Pub Date : 2024-06-01DOI: 10.1016/j.jsasus.2024.03.001
Creating a robust safety management system is crucial for fostering a culture of safety in the workplace, particularly in industries like manufacturing where improvements are still needed. This study aimed to assess the impact of safety behavior on safety culture within the manufacturing sector. Employing a quantitative approach, questionnaires were distributed to 342 employees in manufacturing firms during data collection. The collected data underwent analysis using structural equation modeling (SEM) through IBM-SPSS-AMOS 24.0 to test the proposed model. The study findings revealed that components of safety behavior, specifically safety compliance and safety leadership, have a significant influence on safety culture. This implies that prioritizing safety behavior and culture is vital for occupational safety and health, aligning with guidelines set by responsible entities to ensure a secure work environment. The insights gained from this research can be instrumental in highlighting the importance of safety culture, the pivotal role of leadership, the complex nature of safety culture, and the potential for measuring and enhancing it. By understanding these implications, organizations can foster a safety-centric culture that not only protects employees but also enhances overall performance. Additionally, this research contributed to the existing literature by examining an integrated higher-order construct model using the SEM technique, predicting the model by 53 percent. The insights garnered from this study are applicable to various types of firms, emphasizing the integral role of safety culture in any organization.
建立健全的安全管理系统对于培养工作场所的安全文化至关重要，尤其是在制造业等仍需改进的行业。本研究旨在评估安全行为对制造业安全文化的影响。在数据收集过程中，采用定量方法向 342 名制造企业员工发放了调查问卷。收集到的数据通过 IBM-SPSS-AMOS 24.0 使用结构方程模型（SEM）进行分析，以检验提出的模型。研究结果表明，安全行为的组成部分，特别是安全合规性和安全领导力，对安全文化有重大影响。这意味着，优先考虑安全行为和安全文化对职业安全与健康至关重要，这与负责任的实体为确保安全工作环境而制定的指导方针相一致。从这项研究中获得的启示有助于强调安全文化的重要性、领导力的关键作用、安全文化的复杂性以及衡量和提升安全文化的潜力。通过了解这些影响，企业可以培养一种以安全为中心的文化，这种文化不仅能保护员工，还能提高整体绩效。此外，本研究还利用 SEM 技术对综合高阶建构模型进行了研究，预测了模型的 53%，为现有文献做出了贡献。本研究得出的见解适用于各种类型的公司，强调了安全文化在任何组织中不可或缺的作用。
{"title":"Fostering a safety culture in manufacturing through safety behavior: A structural equation modelling approach","authors":"","doi":"10.1016/j.jsasus.2024.03.001","DOIUrl":"10.1016/j.jsasus.2024.03.001","url":null,"abstract":"<div><div>Creating a robust safety management system is crucial for fostering a culture of safety in the workplace, particularly in industries like manufacturing where improvements are still needed. This study aimed to assess the impact of safety behavior on safety culture within the manufacturing sector. Employing a quantitative approach, questionnaires were distributed to 342 employees in manufacturing firms during data collection. The collected data underwent analysis using structural equation modeling (SEM) through IBM-SPSS-AMOS 24.0 to test the proposed model. The study findings revealed that components of safety behavior, specifically safety compliance and safety leadership, have a significant influence on safety culture. This implies that prioritizing safety behavior and culture is vital for occupational safety and health, aligning with guidelines set by responsible entities to ensure a secure work environment. The insights gained from this research can be instrumental in highlighting the importance of safety culture, the pivotal role of leadership, the complex nature of safety culture, and the potential for measuring and enhancing it. By understanding these implications, organizations can foster a safety-centric culture that not only protects employees but also enhances overall performance. Additionally, this research contributed to the existing literature by examining an integrated higher-order construct model using the SEM technique, predicting the model by 53 percent. The insights garnered from this study are applicable to various types of firms, emphasizing the integral role of safety culture in any organization.</div></div>","PeriodicalId":100831,"journal":{"name":"Journal of Safety and Sustainability","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294992672400009X/pdfft?md5=d2ccdc6c869d6be6f396d66f7bb6a32c&pid=1-s2.0-S294992672400009X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140272790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.jsasus.2024.01.001
The issue of accidental flyrock has the tendency to develop safety concerns for commuters around the main public road as mining progressed from 960 m reduced level (RL) to 912 m RL at Huni pit in Ghana. An evaluation was carried out using empirical models and an artificial neural network to assess and determine the safest blast exclusion zone. The calculations showed that the flyrock could travel a maximum of 220 m and 277.45 m horizontally for blast hole diameters of 115 mm and 127 mm, respectively, with the same stemming length of 2.0 m. The distances to the public road are much farther than these projected maximum horizontal distances. An artificial neural network (ANN) was also employed to predict the flyrock distance and it was found that the ANN model has the best root mean squared error (RMSE) value of 0.0012 and the highest coefficient of determination (R2) value of 0.99 for the flyrock throw prediction. Hence, the blast exclusion zone has been reduced to 500 m all around the pit from the pit crest satisfying the recommendation suggested by the Minerals Commission of Ghana. With the new blast exclusion zone, travelling from Damang through Akyempim to Twifo Praso, Takoradi, Cape Coast, and Accra during blasting times is no longer a bother.
