Environment
Aditi Nag; Smriti Mishra
Abstract
The convergence of Mining Heritage Tourism (MHT) and Artificial Intelligence (AI) presents a transformative paradigm, reshaping heritage preservation, visitor engagement, and sustainable growth. This paper investigates the dynamic synergy between these realms, probing how AI-driven technologies can augment ...
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The convergence of Mining Heritage Tourism (MHT) and Artificial Intelligence (AI) presents a transformative paradigm, reshaping heritage preservation, visitor engagement, and sustainable growth. This paper investigates the dynamic synergy between these realms, probing how AI-driven technologies can augment the authenticity, accessibility, and educational significance of mining heritage sites. Focusing on the profound impact of AI on MHT, this study centers its examination on the Barr Conglomerate located in the culturally rich Pali District, India. Employing a mixed-methods approach involving survey data analysis and neural network modelling, the research work explores AI applications that enhance visitor experiences, interpret historical narratives, optimize resource allocation, and mitigate the adverse effects of over-tourism. The study meticulously navigates a vast landscape of AI technologies, spanning machine learning, natural language processing, and augmented reality, show-casing their potential to enrich encounters with mining heritage. While AI promises to revolutionize heritage management, the paper emphasizes the critical importance of ethical considerations and cultural sensitivities. Balancing innovation with preservation, the study advocates for an inclusive approach that honors diverse cultural values and encourages community engagement. Through this exploration, the paper delves into the practical implementation of AI, unveiling best practices lessons learned and illuminating challenges and opportunities. Ultimately, this research work envisions a future where AI empowers mining heritage to transcend temporal boundaries, cultivating immersive experiences resonating with authenticity, global understanding, and sustainable stewardship.
Exploitation
Hassanreza Ghasemitabar; Andisheh Alimoradi; Hamidreza Hemati Ahooi; Mahdi Fathi
Abstract
Drilling of exploratory boreholes is one of the most important and costly steps in mineral exploration, which can provide us with accurate and appropriate information to continue the mining process. There are limitations on drilling the target boreholes, such as high costs, topographical problems in ...
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Drilling of exploratory boreholes is one of the most important and costly steps in mineral exploration, which can provide us with accurate and appropriate information to continue the mining process. There are limitations on drilling the target boreholes, such as high costs, topographical problems in installation of drilling rigs, restrictions caused by previous mining operation etc. The advances in artificial intelligence can help to solve these problems. In this research, we used python as one of the most pervasive and the most powerful programming languages in the field of data analysis and artificial intelligence. In this method mean shift algorithms have been used to cluster data, random forest to estimate clusters, and gradient boosting to estimate iron grade. Finally, in the studied area of Choghart in Central Iran, more than 91% accuracy was achieved in detection of ore blocks. Also, the results of the neural network indicate the mean square error (MSE) and mean absolute error (MAE) in the training data, respectively equal to 0.001 and 0.029, in the test data is 0.002 and 0.03, and in the validation boreholes, we reached a maximum of 0.06 and 0.2.
Mine Economic and Management
Aditi Nag; Smriti Mishra
Abstract
Integrating Artificial Intelligence (AI) into heritage tourism has opened new avenues for transforming visitors’ engagement with historical sites. This research paper delves into a novel paradigm, focusing on AI integration in inter- and intra-regional mining heritage site planning and design. ...
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Integrating Artificial Intelligence (AI) into heritage tourism has opened new avenues for transforming visitors’ engagement with historical sites. This research paper delves into a novel paradigm, focusing on AI integration in inter- and intra-regional mining heritage site planning and design. Recognizing this context's unique challenges and opportunities, the study aims to uncover critical ideas and theories on how AI enhances visitor experience, promotes cultural preservation, sustainability, and stakeholder collaboration. Acknowledging the distinctive challenges and opportunities presented by inter- and intra-regional mining heritage contexts, this research work underscores the critical importance of striking a harmonious equilibrium between technological advancements and preserving historical and cultural legacies. Drawing from a cross-disciplinary approach, the study examines the profound implications of integrating AI into mining heritage sites' planning and design strategies. The study reviews 199 articles on AI-driven planning and design benefits, examining potential advantages. Ethical considerations, algorithmic biases, and the role of interdisciplinary research are also explored. The study highlights the intricate interplay between AI-enhanced engagement, responsible tourism practices, and the meaningful representation of local cultures. By shedding light on this uncharted territory, the research contributes to developing informed strategies that harness AI's potential to shape inter- and intra-regional mining heritage site planning and design, fostering responsible and impactful tourism experiences. By delving into this paradigm, it hopes to arm the researchers, policy-makers, practitioners, and other stakeholders with information and understanding that will help them forge a progressive and morally upright future, in which technology co-exists peacefully with practices for cultural preservation and sustainable tourism.
H. Fattahi; M. Hasanipanah; N. Zandy Ilghani
Abstract
The mechanical characteristics of rocks and rock masses are considered as the determining factors in making plans in the mining and civil engineering projects. Two factors that determine how rocks responds in varying stress conditions are P-wave velocity (PWV) and its isotropic properties. Therefore, ...
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The mechanical characteristics of rocks and rock masses are considered as the determining factors in making plans in the mining and civil engineering projects. Two factors that determine how rocks responds in varying stress conditions are P-wave velocity (PWV) and its isotropic properties. Therefore, achieving a high-accurate method to estimate PWV is a very important task. This work investigates the use of different intelligent models such as multivariate adaptive regression splines (MARS), classification and regression tree (CART), group method of data handling (GMDH), and gene expression programming (GEP) for the prediction of PWV. The proposed models are then evaluated using several error statistics, i.e. squared correlation coefficient (R2) and root mean squared error (RMSE). The values of R2 obtained from the CART, MARS, GMDH, and GEP models are 0.983, 0.999, 0.995, and 0.998, respectively. Furthermore, the CART, MARS, GMDH, and GEP models predict PWV with the RMSE values of 0.037, 0.007, 0.023, and 0.020, respectively. According to the aforementioned amounts, the models presented in this work predict PWV with a good performance. Nevertheless, the results obtained reveal that the MARS model yields a better prediction in comparison to the GEP, GMDH, and CART models. Accordingly, MARS can be offered as an accurate model for predicting the aims in other rock mechanics and geotechnical fields.
M. Fathi; A. Alimoradi; H.R. Hemati Ahooi
Abstract
Scientific uncertainties make the grade estimation very complicated and important in the metallic ore deposits. This paper introduces a new hybrid method for estimating the iron ore grade using a combination of two artificial intelligence methods; it is based on the single layer-extreme learning machine ...
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Scientific uncertainties make the grade estimation very complicated and important in the metallic ore deposits. This paper introduces a new hybrid method for estimating the iron ore grade using a combination of two artificial intelligence methods; it is based on the single layer-extreme learning machine and the particle swarm optimization approaches, and is designed based on the location of the boreholes, depth of the boreholes, and drill hole information from an orebody, and applied for the ore grade estimation on the basis of a block model. In this work, the two algorithms of optimization clustering and neural networks are used for the iron grade estimation in the Choghart iron ore north anomaly in the central Iran. The results of the training and testing the algorithms indicate a significant ability of the optimized neural network system in the ore grade estimation.
