研究者詳細 - 安達　毅

安達　毅 (アダチ　ツヨシ)

ADACHI Tsuyoshi

写真a

所属	大学院国際資源学研究科資源開発環境学専攻
生年	1969年
研究室住所	010-8502　秋田県秋田市手形学園町１－１　秋田大学　国際資源学部　2階N213号室
研究室電話	018-889-2468
研究室FAX	018-889-2468
ホームページ	http://www.gipc.akita-u.ac.jp/~adachi/
メールアドレス

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資源経済学

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1992年03月

京都大学工学部資源工学科卒業

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1994年03月

京都大学工学研究科資源工学専攻修士課程修了

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2012年10月

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2013年03月

カーティン大学研究員

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東京大学 - 博士（工学）

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2016年04月

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秋田大学大学院国際資源学研究科資源開発環境学専攻教授
2014年04月

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2016年03月

秋田大学国際資源学部国際資源学科資源政策コース教授
2010年09月

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2015年03月

秋田大学国際資源学教育研究センター教授

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2016年04月

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秋田大学大学院国際資源学研究科資源開発環境学専攻教授
2014年04月

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秋田大学国際資源学部国際資源学科資源政策コース教授
2006年06月

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2010年08月

東京大学環境安全研究センター准教授
2006年03月

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2010年08月

東京大学生産技術研究所准教授
1995年04月

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2006年03月

東京大学大学院工学系研究科　地球システム工学専攻助教

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2008年04月

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日本国

日本リアルオプション学会
1995年04月

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日本国

エネルギー・資源学会
1994年04月

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日本国

資源・素材学会

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エネルギー / 地球資源工学、エネルギー学

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鉱物資源開発の持続可能性評価に関する研究

安達毅

2001年07月

単著

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Enhancing Interpretability in Drill Bit Wear Analysis through Explainable Artificial Intelligence: A Grad-CAM Approach

Senjoba L.

Applied Sciences (Switzerland) ( Applied Sciences (Switzerland) ) 14 ( 9 ) 3621 - 3621 2024年05月

研究論文（学術雑誌）

This study introduces a novel method for analyzing vibration data related to drill bit failure. Our approach combines explainable artificial intelligence (XAI) with convolutional neural networks (CNNs). Conventional signal analysis methods, such as fast Fourier transform (FFT) and wavelet transform (WT), require extensive knowledge of drilling equipment specifications, which limits their adaptability to different conditions. In contrast, our method leverages XAI algorithms applied to CNNs to directly identify fault signatures from vibration signals. The signals are transformed into their frequency components and then employed as inputs to a CNN model, which is trained to detect patterns indicative of drill bit failure. XAI algorithms are then employed to generate attention maps, highlighting regions of interest in the CNN. By scrutinizing these maps, engineers can identify critical frequencies associated with drill bit failure, providing valuable insights for maintenance and optimization. This method offers a transparent and interpretable framework for analyzing vibration data, enabling informed decision-making and proactive maintenance strategies to enhance drilling efficiency and minimize downtime. The integration of XAI with CNNs facilitates a deeper understanding of the root causes of drill bit failure and improves overall drilling performance.

DOI

A Comprehensive Numerical Modeling Study for Parameter Optimization and Slope Stability Analysis in the Baganuur Lignite Coal Mine

Enkhbold B.

Mining ( Mining ) 3 ( 4 ) 755 - 772 2023年12月

研究論文（学術雑誌）

The “Baganuur” lignite coal mine is one of the biggest open cast mines in Mongolia. However, there is a huge challenge in managing the stability of its internal dump, which prevents the proper operation of the mine and has an impact on the economy. To solve the internal dump slope stability problem, this study focused on incorporating the inherent mechanical properties of the rock material to build numerical models of the internal dump. By applying two software programs from Rocscience (Phase2 and Slide) and four different methods, the finite element method, the Bishop method, the Janbu simplified method, and the Spencer simplified method, the current and improved internal dump parameters were numerically simulated and analyzed. Based on the properties of the rock, the LEM and FEM were used to determine the parameters that could have an impact on the stability of the internal waste dump. The impacts of the internal dump height, dip angle, and safety berm on these parameters were studied. This study covers several analytical methods for calculating safety factors. Based on the results of the numerical simulation, it is determined that it is possible to increase the internal dump capacity by approximately 56% at a 50 m height and 28° dip angle and using a 15 m safety berm. Under similar conditions, this study presents an optimum SRF at 40 m height, 28° dip angle, and 5 m safety berm. Based on the numerical models, it is found that changes in the dip angle have a greater impact than changes in the dump height on the slope stability of an internal dump.

