Journal of Safety and Sustainability最新文献

{"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>² 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>² 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>² 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":"1 2","pages":"Pages 75-88"},"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}

{"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":"1 2","pages":"Page i"},"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}

{"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":"1 2","pages":"Pages 89-97"},"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}

{"title":"Influence of automation level of human-machine system on operators’ mental load","authors":"Qingyang Huang ,&nbsp;Mingyang Guo ,&nbsp;Yuning Wei ,&nbsp;Jingyuan Zhang ,&nbsp;Fang Xie ,&nbsp;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 ​&lt; ​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":"1 1","pages":"Pages 42-52"},"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}

{"title":"Enhancing safety, sustainability, and economics in mining through innovative pillar design: A state-of-the-art review","authors":"Yulin Zhang ,&nbsp;Hongning Qi ,&nbsp;Chuanqi Li ,&nbsp;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":"1 1","pages":"Pages 53-73"},"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}

{"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":"1 1","pages":"Page i"},"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 ,&nbsp;Zhaozhong Feng ,&nbsp;James Blande ,&nbsp;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":"1 1","pages":"Pages 1-3"},"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}

{"title":"Developing risk assessment framework for wildfire in the United States – A deep learning approach to safety and sustainability","authors":"Pingfan Hu ,&nbsp;Rachel Tanchak ,&nbsp;Qingsheng Wang","doi":"10.1016/j.jsasus.2023.09.002","DOIUrl":"10.1016/j.jsasus.2023.09.002","url":null,"abstract":"<div><p>The frequency and intensity of wildfires have significantly increased in the United States over recent decades, posing profound threats to community safety and ecological sustainability. The escalating losses of human life, property, and biodiversity demand a proactive approach to wildfire prediction and management. This study proposes a highly efficient deep learning framework, utilizing a geospatial database of wildfire incidents in the United States from 1992 to 2018, aimed at bolstering our collective resilience against such disasters. The framework comprises two components: firstly, leveraging deep neural networks (DNN), the cause and size of potential wildfires are predicted, achieving accuracy rates of 76.9% and 76.4% for 5-class classification respectively. Secondly, a forecast model using long short term memory networks (LSTM) and an autoencoder is used to anticipate the likelihood of imminent wildfires, focusing on highly at-risk regions such as California. A specific model created to perform one-week forecasts for California achieved a coefficient of determination (R²) and root-mean-square error (RMSE) of 0.90 and 49.5076, respectively. These predictive models offer a significant step towards advancing community safety and environmental sustainability by facilitating timely and effective responses, thereby mitigating the catastrophic effects of wildfires on human life, properties, and delicate ecosystems.</p></div>","PeriodicalId":100831,"journal":{"name":"Journal of Safety and Sustainability","volume":"1 1","pages":"Pages 26-41"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949926723000033/pdfft?md5=a6ac85642279d58f599d7ccab7c6061b&pid=1-s2.0-S2949926723000033-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135428503","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":"Monte Carlo tree search-based deep reinforcement learning for flexible operation & maintenance optimization of a nuclear power plant","authors":"Zhaojun Hao ,&nbsp;Francesco Di Maio ,&nbsp;Enrico Zio","doi":"10.1016/j.jsasus.2023.08.001","DOIUrl":"10.1016/j.jsasus.2023.08.001","url":null,"abstract":"<div><p>Nuclear power plants (NPPs) are required to operate on a flexible profitable production plan while guaranteeing high safety standards. Deep reinforcement learning (DRL) is an effective method to find the most profitable operation &amp; maintenance (O&amp;M) strategy to adopt in a complex system. However, profit-driven only DRL neglects safety-related issues. In this paper, we propose a DRL approach to solve single-objective sequential decision problems (SOSDPs) and multi-objective sequential decision problems (MOSDPs) to find O&amp;M strategies that trade off reliability and profit. The combinatorial problem related with the training of the RL agent to search for the optimal solution is addressed by Monte Carlo tree search (MCTS), whose performance is compared with the traditionally adopted proximal policy optimization (PPO) &amp; imitation learning (IL). A case study is considered for demonstration.</p></div>","PeriodicalId":100831,"journal":{"name":"Journal of Safety and Sustainability","volume":"1 1","pages":"Pages 4-13"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294992672300001X/pdfft?md5=ee7ea82ca8f59dc3b8f715901a3d3437&pid=1-s2.0-S294992672300001X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76381919","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":"Investigation of foundation theory of safety & security complexity","authors":"Chao Wu","doi":"10.1016/j.jsasus.2023.09.001","DOIUrl":"10.1016/j.jsasus.2023.09.001","url":null,"abstract":"<div><p>With the continuous emergence of complex safety &amp; security (SS) problems, SS complexity studies have become an inevitable tendency of SS science development. First, evolutions of research paths and objects of SS science in the past 100 years and some typical new viewpoints on SS science research in recent years are briefly summarized in order to prove the necessity of SS complexity studies. Also, multi-dimensional analysis of SS problems is made to show the essential reason why SS complexity studies are required. Then, historical analysis method, reasoning method, induction method, theoretical modeling method and prediction method are used to carry out the following research on the basic theory of the SS complexity: typical methods and principles of SS complexity studies are summarized; core concepts and basic definitions of SS complexity are built; some criteria on judging SS complex issues are put forward; models which can be used to express the SS complexity system are constructed and some controlling strategies for the SS complex system are proposed; and finally, the conclusions and outlooks of SS complexity studies are given. These results are of great significance for enrichment of SS science.</p></div>","PeriodicalId":100831,"journal":{"name":"Journal of Safety and Sustainability","volume":"1 1","pages":"Pages 14-25"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949926723000021/pdfft?md5=6a4bbbc67b4c5a1dc9b02aa88bf98394&pid=1-s2.0-S2949926723000021-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135588967","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}