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Safety Analytics in Occupational Settings
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Safety Analytics in Occupational Settings

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 (28 February 2023) | Viewed by 19042

Special Issue Editors

Dr. Fatemeh Davoudi

E-Mail Website
Guest Editor
Department of Technology, College of Engineering, San Jose State University, San Jose, CA 95192, USA
Interests: occupational injury analysis; machine learning; predictive modeling of industrial systems
Special Issues, Collections and Topics in MDPI journals
Dr. Steven A Freeman

E-Mail Website
Guest Editor
Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50011, USA
Interests: occupational safety; agricultural safety; safety education; scholarship of teaching and learning
Special Issues, Collections and Topics in MDPI journals
Dr. Gretchen Mosher

E-Mail Website
Guest Editor
Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50011, USA
Interests: safety decision making; use of safety analytics in prediction; evaluative tools in safety management
Prof. Dr. Mack Shelley

E-Mail Website
Guest Editor
Department of Political Science, and Department of Statistics, Iowa State University, Ames, IA 50011, USA
Interests: public health; public policy analysis; program evaluation. advanced statistical methods

Special Issue Information

Dear Colleagues,

Reducing occupational injuries is among the leading challenges faced by most industries. These occupational incidents affect the lives of workers as well as those of their families and co-workers and impose a considerable economic burden on employers, employees, insurance companies, health care systems, and society. Therefore, researchers have continually sought to gain a better understanding of the factors that impact the occurrence and severity of these incidents to improve the accuracy of predicting the likelihood of future workplace injuries and to develop intervention and mitigation strategies. Analyses of injury statistics are useful in defining characteristics of occupational incidents to assist in the derivation and development of preventative measures. Applying proper analytical tools aimed at discovering injury patterns and understanding the underlying mechanisms of incidents in different work environments may produce effective insights to enhance policymaking, training, and incident/injury intervention, and mitigation efforts. This Special Issue invites papers addressing various aspects of safety analytics in occupational settings, especially those combining safety-related concepts with statistical tools and advanced analytical methods, such as machine learning algorithms, to provide practical insights that can be used in safety decision-making and methods and processes to improve safety outcomes in occupational settings.

Dr. Fatemeh Davoudi
Prof. Dr. Steven Freeman
Dr. Gretchen A. Mosher
Prof. Dr. Mack C. Shelley
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

  • occupational injury
  • safety decision-making
  • safety management
  • safety education
  • analysis of occupational incidents
  • safety science
  • statistical modeling of occupational incidents
  • machine learning for safety analysis
  • public policy
  • program evaluation

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Published Papers (5 papers)