{"title":"Estimating, appraising and establishing blast exclusion zone at Huni pit - A case study","authors":"","doi":"10.1016/j.jsasus.2024.01.001","DOIUrl":"10.1016/j.jsasus.2024.01.001","url":null,"abstract":"<div><div>The issue of accidental flyrock has the tendency to develop safety concerns for commuters around the main public road as mining progressed from 960 m reduced level (RL) to 912 m RL at Huni pit in Ghana. An evaluation was carried out using empirical models and an artificial neural network to assess and determine the safest blast exclusion zone. The calculations showed that the flyrock could travel a maximum of 220 m and 277.45 m horizontally for blast hole diameters of 115 mm and 127 mm, respectively, with the same stemming length of 2.0 m. The distances to the public road are much farther than these projected maximum horizontal distances. An artificial neural network (ANN) was also employed to predict the flyrock distance and it was found that the ANN model has the best root mean squared error (<em>RMSE</em>) value of 0.0012 and the highest coefficient of determination (<em>R</em><sup>2</sup>) value of 0.99 for the flyrock throw prediction. Hence, the blast exclusion zone has been reduced to 500 m all around the pit from the pit crest satisfying the recommendation suggested by the Minerals Commission of Ghana. With the new blast exclusion zone, travelling from Damang through Akyempim to Twifo Praso, Takoradi, Cape Coast, and Accra during blasting times is no longer a bother.</div></div>","PeriodicalId":100831,"journal":{"name":"Journal of Safety and Sustainability","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949926724000015/pdfft?md5=6e942fbb374aa1fda382dd321ac1b539&pid=1-s2.0-S2949926724000015-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139454838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.jsasus.2024.06.001
The changes in mechanical strength and damage analysis of bitumite under the action of liquid nitrogen (LN2) freezing are critical issues that need to be addressed in the application of LN2 in coal seam permeability enhancement. Using an mechanical testing & simulation (MTS) testing machine, strain gauges, and ultrasonic detection instruments, experiments were conducted to obtain stress–strain curves and ultrasonic time-domain images of coal samples after single freezing and cyclic freeze–thaw processes. These data were analyzed to understand the mechanical performance and internal fracture evolution of coal samples subjected to ultra-low temperatures of LN2. The experimental results indicate that: (1) The uniaxial compressive strength and elastic modulus of coal samples subjected to a single LN2 freeze are positively correlated with the freezing time from 0 to 50 min, and negatively correlated with the freezing time from 50 to 180 min. (2) The uniaxial compressive strength and elastic modulus of coal samples subjected to cyclic freezing and thawing with LN2 decrease as the number of freeze-thaw cycles increases. (3) The Poisson's ratio of coal samples subjected to single freezing and cyclic freezing-thawing is negatively correlated with the absolute freezing time. Additionally, the decrease in Poisson's ratio is greater in coal samples subjected to cyclic freezing-thawing compared to those subjected to single freezing. (4) As the absolute freezing time increases, the ultrasonic waveforms of the coal samples begin to become disordered, which is manifested by a decrease in amplitude and a delay in the arrival time of the first wave. Under ultra-low temperature conditions, LN2 can deteriorate the mechanical properties of coal samples, with the degree of deterioration being greater under cyclic freezing-thawing than single freezing. This study can provide theoretical guidance for increasing the permeability of coal seams.
