influence the building's energy use and also the correlations among these features. The findings highlight the significant impact of building floor area on energy use, followed by location, age, etc. Among thermal properties, the exterior walls have high impact and attic floor has the lowest impact on the energy use of Umeå's residential building stock. Ultimately, this study provides municipality-level decision-making insights for planning energy retrofitting initiatives for Umeå building stock.

Characterizing the energy performance of building components and locating anomalies is necessary for effectively refurbishing existing buildings. It is often challenging because defects in building envelopes deteriorate without being visible. Passive infrared thermography (PIRT) is a powerful tool used in building inspection. However, thermal image interpretation requires significant domain knowledge and is prone to artifacts arising from a complex interplay of factors. As a result, PIRT-based inspections require skilled professionals, and are labor-intensive and time-consuming. Artificial intelligence (AI) holds great promise to automate building inspection, but its application remains challenging because common approaches rely on extensive labeling and supervised modeling. It is recognized that there is a need for a more applicable and flexible approach to leverage AI to assist PIRT in realistic building inspections. In this study, we present a label-free method for detecting anomalies during thermographic inspection of building envelopes. It is based on the AI-driven prediction of thermal distributions from color images. Effectively the method performs as a one-class classifier of the thermal image regions with a high mismatch between the predicted and actual thermal distributions. The algorithm can learn to identify certain features as normal or anomalous by selecting the target sample used for training. The proposed method has unsupervised modeling capabilities, greater applicability and flexibility, and can be widely implemented to assist human professionals in routine building inspections or combined with mobile platforms to automate the inspection of large areas.

To enhance on-site safety management, detecting near-misses in a timely and accurate manner is crucial. However, traditional monitoring methods that rely on inspectors are time-consuming, labor-intensive, and limited in their ability to detect three-dimensional (3D) incidents. Digital twin technology presents a promising solution by integrating real-time monitoring, simulation and interactivity capabilities in three dimensional virtual environments. Therefore, we propose a real-time digital twin-driven near-miss detection system to facilitate safety management at construction sites. Using advanced stereovision techniques, the system reproduces the construction site as a digital twin model, enabling the real-time 3D monitoring of near-miss events. Additionally, the system incorporates an early warning mechanism to alert workers and managers of potential safety hazards. To demonstrate the effectiveness of the proposed system, a lifting project under various exposure conditions was investigated. The results demonstrate the system's ability to detect and visualize near-misses in three-dimensional space and provide proactive warnings of potential hazards.

Most accidents in the construction industry are caused by a large proportion of hazards that remain unrecognized; this hazard-identification failure can be traced back to inadequate safety knowledge and ineffective education methods. Eye-movement modeling examples (EMMEs) show an expert's gaze replays in the hazard-identification process with verbal explanations. Thus, with the fundamental objective of measuring the impacts of tacit and explicit safety knowledge on construction workers’ hazard-identification performance, this study demonstrated the moderation effect of EMMEs on this influence pathway. This study created a digital building construction site to conduct two eye-tracking experiments, and every participant completed a hazard-identification test before and after EMMEs training. The effect of tacit knowledge and explicit knowledge on hazard-identification performance was explored in Study 1 and Study 2. The results demonstrated the potential of EMMEs to indirectly teach strategic identification sequences and contribute to deeper safety education, and the influence of individual safety knowledge on the effectiveness of EMMEs.

Energy-efficient renovations significantly affect how people use buildings, and these occupant behaviors, in turn, influence the effectiveness of building renovations. Exploring interactions between occupants and renovations is essential for implementing building energy-efficient renovation. However, physical experiments for this purpose require extensive setups in the laboratory to observe occupant behaviors under various renovations. Immersive virtual environment (IVE) experiments as an emerging method still need to adequately incorporate thermal stimuli, essential for studying occupant behaviors, building renovations, and related interactions. Therefore, this study proposes a novel approach that integrates virtual and physical environments in experiments to explore occupant behaviors and their interactions with building renovations. The interactive and immersive capabilities of IVE experiments allow for effective simulation of various renovations and occupant behaviors. By incorporating thermal stimuli from physical experiments, this approach overcomes previous limitations in studying thermal-related occupant behaviors. In a field study, an office building looking for renovation is used to explore occupant behaviors and their interactions with building renovations. It is found that energy-efficient renovation impacts personal heater use and door opening behaviors, but not clothing behaviors; such changes in heater use subsequently impact the energy performance of building renovation. In further analysis, obvious correlations are revealed between personal heater use and renovation scenarios, thermal perception, and times of day. The proposed approach is validated as a novel method to engage the occupants in achieving occupant-centric building energy-efficient transitions.