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Research

19 pages, 2383 KiB  
Article
Occupational Injury Risk Mitigation: Machine Learning Approach and Feature Optimization for Smart Workplace Surveillance
by Mohamed Zul Fadhli Khairuddin, Puat Lu Hui, Khairunnisa Hasikin, Nasrul Anuar Abd Razak, Khin Wee Lai, Ahmad Shakir Mohd Saudi and Siti Salwa Ibrahim
Int. J. Environ. Res. Public Health 2022, 19(21), 13962; https://doi.org/10.3390/ijerph192113962 - 27 Oct 2022
Cited by 11 | Viewed by 3080
Abstract
Forecasting the severity of occupational injuries shall be all industries’ top priority. The use of machine learning is theoretically valuable to assist the predictive analysis, thus, this study attempts to propose a feature-optimized predictive model for anticipating occupational injury severity. A public database [...] Read more.
Forecasting the severity of occupational injuries shall be all industries’ top priority. The use of machine learning is theoretically valuable to assist the predictive analysis, thus, this study attempts to propose a feature-optimized predictive model for anticipating occupational injury severity. A public database of 66,405 occupational injury records from OSHA is analyzed using five sets of machine learning models: Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, Decision Tree, and Random Forest. For model comparison, Random Forest outperformed other models with higher accuracy and F1-score. Therefore, it highlighted the potential of ensemble learning as a more accurate prediction model in the field of occupational injury. In constructing the model, this study also proposed the feature optimization technique that revealed the three most important features; ‘nature of injury’, ‘type of event’, and ‘affected body part’ in developing model. The accuracy of the Random Forest model was improved by 0.5% or 0.895 and 0.954 for the prediction of hospitalization and amputation, respectively by redeveloping and optimizing the model with hyperparameter tuning. The feature optimization is essential in providing insight knowledge to the Safety and Health Practitioners for future injury corrective and preventive strategies. This study has shown promising potential for smart workplace surveillance. Full article
(This article belongs to the Special Issue Safety Analytics in Occupational Settings)
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18 pages, 2132 KiB  
Article
Risk Prediction of Coal and Gas Outburst in Deep Coal Mines Based on the SAPSO-ELM Algorithm
by Li Yang, Xin Fang, Xue Wang, Shanshan Li and Junqi Zhu
Int. J. Environ. Res. Public Health 2022, 19(19), 12382; https://doi.org/10.3390/ijerph191912382 - 28 Sep 2022
Cited by 9 | Viewed by 1715
Abstract
Effective risk prevention and management in deep coal mines can reduce the occurrences of outburst accidents and casualties. To address the low accuracy and inefficiency of coal–gas outburst prediction in deep coal mines, this study proposes a deep coal–gas outburst risk prediction method [...] Read more.
Effective risk prevention and management in deep coal mines can reduce the occurrences of outburst accidents and casualties. To address the low accuracy and inefficiency of coal–gas outburst prediction in deep coal mines, this study proposes a deep coal–gas outburst risk prediction method based on kernal principal component analysis (KPCA) and an improved extreme learning machine (SAPSO-ELM) algorithm. Firstly, high-dimensional nonlinear raw data were processed by KPCA. Secondly, the extracted sequence of outburst-causing indicator principal components were used as the input variables for the simulated annealing particle swarm algorithm (SAPSO), which was proposed to optimize the input layer weights and implied layer thresholds of the ELM. Finally, a coal and gas outburst risk prediction model for a deep coal mine based on the SAPSO-ELM algorithm was developed. The research results show that, compared with the ELM and PSO-ELM algorithms, the SAPSO-ELM optimization algorithm significantly improved the accuracy of risk prediction for coal–gas outbursts in deep coal mines, and the accuracy rate was as high as 100%. This study enriches the theory and methods of safety management in deep coal mines, and effectively helps coal mine enterprises in improving their ability to manage coal–gas outburst risks. Full article
(This article belongs to the Special Issue Safety Analytics in Occupational Settings)
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11 pages, 750 KiB  
Article
Research on the Safety and Security Distance of Above-Ground Liquefied Gas Storage Tanks and Dispensers
by Bożena Kukfisz, Aneta Kuczyńska, Robert Piec and Barbara Szykuła-Piec
Int. J. Environ. Res. Public Health 2022, 19(2), 839; https://doi.org/10.3390/ijerph19020839 - 12 Jan 2022
Cited by 5 | Viewed by 3180
Abstract
Many countries lack clear legal requirements on the distance between buildings and petrol station facilities. The regulations in force directly determine the petrol station facilities’ required distance to buildings, and such distances are considered relevant for newly designed and reconstructed buildings. Public buildings [...] Read more.
Many countries lack clear legal requirements on the distance between buildings and petrol station facilities. The regulations in force directly determine the petrol station facilities’ required distance to buildings, and such distances are considered relevant for newly designed and reconstructed buildings. Public buildings must be located no closer than 60 m to the above-ground liquefied gas tanks and liquid gas dispensers. Still, based on engineering calculations and the applied technical measures, it is possible to determine a safe distance for buildings that are constructed, extended and reconstructed, to which superstructures are added or whose utilisation method changes. The paper presents the results of calculations devoted to determining a safe distance between public buildings and LPG filling station facilities, using selected analytical models. The analyses were carried out for the LPG gas system commonly used in petrol stations, consisting of two gas storage tanks of 4.85 m3 capacity each, and a dispenser. It is legitimate to eliminate the obligation to observe the 60 m distance between LPG filling stations and public buildings and the mandatory distance of 60 m between liquefied gas dispensers and public buildings is not justified in light of the implemented requirements to use various protections at self-service liquefied gas filling stands. Full article