{"title":"Mechanical strength change and coal damage analysis of frozen saturated bitumite after cryogenic freezing","authors":"","doi":"10.1016/j.jsasus.2024.06.001","DOIUrl":"10.1016/j.jsasus.2024.06.001","url":null,"abstract":"<div><div>The changes in mechanical strength and damage analysis of bitumite under the action of liquid nitrogen (LN<sub>2</sub>) freezing are critical issues that need to be addressed in the application of LN<sub>2</sub> in coal seam permeability enhancement. Using an mechanical testing & simulation (MTS) testing machine, strain gauges, and ultrasonic detection instruments, experiments were conducted to obtain stress–strain curves and ultrasonic time-domain images of coal samples after single freezing and cyclic freeze–thaw processes. These data were analyzed to understand the mechanical performance and internal fracture evolution of coal samples subjected to ultra-low temperatures of LN<sub>2</sub>. The experimental results indicate that: (1) The uniaxial compressive strength and elastic modulus of coal samples subjected to a single LN<sub>2</sub> freeze are positively correlated with the freezing time from 0 to 50 min, and negatively correlated with the freezing time from 50 to 180 min. (2) The uniaxial compressive strength and elastic modulus of coal samples subjected to cyclic freezing and thawing with LN<sub>2</sub> decrease as the number of freeze-thaw cycles increases. (3) The Poisson's ratio of coal samples subjected to single freezing and cyclic freezing-thawing is negatively correlated with the absolute freezing time. Additionally, the decrease in Poisson's ratio is greater in coal samples subjected to cyclic freezing-thawing compared to those subjected to single freezing. (4) As the absolute freezing time increases, the ultrasonic waveforms of the coal samples begin to become disordered, which is manifested by a decrease in amplitude and a delay in the arrival time of the first wave. Under ultra-low temperature conditions, LN<sub>2</sub> can deteriorate the mechanical properties of coal samples, with the degree of deterioration being greater under cyclic freezing-thawing than single freezing. This study can provide theoretical guidance for increasing the permeability of coal seams.</div></div>","PeriodicalId":100831,"journal":{"name":"Journal of Safety and Sustainability","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949926724000180/pdfft?md5=f88271eb40a500139896b338d3431b7a&pid=1-s2.0-S2949926724000180-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141413641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.jsasus.2024.05.001
Blast toe volume, pivotal in explosive engineering, underpins explosive energy efficient utilization, blast safety and mine production sustainability. While current research explores the use of artificial intelligence (AI) model to maximize toe volume prediction, gaps persist in understanding the application of ensemble learning algorithm techniques like hybrid and voting techniques in addressing toe volume problem. Bridging these gaps promises enhanced safety and optimization in blasting operations. This study performs AI model hybrid and voting to enhance toe volume prediction model robustness by leveraging diverse algorithms, mitigating biases, and optimizing accuracy. The study combines separate models, looks for ways that hybrid approaches can work together, and improves accuracy through group voting in order to give more complete information and more accurate predictions for estimating blast toe volume in different approaches. To develop the models, 457 blasting data was collected at the Anguran lead and zinc mine in Iran. The accuracy of the developed models was assessed using nine indices to compare their prediction performance. To understand the input relationship, multicollinearity, Spearman, Pearson, and Kendall correlation analyses show that there is a strong link between the size of the toe and the explosive charge per delay. Findings from the model analysis showed that the light gradient boosting machine (LightGBM) was the most accurate of the eight traditional models, with R2 values of 0.9004 for the training dataset and 0.8625 for the testing dataset. The Hybrid 6 model, which combines LightGBM and classification and regression trees (CART) algorithms, achieved the highest R2 scores of 0.9473 in the training phase and 0.9467 in the testing phase. The Voting 8 models, consisting of LightGBM, gradient boosting machine (GBM), decision tree (DT), ensemble tree (ET), random forest (RF), categorical boosting (CatBoost), CART, adaptive boosting (AdaBoost) and extreme gradient boosting (XGBoost) had the greatest R2 scores of 0.9876 and 0.9726 in both the training and testing stages. Using novel modelling tools to forecast blast toe volume in this study allows for resource extraction optimization, decreases environmental disturbance through mine toe smoothening, and improves safety, supporting sustainable mining practices and long-term sustainability.