Concrete is one of the most used building materials globally, leading to a large amount of greenhouse gas (GHG) emissions. Using supplementary cementitious materials (SCM) as replacements for cement in concrete provides an effective way to reduce GHG emissions. However, quantifying the construction performance of using SCM concrete is hard because of complex interactions between concrete's mechanical properties and construction characteristics, like local energy supply, surrounding temperature and construction plans, which leads to only the fragmental performance of using SCM concrete being explored in previous studies. There still lacks an effective way to quantify the comprehensive performance and provide decision support for contractors about how to use SCM concrete. To deal with the gap, this research proposes a Collection–Simulation–Calculation–Decision (CSCD) method to analyze the complex interactions between concrete and construction, and to quantify the performance of the supply chain–construction when using SCM. A case study is also conducted to demonstrate the effectiveness of the proposed method. The results show that the proposed method is effective in quantifying the performance of using SCM concrete in construction and providing decision support for construction decision makers. A scenario analysis is also conducted to demonstrate the effectiveness of the proposed method in different project characteristics, including the global warming potential (GWP) factors for different construction sites, seasonal temperature changes and different construction plans. The proposed method is an effective tool to quantify the construction performance of using SCM concrete considering complex interactions between concrete mechanical properties and construction characteristics. The results of the research can assist construction decision makers to make decisions about using SCM concrete by comprehensively understanding the impacts shifting along the concrete supply chain and construction.

Modelling the performance of building stocks is crucial in facilitating the renovation at the building stock level. Bottom-up building stock modelling begins by detailing individual buildings and then aggregates them into stock level. Its primary advantage lies in capturing the inherent heterogeneity among distinct buildings, which enables tailored retrofitting. Naturally, this approach requires a comprehensive dataset with detailed building information such as geometry and envelope thermal properties. However, a common challenge is the incompleteness of available data in individual datasets. To address this, previous bottom-up studies have filled the missing data with representative or statistical data. Such practice could lead to homogeneous modelling of distinct buildings within the same statistical group. This limits the utilization of key ability of bottom-up building stock modelling in capturing heterogeneity, such as tailored retrofitting to explore potential retrofitting areas and strategies. To address this challenge of homogeneous modelling, we utilize data fusion framework for bottom-up building stock modelling, employing probabilistic record linkage and inverse modelling techniques to integrate multiple incomplete building performance datasets. This framework fills the missing data in one dataset with information from another, thus capturing inherent heterogeneity in the building stock. An empirical study was conducted in Umeå, Sweden, to investigate the framework's effectiveness by modelling building stock with various retrofitting strategies. This study contribution lies in enhancing bottom-up building stock modelling by capturing inherent heterogeneity, to provide tailored retrofitting solutions.

Temperature extreme weather is rapid changes in heat or cold temperatures that occur suddenly and persists for days to weeks, having an important impact on inhabitants' indoor living comfort and health. During temperature extreme weather, inhabitants may experience indoor overheating or overcooling and engage in self-adaptation to build their own resilience against these threats. However, self-adaptation alone may not adequately address the climate change challenge, external support is necessary in this process. This study aims to configure an experimental platform in the Intelligent Human-Buildings Interactions lab (IHBI) at Umeå University, to create a well-controlled experimental environment to investigate how inhabitants adapt to temperature extreme weather. The IHBI lab is developed by a hybrid virtual-physical framework, with the ability to simulate extreme weather and observe inhabitants' adaptive behaviors, facilitating the development of behavioral guidelines, interventions, and other external supports for inhabitants' adaptations. A pilot study validated the accuracy and authenticity of the developed experimental platform.

The circular economy involves a shift from the previous linear production to the closed-loop production, leading to potential circular products that can be reused or recycled. Regarding the upcoming circular products, it is essential to understand the consumers' willingness to pay (WTP) for circular products beforehand. Previous studies have proposed many common theoretical models to predict consumers' WTP for circular products. Currently, it is more significant to explore the effectiveness of these common theoretical models in this field. This study employed a mixed methods design to systematically review and comparatively analyze common theoretical models in the field of consumers' WTP for circular products. We applied the meta-analytic structural equation modeling to integrate the results of past studies to evaluate the explanatory power and rationality of common theoretical models. The results suggested that the theory of consumption values with an explanatory power R2 of 66.7 % had the most predictive power for consumers' WTP for circular products. There is the strongest positive correlation between personal norms and WTP. The findings have improved the clarity of the theory and the predictiveness and accuracy of common theories in consumers' WTP for circular products.

Energy-efficient building retrofitting plays a crucial role in reducing energy consumption and carbon emissions within the building sector. Energy-efficient retrofitting brings about changes in the built environment and it could influence the occupant behaviors. Additionally, occupant behaviors, in turn, alter the indoor environment, thereby affecting the comfort performance of the building after retrofitting. To explore this intricate relation between occupant behaviors and comfort performances of energy-efficient measures, this paper employs a data-driven approach to compile a comprehensive dataset encompassing occupant behaviors, energy-efficient measures, and associated indoor comfort of an office building in Umeå University, Sweden. Multiple binary logistic regression is applied to quantify the relationship between occupant behaviors and comfort performances of energy-efficient measures. The findings of this study hold significant value, providing guidance for occupants in adapting to energy-efficient measures while also informing future retrofitting implementation.