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大学院国際資源学研究科 資源開発環境学専攻 |
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1969年 |
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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 |
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安達 毅 (アダチ ツヨシ)
ADACHI Tsuyoshi
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職務経歴（学内） 【 表示 ／ 非表示 】
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2016年04月-継続中
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
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2014年04月-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
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2010年09月-2015年03月
秋田大学 国際資源学教育研究センター 教授
職務経歴（学外） 【 表示 ／ 非表示 】
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2016年04月-継続中
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
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2014年04月-継続中
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
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2006年06月-2010年08月
東京大学 環境安全研究センター 准教授
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2006年03月-2010年08月
東京大学 生産技術研究所 准教授
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1995年04月-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
学会（学術団体）・委員会 【 表示 ／ 非表示 】
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2008年04月-継続中
日本国
日本リアルオプション学会
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1995年04月-継続中
日本国
エネルギー・資源学会
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1994年04月-継続中
日本国
資源・素材学会
研究等業績 【 表示 ／ 非表示 】
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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.
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Urban Quarry Ground Vibration Forecasting: A Matrix Factorization Approach
Hajime Ikeda, Masato Takeuchi, Elsa Pansilvania, Brian Bino Sinaice, Hisatoshi Toriya, Tsuyoshi Adachi, Youhei Kawamura
Applied Sciences ( MDPI AG ) 13 ( 23 ) 12674 - 12674 2023年11月
研究論文（学術雑誌）
Blasting is routinely carried out in urban quarry sites. Residents or houses around quarry sites are affected by the ground vibrations induced by blasting. Peak Particle Velocity (PPV) is used as a metric to measure ground vibration intensity. Therefore, many prediction models of PPV using experimental methods, statistical methods, and Artificial Neural Networks (ANNs) have been proposed to mitigate this effect. However, prediction models using experimental and statistical methods have a tendency of poor prediction accuracy. In addition, while prediction models using ANNs can produce a highly accurate prediction results, a large amount of measured data is necessarily collected. In an urban quarry site where the number of blastings is limited, it is difficult to collect a lot of measured data. In this study, a new PPV prediction method using Weighted Non-negative Matrix Factorization (WNMF) is proposed. WNMF is a method that approximates a non-negative matrix (including missing data) to the product of two low-dimensional matrices and predicts the missing data. In addition, WNMF is one of the unsupervised learning methods, so it can predict PPV regardless of the amount of data. In this study, PPV was predicted using measured data from 100 sites at the Mikurahana quarry site in Japan. As a result, the proposed method showed higher accuracy when using measured data at 60 sites rather than 100 sites, and the root mean square error for PPV prediction decreased from 0.1759 (100 points) to 0.1378 (60 points).
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Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration
Fissha Y.
Applied Sciences (Switzerland) ( Applied Sciences (Switzerland) ) 13 ( 5 ) 2023年03月
研究論文（学術雑誌）
Rock blasting is one of the most common and cost-effective excavation techniques. However, rock blasting has various negative environmental effects, such as air overpressure, fly rock, and ground vibration. Ground vibration is the most hazardous of these inevitable impacts since it has a negative impact not only on the environment of the surrounding area but also on the human population and the rock itself. The PPV is the most critical base parameter practice for understanding, evaluating, and predicting ground vibration in terms of vibration velocity. This study aims to predict the blast-induced ground vibration of the Mikurahana quarry, using Bayesian neural network (BNN) and four machine learning techniques, namely, gradient boosting, k-neighbors, decision tree, and random forest. The proposed models were developed using eight input parameters, one output, and one hundred blasting datasets. The assessment of the suitability of one model in comparison to the others was conducted by using different performance evaluation metrics, such as R, RMSE, and MSE. Hence, this study compared the performances of the BNN model with four machine learning regression analyses, and found that the result from the BNN was superior, with a lower error: R = 0.94, RMSE = 0.17, and MSE = 0.03. Finally, after the evaluation of the models, SHAP was performed to describe the importance of the models’ features and to avoid the black box issue.
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Fragmentation Size Distribution Measurement by GNSS-Aided Photogrammetry at Real Mine Site
Hisatoshi Toriya, Zedrick Paul Tungol, Hajime Ikeda, Narihiro Owada, HYONGDOO JANG, Tsuyoshi Adachi, Itaru Kitahara, Youhei Kawamura
Mining ( Mining ) 2 ( 3 ) 438 - 448 2022年06月
研究論文（学術雑誌）
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Long-Term Sustainability of Copper and Iron Based on a System Dynamics Model
Larona Teseletso, Tsuyoshi Adachi
Resources ( Resources ) 11 ( 4 ) 2022年04月 [査読有り]
研究論文（学術雑誌） 国内共著
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Electricity generating resources portfolio optimization; Kenya’s case.