{"title":"Fostering sustainable mining practices in rock blasting: Assessment of blast toe volume prediction using comparative analysis of hybrid ensemble machine learning techniques","authors":"","doi":"10.1016/j.jsasus.2024.05.001","DOIUrl":"10.1016/j.jsasus.2024.05.001","url":null,"abstract":"<div><div>Blast toe volume, pivotal in explosive engineering, underpins explosive energy efficient utilization, blast safety and mine production sustainability. While current research explores the use of artificial intelligence (AI) model to maximize toe volume prediction, gaps persist in understanding the application of ensemble learning algorithm techniques like hybrid and voting techniques in addressing toe volume problem. Bridging these gaps promises enhanced safety and optimization in blasting operations. This study performs AI model hybrid and voting to enhance toe volume prediction model robustness by leveraging diverse algorithms, mitigating biases, and optimizing accuracy. The study combines separate models, looks for ways that hybrid approaches can work together, and improves accuracy through group voting in order to give more complete information and more accurate predictions for estimating blast toe volume in different approaches. To develop the models, 457 blasting data was collected at the Anguran lead and zinc mine in Iran. The accuracy of the developed models was assessed using nine indices to compare their prediction performance. To understand the input relationship, multicollinearity, Spearman, Pearson, and Kendall correlation analyses show that there is a strong link between the size of the toe and the explosive charge per delay. Findings from the model analysis showed that the light gradient boosting machine (LightGBM) was the most accurate of the eight traditional models, with <em>R</em><sup>2</sup> values of 0.9004 for the training dataset and 0.8625 for the testing dataset. The Hybrid 6 model, which combines LightGBM and classification and regression trees (CART) algorithms, achieved the highest <em>R</em><sup>2</sup> scores of 0.9473 in the training phase and 0.9467 in the testing phase. The Voting 8 models, consisting of LightGBM, gradient boosting machine (GBM), decision tree (DT), ensemble tree (ET), random forest (RF), categorical boosting (CatBoost), CART, adaptive boosting (AdaBoost) and extreme gradient boosting (XGBoost) had the greatest <em>R</em><sup>2</sup> scores of 0.9876 and 0.9726 in both the training and testing stages. Using novel modelling tools to forecast blast toe volume in this study allows for resource extraction optimization, decreases environmental disturbance through mine toe smoothening, and improves safety, supporting sustainable mining practices and long-term sustainability.</div></div>","PeriodicalId":100831,"journal":{"name":"Journal of Safety and Sustainability","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949926724000179/pdfft?md5=5813e964c335413c7655925d146f9c38&pid=1-s2.0-S2949926724000179-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141141668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/S2949-9267(24)00021-0
{"title":"Editorial Board Member","authors":"","doi":"10.1016/S2949-9267(24)00021-0","DOIUrl":"10.1016/S2949-9267(24)00021-0","url":null,"abstract":"","PeriodicalId":100831,"journal":{"name":"Journal of Safety and Sustainability","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949926724000210/pdfft?md5=a43054ba4f8bc9fe772c9f0c989de7f7&pid=1-s2.0-S2949926724000210-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.jsasus.2024.02.001
Steel wire ropes are widely used in various fields, such as mining, elevators, and cable cars. However, their long-term use can lead to wire breakage, posing safety risks. The detection of wire breakages in steel wire ropes is crucial. This study addresses the shortcomings of existing quantitative identification methods for steel wire rope damage detection and proposes a novel model for fusion-based classification and recognition of wire rope damage. This model first combines the continuous wavelet transform and variational mode decomposition for feature extraction. Subsequently, it utilized convolutional neural networks to learn data features and introduced an attention mechanism to weigh and select the fused data. The final output provides the classification results, aiming to enhance the classification accuracy. Comparative experiments and ablation studies were conducted using the memory networks, autoencoder, and support vector machine models. The experimental results demonstrate the superiority of the proposed model regarding feature extraction, classification accuracy, and automation. The model achieved an accuracy rate of 94.44 % when classifying the nine types of wire breakages. This study presents an effective approach for signal processing and damage classification in steel wire rope damage detection, which is crucial for improving the reliability of wire breakage detection in steel wire ropes.
