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Fostering sustainable mining practices in rock blasting: Assessment of blast toe volume prediction using comparative analysis of hybrid ensemble machine learning techniques 在岩石爆破中促进可持续采矿实践:利用混合集合机器学习技术的比较分析评估爆破脚趾体积预测
Pub Date : 2024-06-01 DOI: 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.
爆破趾量在爆破工程中举足轻重,是高效利用爆破能量、确保爆破安全和矿山生产可持续性的基础。虽然目前的研究正在探索使用人工智能(AI)模型最大限度地预测趾部爆破体积,但在了解混合和投票技术等集合学习算法技术在解决趾部爆破体积问题中的应用方面仍存在差距。缩小这些差距有望提高爆破作业的安全性和优化性。本研究采用人工智能模型混合和投票技术,通过利用不同的算法、减少偏差和优化准确性来增强趾尖体积预测模型的稳健性。该研究结合了不同的模型,寻找混合方法的协同工作方式,并通过分组投票提高准确性,以便为不同方法的爆破趾量估算提供更完整的信息和更准确的预测。为开发模型,在伊朗安古兰铅锌矿收集了 457 个爆破数据。使用九项指标对所开发模型的准确性进行了评估,以比较其预测性能。为了解输入关系,多重共线性、Spearman、Pearson 和 Kendall 相关性分析表明,坡脚尺寸与每次延迟的炸药装药量之间存在密切联系。模型分析结果显示,轻梯度提升机(LightGBM)是八个传统模型中最准确的,训练数据集的 R2 值为 0.9004,测试数据集的 R2 值为 0.8625。混合 6 模型结合了 LightGBM 和分类与回归树(CART)算法,在训练阶段和测试阶段分别获得了 0.9473 和 0.9467 的最高 R2 值。由 LightGBM、梯度提升机 (GBM)、决策树 (DT)、集合树 (ET)、随机森林 (RF)、分类提升 (CatBoost)、CART、自适应提升 (AdaBoost) 和极端梯度提升 (XGBoost) 组成的 Voting 8 模型在训练和测试阶段的 R2 得分最高,分别为 0.9876 和 0.9726。在这项研究中,利用新型建模工具预测爆破坡面体积可以优化资源开采,通过平整矿山坡面减少对环境的干扰,并提高安全性,从而支持可持续采矿实践和长期可持续性发展。
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引用次数: 0
Editorial Board Member 编辑委员会成员
Pub Date : 2024-06-01 DOI: 10.1016/S2949-9267(24)00021-0
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引用次数: 0
Research on quantitative identification method for wire rope wire breakage damage signals based on multi-decomposition information fusion 基于多分解信息融合的钢丝绳断丝损伤信号定量识别方法研究
Pub Date : 2024-06-01 DOI: 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.
钢丝绳广泛应用于采矿、电梯和缆车等各个领域。然而,长期使用会导致钢丝断裂,带来安全隐患。钢丝绳断丝的检测至关重要。本研究针对现有钢丝绳损伤检测定量识别方法的不足,提出了一种基于融合的钢丝绳损伤分类和识别新模型。该模型首先结合连续小波变换和变模分解进行特征提取。随后,它利用卷积神经网络学习数据特征,并引入注意力机制来权衡和选择融合数据。最终输出提供分类结果,以提高分类准确性。使用记忆网络、自动编码器和支持向量机模型进行了对比实验和消融研究。实验结果表明,所提出的模型在特征提取、分类准确性和自动化方面都具有优势。在对九种断线类型进行分类时,该模型的准确率达到了 94.44%。这项研究为钢丝绳损伤检测中的信号处理和损伤分类提出了一种有效的方法,对于提高钢丝绳断丝检测的可靠性至关重要。
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引用次数: 0
Influence of automation level of human-machine system on operators’ mental load 人机系统自动化程度对操作员心理负担的影响
Pub Date : 2024-03-01 DOI: 10.1016/j.jsasus.2023.12.001
Qingyang Huang , Mingyang Guo , Yuning Wei , Jingyuan Zhang , Fang Xie , Xiaoping Jin

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。
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引用次数: 0
Enhancing safety, sustainability, and economics in mining through innovative pillar design: A state-of-the-art review 通过创新性矿柱设计提高采矿业的安全性、可持续性和经济性:最新综述
Pub Date : 2024-03-01 DOI: 10.1016/j.jsasus.2023.11.001
Yulin Zhang , Hongning Qi , Chuanqi Li , Jian Zhou

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.

