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Application of Emerging Artificial Intelligence-Based Technologies and Techniques to Improve the Occupational Health and Safety Management of Industries: From Singular to Ensemble Approaches

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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Occupational Safety and Health".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 20117

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Special Issue Editors

Dr. Amir Mahdiyar

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Guest Editor

School of Housing, Building and Planning, Universiti Sains Malaysia, Penang 11800, Malaysia
Interests: sustainable construction; digital technology; construction safety; MCDM; probabilistic simulation
Special Issues, Collections and Topics in MDPI journals

Dr. Saeed Reza Mohandes

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Guest Editor

Department of Building and Real Estate (BRE), Faculty of Construction and Environment (FCE), The Hong Kong Polytechnic University, Hong Kong, China
Interests: artificial intelligence and machine learning in construction; construction safety; sustainable development; infrastructure management; building energy efficiency; facility management
Special Issues, Collections and Topics in MDPI journals

Dr. Mehrdad Arashpour

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Guest Editor

Department of Civil Engineering, Monash University, Melbourne VIC 3800, Australia
Interests: prefabricated structures; deep learning for construction performance improvements; auomation and robotics; building information modeling (BIM); project risk management; digital twins

Special Issue Information

Dear Colleagues,

All industries and firms have, without exception, encountered different Occupational Health and Safety (OHS)-related risks every year, which not only endanger the wellbeing of the involved parties but also cast a shadow on the profitability of the industries suffering from the repercussions of such occurrences. To revolutionize the status quo of the management of OHS (OHSM) within these industries, the emergence of cutting-edge techniques and top-notch technologies is observed to have been the fulcrum of myriad researchers on this ground. With this in mind, the introduction of advanced Artificial Intelligence (AI)-based technologies and techniques to the area of OHSM has led to a paradigm shift in the way the OHSM is being undertaken. Given that the OHSM has been given careful attention, the exploitation of emerging AI-based technologies (such as the Internet of Things, digital twins, sensors, virtual reality, augmented reality) and techniques (such as artificial neural networks, deep learning, meta-heuristic optimization algorithms, and so forth) within the concerned domain is still naive and fragmented; thus, more studies need to tilt their focus toward such crucial stepping stones. Considering this, this Special Issue opens avenues for scholars undertaking research in OHSM—including but not limited to the industries of construction, manufacturing, shipping, textile, petroleum, and healthcare—using advanced AI-based technologies and techniques to prudently disseminate their findings. Sharing such research will culminate in improving the wellbeing of crew members through reductions in the rate of associated injuries. This Special Issue also provides a podium for researchers not only to share the contributions of AI-based approaches in coping with safety challenges caused by COVID-19, but also to discuss possible enhancements in using such approaches to deal with the safety aspects of this pandemic. Additionally, this Special Issue welcomes studies that have developed hybrid and ensemble approaches to improve OHSM within different industries.

Dr. Amir Mahdiyar
Dr. Saeed Reza Mohandes
Dr. Mehrdad Arashpour
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Safety management
Safety assessment
Occupational health and safety
Artificial neural networks
Fuzzy sets theory
Fuzzy inference system
Fuzzy neural networks
Deep learning
Machine learning
Fuzzy deep learning
Genetic algorithm
Simulation
Particle swarm optimization
Ant colony optimization
Fuzzy MCDM
Large-scale group decision making
Internet of things
Sensors
Digital twins
Building information modeling
Virtual reality
Augmented reality

Published Papers (5 papers)

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Research

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15 pages, 1059 KiB

Open AccessArticle

Investigating the Barriers to Applying the Internet-of-Things-Based Technologies to Construction Site Safety Management

by Sanaz Tabatabaee, Saeed Reza Mohandes, Rana Rabnawaz Ahmed, Amir Mahdiyar, Mehrdad Arashpour, Tarek Zayed and Syuhaida Ismail

Int. J. Environ. Res. Public Health 2022, 19(2), 868; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19020868 - 13 Jan 2022

Cited by 15 | Viewed by 3261

Abstract

The utilization of Internet-of-Things (IoT)-based technologies in the construction industry has recently grabbed the attention of numerous researchers and practitioners. Despite the improvements made to automate this industry using IoT-based technologies, there are several barriers to the further utilization of these leading-edge technologies. A review of the literature revealed that it lacks research focusing on the obstacles to the application of these technologies in Construction Site Safety Management (CSSM). Accordingly, the aim of this research was to identify and analyze the barriers impeding the use of such technologies in the CSSM context. To this end, initially, the extant literature was reviewed extensively and nine experts were interviewed, which led to the identification of 18 barriers. Then, the fuzzy Delphi method (FDM) was used to calculate the importance weights of the identified barriers and prioritize them through the lenses of competent experts in Hong Kong. Following this, the findings were validated using semi-structured interviews. The findings showed that the barriers related to “productivity reduction due to wearable sensors”, “the need for technical training”, and “the need for continuous monitoring” were the most significant, while “limitations on hardware and software and lack of standardization in efforts,” “the need for proper light for smooth functionality”, and “safety hazards” were the least important barriers. The obtained findings not only give new insight to academics, but also provide practical guidelines for the stakeholders at the forefront by enabling them to focus on the key barriers to the implementation of IoT-based technologies in CSSM. Full article

(This article belongs to the Special Issue Application of Emerging Artificial Intelligence-Based Technologies and Techniques to Improve the Occupational Health and Safety Management of Industries: From Singular to Ensemble Approaches)

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22 pages, 1102 KiB

Open AccessArticle

The Opportunities and Challenges Associated with the Implementation of Fourth Industrial Revolution Technologies to Manage Health and Safety

by Reneiloe Malomane, Innocent Musonda and Chioma Sylvia Okoro

Int. J. Environ. Res. Public Health 2022, 19(2), 846; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19020846 - 13 Jan 2022