{"title":"Research on quantitative identification method for wire rope wire breakage damage signals based on multi-decomposition information fusion","authors":"","doi":"10.1016/j.jsasus.2024.02.001","DOIUrl":"10.1016/j.jsasus.2024.02.001","url":null,"abstract":"<div><div>Steel wire ropes are widely used in various fields, such as mining, elevators, and cable cars. However, their long-term use can lead to wire breakage, posing safety risks. The detection of wire breakages in steel wire ropes is crucial. This study addresses the shortcomings of existing quantitative identification methods for steel wire rope damage detection and proposes a novel model for fusion-based classification and recognition of wire rope damage. This model first combines the continuous wavelet transform and variational mode decomposition for feature extraction. Subsequently, it utilized convolutional neural networks to learn data features and introduced an attention mechanism to weigh and select the fused data. The final output provides the classification results, aiming to enhance the classification accuracy. Comparative experiments and ablation studies were conducted using the memory networks, autoencoder, and support vector machine models. The experimental results demonstrate the superiority of the proposed model regarding feature extraction, classification accuracy, and automation. The model achieved an accuracy rate of 94.44 % when classifying the nine types of wire breakages. This study presents an effective approach for signal processing and damage classification in steel wire rope damage detection, which is crucial for improving the reliability of wire breakage detection in steel wire ropes.</div></div>","PeriodicalId":100831,"journal":{"name":"Journal of Safety and Sustainability","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949926724000027/pdfft?md5=dd9d7492d9e31b58e002d65f19d7eb04&pid=1-s2.0-S2949926724000027-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140090907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The appropriate automation in armored vehicles is vital for the operational efficiency and personnel security of operators. In this study, fifty subjects conducted over-the-horizon strike and N-back tests at different automation levels based on a virtual simulation system for armored vehicles. Physiological signals and subjective assessments were recorded. The mental load and task performance of operators were related to different automation levels. Results suggested that the mental load decreased with the increase of automation levels. Apart from object destruction time, heart rate and standard deviation of NN intervals (SDNN), other indexes were all significantly affected by the automation level of subtasks (p < 0.01). The NASA-TLX scores, object destruction time, response time of abnormal states, and reaction time in N-back tests decreased by at least 2.9 %, 8.2 %, 11.2 % and 1.3 % respectively, while the mean accuracy in N-back tests increased by 0.1 %. Furthermore, there existed several automation levels of tasks where the task performance remained almost unchanged under normal operation. The function of task automation on decreasing mental load reduced in the following order: A3-B3-C2-D2-E2, A2-B2-C2-D2-E2, and A3-B3-C1-D1-E1. The main contribution of this research was to provide a qualitative method and framework for the evaluation of influences of automation level on operators’ mental load, and the design of human-machine interaction and adaptive automation in automated systems.
装甲车辆的适当自动化对操作员的操作效率和人员安全至关重要。在这项研究中，50 名受试者在装甲车辆虚拟仿真系统的基础上进行了不同自动化水平的超视距打击和 N-后退测试。对生理信号和主观评价进行了记录。操作员的心理负荷和任务表现与不同的自动化水平有关。结果表明，随着自动化水平的提高，心理负荷也随之降低。除物体破坏时间、心率和 NN 间隔标准偏差（SDNN）外，其他指标均受到子任务自动化水平的显著影响（p < 0.01）。N-back测试中的NASA-TLX得分、物体破坏时间、异常状态反应时间和反应时间分别减少了至少2.9%、8.2%、11.2%和1.3%，而N-back测试的平均准确率则增加了0.1%。此外，在一些自动化程度较高的任务中，正常操作下的任务表现几乎保持不变。任务自动化对降低心理负荷的作用按以下顺序降低：A3-B3-C2-D2-E2、A2-B2-C2-D2-E2 和 A3-B3-C1-D1-E1。本研究的主要贡献在于提供了一种定性方法和框架，用于评估自动化水平对操作员精神负担的影响，以及设计自动化系统中的人机交互和自适应自动化。
{"title":"Influence of automation level of human-machine system on operators’ mental load","authors":"Qingyang Huang , Mingyang Guo , Yuning Wei , Jingyuan Zhang , Fang Xie , Xiaoping Jin","doi":"10.1016/j.jsasus.2023.12.001","DOIUrl":"10.1016/j.jsasus.2023.12.001","url":null,"abstract":"<div><p>The appropriate automation in armored vehicles is vital for the operational efficiency and personnel security of operators. In this study, fifty subjects conducted over-the-horizon strike and N-back tests at different automation levels based on a virtual simulation system for armored vehicles. Physiological signals and subjective assessments were recorded. The mental load and task performance of operators were related to different automation levels. Results suggested that the mental load decreased with the increase of automation levels. Apart from object destruction time, heart rate and standard deviation of NN intervals (SDNN), other indexes were all significantly affected by the automation level of subtasks (p < 0.01). The NASA-TLX scores, object destruction time, response time of abnormal states, and reaction time in N-back tests decreased by at least 2.9 %, 8.2 %, 11.2 % and 1.3 % respectively, while the mean accuracy in N-back tests increased by 0.1 %. Furthermore, there existed several automation levels of tasks where the task performance remained almost unchanged under normal operation. The function of task automation on decreasing mental load reduced in the following order: A3-B3-C2-D2-E2, A2-B2-C2-D2-E2, and A3-B3-C1-D1-E1. The main contribution of this research was to provide a qualitative method and framework for the evaluation of influences of automation level on operators’ mental load, and the design of human-machine interaction and adaptive automation in automated systems.