Ojiambo N. Malala, Tsuyoshi Adachi
14th International Symposium on East Asian Resources Recycling Technology, EARTH 2017 ( 14th International Symposium on East Asian Resources Recycling Technology, EARTH 2017 ) 2017年
研究論文（国際会議プロシーディングス）
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Evaluation of rare earth element in Mongolia using real option analysis
Sambuudorj Erdenebat, Tsuyoshi Adachi
14th International Symposium on East Asian Resources Recycling Technology, EARTH 2017 ( 14th International Symposium on East Asian Resources Recycling Technology, EARTH 2017 ) 2017年
研究論文（国際会議プロシーディングス）
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How macroeconomic factors influence volatility of metal prices?
Wenhua Li, Tsuyoshi Adachi
14th International Symposium on East Asian Resources Recycling Technology, EARTH 2017 ( 14th International Symposium on East Asian Resources Recycling Technology, EARTH 2017 ) 2017年
研究論文（国際会議プロシーディングス）
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Investment of long-term supply of electricity through coal production in Botswana
Larona Teseletso, Tsuyoshi Adachi
14th International Symposium on East Asian Resources Recycling Technology, EARTH 2017 ( 14th International Symposium on East Asian Resources Recycling Technology, EARTH 2017 ) 2017年
研究論文（国際会議プロシーディングス）
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What causes copper price changes: Stock markets or Speculation?
Kegomoditswe Koitsiwe, Tsuyoshi Adachi
14th International Symposium on East Asian Resources Recycling Technology, EARTH 2017 ( 14th International Symposium on East Asian Resources Recycling Technology, EARTH 2017 ) 2017年
研究論文（国際会議プロシーディングス）
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専門教育のテーマを視野に入れた初年次教育の検討――資源政策コースにおける2014年度～2016年度の取り組みから
田所聖志・宮本律子・三宅良美・中村裕・安達毅
秋田大学教養基礎教育研究年報 Vol. 13 ( 24 ) pp.13-24 2018年01月 [査読有り]
研究論文（大学，研究機関紀要） 国内共著
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専門教育のテーマを視野に入れた初年次教育の検討――資源政策コースにおける2014年度～2016年度の取り組みから
田所聖志, 宮本律子, 三宅良美, 中村裕, 安達毅
秋田大学教養基礎教育研究年報 Vol. 13 ( 24 ) pp.13-24 2018年01月 [査読有り]
研究論文（大学，研究機関紀要）
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MANJATE Elsa Pansilvania Andre, OHTOMO Yoko, ARIMA Takahiko, ADACHI Tsuyoshi, Miguel BENE Bernardo, KAWAMURA Youhei
Ｉｎｔｅｒｎａｔｉｏｎａｌ Ｊｏｕｒｎａｌ ｏｆ ｔｈｅ Ｓｏｃｉｅｔｙ ｏｆ Ｍａｔｅｒｉａｌｓ Ｅｎｇｉｎｅｅｒｉｎｇ ｆｏｒ Ｒｅｓｏｕｒｃｅｓ ( 日本素材物性学会 ) advpub ( 0 ) 2024年03月
<p>Mining methods selection (MMS) is one of the most critical and complex decision-making tasks in mine planning. The selection of underground mining methods is considered to be the most problematic due to the complexity associated with the orebody geometry, geology, and geotechnical properties. This study integrated artificial intelligence and machine learning in the MMS process by introducing the recommendation systems (RS) approach in MMS through the nonnegative matrix factorization (NMF) algorithm. As such, the weighted nonnegative matrix factorization (WNMF) algorithm is applied to build a model for underground MMS. The study's input dataset is based on thirty mining projects' historical data. In the experiments, we evaluate the capability of the WNMF to predict underground mining methods using five input variables: ore strength, host-rock strength, orebody thickness, shape, and dip. The results show that the WNMF model achieved an average prediction accuracy of 67.5%, considered reasonable and realistic. Further findings reveal that the WNMF model is sensitive to the imbalanced class dataset used in the experiments, thus, suggesting the need to improve the dataset's quality. These results reveal the model's effectiveness in predicting underground mining methods; therefore, with continuous improvement, the WNMF model can be effectively applied in underground MMS.</p>
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Deep Learning-Based Estimation of Muckpile Fragmentation Using Simulated 3D Point Cloud Data
Ikeda H.
Applied Sciences (Switzerland) ( Applied Sciences (Switzerland) ) 13 ( 19 ) 2023年10月
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Selected critical metals for a low-carbon future
Ojiambo M.N.
Mineral Economics ( Mineral Economics ) 36 ( 3 ) 519 - 534 2023年09月
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Application of Artificial Neural Network for the Prediction of Copper Ore Grade
Tsae N.B.
Minerals ( Minerals ) 13 ( 5 ) 2023年05月
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Research on vibration-based early diagnostic system for excavator motor bearing using 1-D CNN
Yandagsuren D.
Journal of Sustainable Mining ( Journal of Sustainable Mining ) 22 ( 1 ) 65 - 80 2023年
◆原著論文【 表示 ／ 非表示 】
◆国際会議プロシーディングス【 表示 ／ 非表示 】
◆⼤学，研究機関紀要【 表示 ／ 非表示 】
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学内活動 【 表示 ／ 非表示 】
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2014年04月-2018年03月国際資源学部資源政策コース コース長 (所属部局内委員会)
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2013年04月-2015年03月国際資源学教育研究センター長 (センター・施設長)