DOI

Digital Twin Technology in Data Center Simulations: Evaluating the Feasibility of a Former Mine Site

Ikeda H.

Sustainability (Switzerland) ( Sustainability (Switzerland) ) 15 ( 23 ) 16176 - 16176 2023年12月

研究論文（学術雑誌）

Mining activities often deem mine sites as temporary, leading to their eventual reclamation, rehabilitation, or abandonment. This study innovates by proposing the re-purposing of the disused Osarizawa mine in Akita, Japan, leveraging its consistently low tunnel temperatures to establish a data center, thereby offering a sustainable economic avenue to offset reclamation costs. We assessed the feasibility of this transformation by gathering comprehensive environmental data from the site and conducting meticulous ventilation simulations. These simulations explored various scenarios encompassing diverse ventilation configurations, data server room dimensions, thermal outputs, and the inherent cooling capabilities of the proposed humid rooms. By juxtaposing the simulation outcomes with the criteria set forth in the ASHRAE 2011 Thermal Guidelines, we pinpointed the optimal parameters that satisfy the stringent temperature and relative humidity prerequisites essential for a data center’s operation. This research underscores the potential of reimagining abandoned mine sites as strategic assets, providing economic benefits while adhering to critical data center infrastructure standards.

DOI

Evaluation and Prediction of Blast-Induced Ground Vibrations: A Gaussian Process Regression (GPR) Approach

Fissha Y.

Mining ( Mining ) 3 ( 4 ) 659 - 682 2023年12月

研究論文（学術雑誌）

Ground vibration is one of the most hazardous outcomes of blasting. It has a negative impact both on the environment and the human population near to the blasting area. To evaluate the magnitude of blasting vibrations, it is important to consider PPV as a fundamental critical base parameter practice in terms of vibration velocity. This study aims to explore the application of different soft computing techniques, including a Gaussian process regression (GPR), decision tree (DT), and support vector regression (SVR), for the prediction of blast-induced ground vibration (PPV) in quarry mining. The three models were evaluated using classical mathematical evaluation metrics (R2, RMSE, MSE, MAE). The result shows that the GPR model achieves an excellent prediction result; with R2 = 0.94, RMSE = 0.0384, MSE = 0.0014, and MAE = 0.0265, it shows high accuracy in predicting PPV. The Shapley additive explanation (SHAP) results emphasize the importance of understanding the interactions between the various factors and their effects on the vibration assessment. The findings can inform the development of more sustainable and environmentally friendly models for predicting blasting vibrations. Using a GPR to simulate and predict blasting-induced ground vibrations is the study’s main contribution. The GPR can capture complicated, non-linear correlations in data, making it ideal for blast-induced ground vibrations, which are dynamic and nonlinear. By using a Gaussian process regression, we can help companies and researchers improve the safety and efficiency in blast-induced ground vibration environments.

DOI

Soft ground tunnel lithology classification using clustering-guided light gradient boosting machine

Kilic K.

Journal of Rock Mechanics and Geotechnical Engineering ( Journal of Rock Mechanics and Geotechnical Engineering ) 15 ( 11 ) 2857 - 2867 2023年11月

研究論文（学術雑誌）

During tunnel boring machine (TBM) excavation, lithology identification is an important issue to understand tunnelling performance and avoid time-consuming excavation. However, site investigation generally lacks ground samples and the information is subjective, heterogeneous, and imbalanced due to mixed ground conditions. In this study, an unsupervised (K-means) and synthetic minority oversampling technique (SMOTE)-guided light-gradient boosting machine (LightGBM) classifier is proposed to identify the soft ground tunnel classification and determine the imbalanced issue of tunnelling data. During the tunnel excavation, an earth pressure balance (EPB) TBM recorded 18 different operational parameters along with the three main tunnel lithologies. The proposed model is applied using Python low-code PyCaret library. Next, four decision tree-based classifiers were obtained in a short time period with automatic hyperparameter tuning to determine the best model for clustering-guided SMOTE application. In addition, the Shapley additive explanation (SHAP) was implemented to avoid the model black box problem. The proposed model was evaluated using different metrics such as accuracy, F1 score, precision, recall, and receiver operating characteristics (ROC) curve to obtain a reasonable outcome for the minority class. It shows that the proposed model can provide significant tunnel lithology identification based on the operational parameters of EPB-TBM. The proposed method can be applied to heterogeneous tunnel formations with several TBM operational parameters to describe the tunnel lithologies for efficient tunnelling.

DOI

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