Fusing imperfect experimental data for risk assessment of musculoskeletal disorders in construction using canonical polyadic decomposition

A Bibliometric Analysis of Neuroscience Tools Use in Construction Health and Safety Management

Despite longstanding traditional construction health and safety management (CHSM) methods, the construction industry continues to face persistent challenges in this field. Neuroscience tools offer potential advantages in addressing these safety and health issues by providing objective data to indicate subjects' cognition and behavior. The application of neuroscience tools in the CHSM has received much attention in the construction research community, but comprehensive statistics on the application of neuroscience tools to CHSM is lacking to provide insights for the later scholars. Therefore, this study applied bibliometric analysis to examine the current state of neuroscience tools use in CHSM. The development phases; the most productive journals, regions, and institutions; influential scholars and articles; author collaboration; reference co-citation; and application domains of the tools were identified. It revealed four application domains: monitoring the safety status of construction workers, enhancing the construction hazard recognition ability, reducing work-related musculoskeletal disorders of construction workers, and integrating neuroscience tools with artificial intelligence techniques in enhancing occupational safety and health, where magnetoencephalography (EMG), electroencephalography (EEG), eye-tracking, and electrodermal activity (EDA) are four predominant neuroscience tools. It also shows a growing interest in integrating the neuroscience tools with artificial intelligence techniques to address the safety and health issues. In addition, future studies are suggested to facilitate the applications of these tools in construction workplaces by narrowing the gaps between experimental settings and real situations, enhancing the quality of data collected by neuroscience tools and performance of data processing algorithms, and overcoming user resistance in tools adoption.

Construction accident prevention: A systematic review of machine learning approaches

BACKGROUND

The construction industry is an important productive sector worldwide. However, the industry is also responsible for high numbers of work-related accidents, which highlights the necessity for improving safety management on construction sites. In parallel, technological applications such as machine learning (ML) are used in many productive sectors, including construction, and have proved significant in process optimizations and decision-making. Thus, advanced studies are required to comprehend the best way of using this technology to enhance construction site safety.

OBJECTIVE

This research developed a systematic literature review using ten scientific databases to retrieve relevant publications and fill the knowledge gaps regarding ML applications in construction accident prevention.

METHODS

This study examined 73 scientific articles through bibliometric research and descriptive analysis.

RESULTS

The results showed the publications timeline and the most recurrent journals, authors, institutions, and countries-regions. In addition, the review discovered information about the developed models, such as the research goals, the ML methods used, and the data features. The research findings revealed that USA and China are the leading countries regarding publications. Also, Support Vector Machine - SVM was the most used ML method. Furthermore, most models used textual data as a source, generally related to inspection reports and accident narratives. The data approach was usually related to facts before an accident (proactive data).

CONCLUSION

The review highlighted improvement proposals for future works and provided insights into the application of ML in construction safety management.

Application of Neuroscience Tools in Building Construction – An Interdisciplinary Analysis

Interdisciplinary integration is a new driving force in development of science and technology. Neuroscience, a powerful tool for studying human physiology and psychology that is greatly interconnected with the field of building construction, has attracted numerous research attention. In this paper, we systematically review the interdisciplinary applications of neuroscience tools using bibliometric methods. We report that the built environment, construction safety, architectural design, and occupational health are the main areas of research attention, while thermal comfort, air quality, hazard recognition, safety training, aesthetic design, and biophilic design, among others, comprise the most frequently studied topics with regards to application of neuroscience tools. Currently, eye tracking and the electroencephalogram are the most commonly used tools in the field of building construction, while functional near-infrared spectroscopy, functional magnetic resonance imaging and trigeminal nerve stimulation are still at their initial stage of application.