Cited by 17 | Viewed by 5898

Abstract

The fourth industrial revolution (4iR) technologies offer an opportunity for the construction industry to improve health and safety (H&S) compliance. Therefore, implementing the technologies is of top priority to improve the endless H&S incidents in construction projects, which lead to poor quality of work, late project delivery, and increased labour injury claims. Central to improving the nature of work and other industrial processes, the 4iR technologies have emerged. Concurrent with this trend is the importance of 4iR technologies in enhancing health and safety performance on construction sites. However, the implementation of 4iR technologies in the construction industry is faced with various challenges. Therefore, this paper reports on a study aimed at examining the challenges associated with implementing 4iR technologies in the construction sector in South Africa towards effective management of H&S. The study followed a systematic literature review, data collection using a questionnaire survey and thereafter, descriptive, and inferential analyses were conducted. The findings revealed that the implementation of 4iR technologies is challenged by a lack of adequate relevant skills, the unavailability of training capacities, expensive technologies, and negative perceptions such as fear of job loss by industry professionals. The findings are essential for the advancement of H&S research and implementation. In addition, the findings are important to industry decision-makers in order to elevate their awareness and promote the use of 4iR technologies to manage construction activities. The study implications include the need for the construction industry to collaborate with higher education institutions to conduct research and include 4iR in the curriculum. Full article

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20 pages, 5657 KiB

Open AccessArticle

Multi-Angle Fusion-Based Safety Status Analysis of Construction Workers

by Hui Deng, Zhibin Ou and Yichuan Deng

Int. J. Environ. Res. Public Health 2021, 18(22), 11815; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph182211815 - 11 Nov 2021

Cited by 3 | Viewed by 1588

Abstract

Hazardous accidents often happen in construction sites and bring fatal consequences, and therefore safety management has been a certain dilemma to construction managers for long time. Although computer vision technology has been used on construction sites to identify construction workers and track their movement trajectories for safety management, the detection effect is often influenced by limited coverage of single cameras and occlusion. A multi-angle fusion method applying SURF feature algorithm is proposed to coalesce the information processed by improved GMM (Gaussian Mixed Model) and HOG + SVM (Histogram of Oriented Gradient and Support Vector Machines), identifying the obscured workers and achieving a better detection effect with larger coverage. Workers are tracked in real-time, with their movement trajectory estimated by utilizing Kalman filters and safety status analyzed to offer a prior warning signal. Experimental studies are conducted for validation of the proposed framework for workers’ detection and trajectories estimation, whose result indicates that the framework is able to detect workers and predict their movement trajectories for safety forewarning. Full article

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16 pages, 1243 KiB

Open AccessArticle

Critical Success Factors of Safety Program Implementation in Construction Projects in Iraq

by Mohanad Kamil Buniya, Idris Othman, Riza Yosia Sunindijo, Ghanim Kashwani, Serdar Durdyev, Syuhaida Ismail, Maxwell Fordjour Antwi-Afari and Heng Li

Int. J. Environ. Res. Public Health 2021, 18(16), 8469; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18168469 - 11 Aug 2021

Cited by 17 | Viewed by 3995

Abstract

The construction sector is recognized as one of the most dangerous industries in the world. The situation is worsening in Iraq, as a result of a lack of attention to safety in the building industry and the poor implementation of safety programs. This research aims to identify the critical safety factors (CSFs) of safety program implementation in the Iraqi construction industry. The CSFs were first identified from a review of literature before being verified by construction practitioners, using semi-structured interviews. A questionnaire, based on the verified CSFs, was distributed to construction practitioners in Iraq. Exploratory factor analysis (EFA) was used to analyze the quantitative data, and the results show that the CSFs can be categorized into four constructs: worker involvement, safety prevention and control system, safety arrangement, and management commitment. Following that, partial least square structural equation modelling (PLS-SEM) was executed to establish the connection between safety program implementation and overall project success. The result confirms that safety program implementation has a significant, positive impact on project success. This article contributes to knowledge and practice by identifying the CSFs for implementing safety programs in the Iraqi construction industry. The successful implementation of a safety program not only improves safety performance, but also helps to meet other project goals. Full article

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Other

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15 pages, 10597 KiB

Open AccessTechnical Note

Smart Helmet-Based Proximity Warning System to Improve Occupational Safety on the Road Using Image Sensor and Artificial Intelligence

by Yeanjae Kim and Yosoon Choi

Int. J. Environ. Res. Public Health 2022, 19(23), 16312; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph192316312 - 06 Dec 2022

Cited by 4 | Viewed by 4045

Abstract

Recently, collisions between equipment and workers occur frequently on the road in construction and surface mining sites. To prevent such accidents, we developed a smart helmet-based proximity warning system (PWS) that facilitates visual and tactile proximity warnings. In this system, a smart helmet comprising an Arduino Uno board and a camera module with built-in Wi-Fi was used to transmit images captured by the camera to a smartphone via Wi-Fi. When the image was analyzed through object detection and a heavy-duty truck or other vehicle was detected in an image, the smartphone transmitted a signal to the Arduino via Bluetooth and, when a signal was received, an LED strip with a three-color LED visually alerted the worker and the equipment operator. The performance of the system tested the recognition distance of the helmet according to the pixel size of the detected image in an outdoor environment. The proposed personal PWS can directly produce visual proximity warnings to both workers and operators enabling them to quickly identify and evacuate from dangerous situations. Full article

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Application of Emerging Artificial Intelligence-Based Technologies and Techniques to Improve the Occupational Health and Safety Management of Industries: From Singular to Ensemble Approaches

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