Indoor physical environments appear to influence learning efficiency nowadays. For improvement in learning efficiency, environmental scenarios need to be designed when occupants engage in different learning tasks. However, how learning efficiency is affected by indoor physical environment based on task types are still not well understood. The present study aims to explore the impacts of three physical environmental factors (i.e., temperature, noise, and illuminance) on learning efficiency according to different types of tasks, including perception, memory, problem-solving, and attention-oriented tasks. A 3 × 4 × 3 full factorial design experiment was employed in a university classroom with 10 subjects recruited. Environmental scenarios were generated based on different levels of temperature (17 °C, 22 °C, and 27 °C), noise (40 dB(A), 50 dB(A), 60 dB(A), and 70 dB(A)) and illuminance (60 lx, 300 lx, and 2200 lx). Accuracy rate (AC), reaction time (RT), and the final performance indicator (PI) were used to quantify learning efficiency. The results showed ambient temperature, noise, and illuminance exerted significant main effect on learning efficiency based on four task types. Significant concurrent effects of the three factors on final learning efficiency was found in all tasks except problem-solving-oriented task. The optimal environmental scenarios for top learning efficiency were further identified under different environmental interactions. The highest learning efficiency came in thermoneutral, relatively quiet, and bright conditions in perception-oriented task. Subjects performed best under warm, relatively quiet, and moderately light exposure when recalling images in the memory-oriented task. Learning efficiency peaked to maxima in thermoneutral, fairly quiet, and moderately light environment in problem-solving process while in cool, fairly quiet and bright environment with regard to attention-oriented task. The study provides guidance for building users to conduct effective environmental intervention with simultaneous controls of ambient temperature, noise, and illuminance. It contributes to creating the most suitable indoor physical environment for improving occupants learning efficiency according to different task types. The findings could further supplement the present indoor environment-related standards or norms with providing empirical reference on environmental interactions.

To reduce harm caused by occupational health risks of construction workers exposed to working environments, especially those for interior decoration, it is crucial for them to actively recognize and prevent these risks. Therefore, how to improve their occupational health risks perception and regulate their coping behaviors should be of great concern. However, most prior studies target construction worker safety, and little research focuses on risk analysis from the psychological level of workers. Hence, construction workers’ occupational health risk perception level and coping behavior level in Nanjing and the influencing factors were analyzed through statistical analysis with 341 valid questionnaires. Bootstrapping was applied to test the mediating effects of risk perception on the proposed factors and coping behaviors. This study revealed that construction workers have a high-level of occupational health risk perception, yet low-level coping behavior. Gender, age, education level, and unit qualification cause differences in individual risk perception level. Personal knowledge and group effects significantly affect the level of risk perception, which subsequently affect coping behavior. Education level, monthly income, and personal knowledge influence the coping behavior through risk perception. Recommendations were put forward for risk perception and coping behavior improvement from the perspectives of construction workers themselves, enterprises, and governments. This study sheds new light for research areas of occupational health and risk management and provides beneficial practice for improving construction workers’ responses to occupational health risks.

Building Information Modelling (BIM) has been increasingly integrated with Artificial Intelligence (AI) solutions to automate building construction processes. However, the methods for effectively transforming data from BIM formats, such as Industry Foundation Classes (IFC), into formats suitable for AI applications still need to be explored. This paper conducts a Systematic Literature Review (SLR) following the PRISMA guidelines to analyse current data preparation approaches in BIM applications. The goal is to identify the most suitable methods for AI integration by reviewing current data preparation practices in BIM applications. The review included a total of 93 articles from SCOPUS and WoS. The results include eight common data types, two data management frameworks, and four primary data conversion methods. Further analysis identified three barriers: first, the IFC format’s lack of support for time-series data; second, limitations in extracting geometric information from BIM models; and third, the absence of established toolchains to convert IFC files into usable formats. Based on the evidence, the data readiness is at an intermediate level. This research may serve as a guideline for future studies to address the limitations in data preparation within BIM for AI integration.

Environmental quality in public rail transit has recently raised great concern, with more attention paid to underground subway microenvironment. This research aimed to provide guidance for healthy urban subway microenvironments (sub-MEs) according to comprehensive micro-environmental categories, including thermal environment, air quality, lighting environment, and acoustic environment from both practical and regulation perspectives. Field sampling experiments were conducted in Nanjing Metro Line X (NMLX). Descriptive analysis, correlation analysis and one-way analysis of variance were used to investigate the status quo of urban sub-MEs. A paired samples t-test was then performed to compare among subway station halls, platforms, and in-cabin trains based on integrated sub-MEs. Results show that relative humidity, air velocity, respirable particulate matter (PM₁₀) concentration, and illuminance dissatisfy the requirements in relevant national standards. Significant difference was observed in lighting environment between station hall and platform. It was detected platforms are warmer and more polluted than train cabins. Additionally, subway trains generate main noise on platform which is much louder when leaving than arriving. Protective strategies for sub-ME improvement as well as principles for updating standards were proposed from a proactive point of view. The findings are beneficial for moving towards healthy urban sub-MEs and more sustainable operation of subway systems.