</p></div>","PeriodicalId":100831,"journal":{"name":"Journal of Safety and Sustainability","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949926723000069/pdfft?md5=32eb6a4016d147e6ae4221dee814732b&pid=1-s2.0-S2949926723000069-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138608622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The design of underground hard rock pillars plays a crucial role in the safety and stability of underground mining operations. Ensuring safe and efficient resource extraction while safeguarding the well-being of miners is of paramount importance. This paper provides an overview of the background and significance of underground hard rock pillar design, presenting a comprehensive exploration of various technologies employed in assessing and designing stable pillars. These methodologies include empirical formulas, numerical simulations, statistical analyses, and artificial intelligence (AI) techniques, each contributing to enhancing safety and resource extraction efficiency in mining operations. Furthermore, this paper conducts a systematically analysis of global trends from the year 2000 onwards, utilizing CiteSpace and VOSviewer software tools. This analytical approach aims to provide a quantitative assessment of the domain of pillar design. Notably, the future of hard rock pillar design is poised for a transformative shift, as it involves the integration of data-driven and theory-driven approaches. By combining AI with finite element and discrete element simulations, the industry anticipates achieving more accurate, adaptable, and dynamic pillar designs. This integration is expected to not only improve safety and environmental sustainability but also yield significant economic benefits. In conclusion, the merging of data-driven and theory-driven methodologies in underground hard rock pillar design represents a promising avenue for advancing the field, ensuring safer, more sustainable, and economically viable underground mining practices.
{"title":"Enhancing safety, sustainability, and economics in mining through innovative pillar design: A state-of-the-art review","authors":"Yulin Zhang , Hongning Qi , Chuanqi Li , Jian Zhou","doi":"10.1016/j.jsasus.2023.11.001","DOIUrl":"10.1016/j.jsasus.2023.11.001","url":null,"abstract":"<div><p>The design of underground hard rock pillars plays a crucial role in the safety and stability of underground mining operations. Ensuring safe and efficient resource extraction while safeguarding the well-being of miners is of paramount importance. This paper provides an overview of the background and significance of underground hard rock pillar design, presenting a comprehensive exploration of various technologies employed in assessing and designing stable pillars. These methodologies include empirical formulas, numerical simulations, statistical analyses, and artificial intelligence (AI) techniques, each contributing to enhancing safety and resource extraction efficiency in mining operations. Furthermore, this paper conducts a systematically analysis of global trends from the year 2000 onwards, utilizing CiteSpace and VOSviewer software tools. This analytical approach aims to provide a quantitative assessment of the domain of pillar design. Notably, the future of hard rock pillar design is poised for a transformative shift, as it involves the integration of data-driven and theory-driven approaches. By combining AI with finite element and discrete element simulations, the industry anticipates achieving more accurate, adaptable, and dynamic pillar designs. This integration is expected to not only improve safety and environmental sustainability but also yield significant economic benefits. In conclusion, the merging of data-driven and theory-driven methodologies in underground hard rock pillar design represents a promising avenue for advancing the field, ensuring safer, more sustainable, and economically viable underground mining practices.</p></div>","PeriodicalId":100831,"journal":{"name":"Journal of Safety and Sustainability","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949926723000057/pdfft?md5=61360ae0c863855d11ad5896af9f77f7&pid=1-s2.0-S2949926723000057-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139019552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-01DOI: 10.1016/S2949-9267(24)00005-2
{"title":"Editorial Board Member","authors":"","doi":"10.1016/S2949-9267(24)00005-2","DOIUrl":"https://doi.org/10.1016/S2949-9267(24)00005-2","url":null,"abstract":"","PeriodicalId":100831,"journal":{"name":"Journal of Safety and Sustainability","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949926724000052/pdfft?md5=30cd667a206a754e2ec554d77e4588cf&pid=1-s2.0-S2949926724000052-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140901357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Pollution shows no mercy to pollination: Act yesterday","authors":"Evgenios Agathokleous , Zhaozhong Feng , James Blande , Josep Peñuelas","doi":"10.1016/j.jsasus.2023.10.001","DOIUrl":"10.1016/j.jsasus.2023.10.001","url":null,"abstract":"","PeriodicalId":100831,"journal":{"name":"Journal of Safety and Sustainability","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949926723000045/pdfft?md5=4b93434c25407b601883707de1a64868&pid=1-s2.0-S2949926723000045-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135371075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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