地下硬岩支柱的设计对地下采矿作业的安全性和稳定性起着至关重要的作用。在保障矿工福利的同时,确保安全高效地开采资源至关重要。本文概述了地下硬岩支柱设计的背景和意义,全面探讨了评估和设计稳定支柱所采用的各种技术。这些方法包括经验公式、数值模拟、统计分析和人工智能(AI)技术,每种方法都有助于提高采矿作业的安全性和资源开采效率。此外,本文还利用 CiteSpace 和 VOSviewer 软件工具对 2000 年以来的全球趋势进行了系统分析。这种分析方法旨在对矿柱设计领域进行量化评估。值得注意的是,硬岩支柱设计的未来将发生转变,因为它涉及数据驱动和理论驱动方法的整合。通过将人工智能与有限元和离散元模拟相结合,业内预计将实现更精确、适应性更强和更动态的支柱设计。预计这种整合不仅能提高安全性和环境可持续性,还能产生显著的经济效益。总之,在地下硬岩支柱设计中融合数据驱动和理论驱动的方法,是推进该领域发展的一条大有可为的途径,可确保地下采矿实践更安全、更具可持续性和经济可行性。
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引用次数: 0
Editorial Board Member 编辑委员会成员
Pub Date : 2024-03-01 DOI: 10.1016/S2949-9267(24)00005-2
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引用次数: 0
Pollution shows no mercy to pollination: Act yesterday 污染对授粉毫不留情:昨天行动起来
Pub Date : 2024-03-01 DOI: 10.1016/j.jsasus.2023.10.001
Evgenios Agathokleous , Zhaozhong Feng , James Blande , Josep Peñuelas
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引用次数: 0
Developing risk assessment framework for wildfire in the United States – A deep learning approach to safety and sustainability 制定美国野火风险评估框架--安全和可持续性的深度学习方法
Pub Date : 2024-03-01 DOI: 10.1016/j.jsasus.2023.09.002
Pingfan Hu , Rachel Tanchak , Qingsheng Wang

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.

近几十年来,美国野火发生的频率和强度显著增加,对社区安全和生态可持续性构成了严重威胁。人类生命、财产和生物多样性的损失不断增加,这就要求我们采取积极主动的方法来预测和管理野火。本研究利用 1992 年至 2018 年美国野火事件地理空间数据库,提出了一种高效的深度学习框架,旨在增强我们应对此类灾害的集体复原力。该框架由两部分组成:首先,利用深度神经网络(DNN)预测潜在野火的起因和规模,5 类分类的准确率分别达到 76.9% 和 76.4%。其次,利用长期短期记忆网络(LSTM)和自动编码器建立预测模型,预测即将发生野火的可能性,重点关注加利福尼亚等高风险地区。为对加利福尼亚州进行一周预测而创建的特定模型的判定系数 (R2) 和均方根误差 (RMSE) 分别为 0.90 和 49.5076。这些预测模型促进了及时有效的应对措施,从而减轻了野火对人类生命、财产和脆弱生态系统的灾难性影响,为提高社区安全和环境可持续性迈出了重要一步。
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引用次数: 0
Monte Carlo tree search-based deep reinforcement learning for flexible operation & maintenance optimization of a nuclear power plant 基于蒙特卡洛树搜索的深度强化学习,用于核电站的灵活运维优化
Pub Date : 2024-03-01 DOI: 10.1016/j.jsasus.2023.08.001
Zhaojun Hao , Francesco Di Maio , Enrico Zio

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 & maintenance (O&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&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) & imitation learning (IL). A case study is considered for demonstration.

核电站(NPP)需要在保证高安全标准的同时,按照灵活的盈利生产计划运行。深度强化学习(DRL)是在复杂系统中寻找最有利可图的运行与维护(O&M)策略的有效方法。然而,仅以利润为导向的 DRL 忽略了与安全相关的问题。在本文中,我们提出了一种 DRL 方法来解决单目标连续决策问题(SOSDP)和多目标连续决策问题(MOSDP),以找到在可靠性和利润之间进行权衡的运行与维护(O&M)策略。蒙特卡洛树搜索(MCTS)解决了与训练 RL 代理搜索最优解有关的组合问题,并将其性能与传统采用的近似策略优化(PPO)&模仿学习(IL)进行了比较。通过一个案例进行了演示。
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引用次数: 0
Investigation of foundation theory of safety & security complexity 安全与安保复杂性基础理论研究
Pub Date : 2024-03-01 DOI: 10.1016/j.jsasus.2023.09.001
Chao Wu

With the continuous emergence of complex safety & 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.

随着复杂安全问题的不断涌现,安全科学复杂性研究已成为安全科学发展的必然趋势。首先,简要总结了近百年来安全与安保科学研究路径和对象的演变,以及近年来安全与安保科学研究的一些典型新观点,以证明安全与安保复杂性研究的必要性。同时,通过对 SS 问题的多维分析,说明 SS 复杂性研究之所以必要的根本原因。然后,运用历史分析法、推理法、归纳法、理论建模法和预测法等方法,对党卫军复杂性的基本理论进行了如下研究:总结了党卫军复杂性研究的典型方法和原理;构建了党卫军复杂性的核心概念和基本定义;提出了党卫军复杂性问题的若干判断标准;构建了可用于表达党卫军复杂性系统的模型,并提出了党卫军复杂性系统的若干控制策略;最后,给出了党卫军复杂性研究的结论和展望。这些成果对于丰富 SS 科学具有重要意义。
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引用次数: 0
期刊
Journal of Safety and Sustainability
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