(This article belongs to the Special Issue Safety Analytics in Occupational Settings)
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23 pages, 2930 KiB  
Article
Temporal Visual Patterns of Construction Hazard Recognition Strategies
by Rui Cheng, Jiaming Wang and Pin-Chao Liao
Int. J. Environ. Res. Public Health 2021, 18(16), 8779; https://doi.org/10.3390/ijerph18168779 - 20 Aug 2021
Cited by 6 | Viewed by 3219
Abstract
Visual cognitive strategies in construction hazard recognition (CHR) signifies prominent value for the development of CHR computer vision techniques and safety training. Nonetheless, most studies are based on either sparse fixations or cross-sectional (accumulative) statistics, which lack consideration of temporality and yielding limited [...] Read more.
Visual cognitive strategies in construction hazard recognition (CHR) signifies prominent value for the development of CHR computer vision techniques and safety training. Nonetheless, most studies are based on either sparse fixations or cross-sectional (accumulative) statistics, which lack consideration of temporality and yielding limited visual pattern information. This research aims to investigate the temporal visual search patterns for CHR and the cognitive strategies they imply. An experimental study was designed to simulate CHR and document participants’ visual behavior. Temporal qualitative comparative analysis (TQCA) was applied to analyze the CHR visual sequences. The results were triangulated based on post-event interviews and show that: (1) In the potential electrical contact hazards, the intersection of the energy-releasing source and wire that reflected their interaction is the cognitively driven visual area that participants tend to prioritize; (2) in the PPE-related hazards, two different visual strategies, i.e., “scene-related” and “norm-guided”, can usually be generalized according to the participants’ visual cognitive logic, corresponding to the bottom-up (experience oriented) and top-down (safety knowledge oriented) cognitive models. This paper extended recognition-by-components (RBC) model and gestalt model as well as providing feasible practical guide for safety trainings and theoretical foundations of computer vision techniques for CHR. Full article
(This article belongs to the Special Issue Safety Analytics in Occupational Settings)
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17 pages, 579 KiB  
Article
Predictive Modeling for Occupational Safety Outcomes and Days Away from Work Analysis in Mining Operations
by Anurag Yedla, Fatemeh Davoudi Kakhki and Ali Jannesari
Int. J. Environ. Res. Public Health 2020, 17(19), 7054; https://doi.org/10.3390/ijerph17197054 - 27 Sep 2020
Cited by 29 | Viewed by 6254
Abstract
Mining is known to be one of the most hazardous occupations in the world. Many serious accidents have occurred worldwide over the years in mining. Although there have been efforts to create a safer work environment for miners, the number of accidents occurring [...] Read more.
Mining is known to be one of the most hazardous occupations in the world. Many serious accidents have occurred worldwide over the years in mining. Although there have been efforts to create a safer work environment for miners, the number of accidents occurring at the mining sites is still significant. Machine learning techniques and predictive analytics are becoming one of the leading resources to create safer work environments in the manufacturing and construction industries. These techniques are leveraged to generate actionable insights to improve decision-making. A large amount of mining safety-related data are available, and machine learning algorithms can be used to analyze the data. The use of machine learning techniques can significantly benefit the mining industry. Decision tree, random forest, and artificial neural networks were implemented to analyze the outcomes of mining accidents. These machine learning models were also used to predict days away from work. An accidents dataset provided by the Mine Safety and Health Administration was used to train the models. The models were trained separately on tabular data and narratives. The use of a synthetic data augmentation technique using word embedding was also investigated to tackle the data imbalance problem. Performance of all the models was compared with the performance of the traditional logistic regression model. The results show that models trained on narratives performed better than the models trained on structured/tabular data in predicting the outcome of the accident. The higher predictive power of the models trained on narratives led to the conclusion that the narratives have additional information relevant to the outcome of injury compared to the tabular entries. The models trained on tabular data had a lower mean squared error compared to the models trained on narratives while predicting the days away from work. The results highlight the importance of predictors, like shift start time, accident time, and mining experience in predicting the days away from work. It was found that the F1 score of all the underrepresented classes except one improved after the use of the data augmentation technique. This approach gave greater insight into the factors influencing the outcome of the accident and days away from work. Full article
(This article belongs to the Special Issue Safety Analytics in Occupational Settings)
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