研究等業績 - 原著論文 - 安達　毅

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

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).

DOI

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.

DOI

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）  

DOI

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

DOI

One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction

Kursat Kilic, Hisatoshi Toriya, Yoshino Kosugi, Tsuyoshi Adachi, Youhei Kawamura

Applied Sciences (Switzerland) ( Applied Sciences (Switzerland) )  12 ( 5 )   2022年03月  [査読有り]

研究論文（学術雑誌）  

DOI

Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV

Brian Bino Sinaice, Narihiro Owada, Hajime Ikeda, Hisatoshi Toriya, Zibisani Bagai, Elisha Shemang, Tsuyoshi Adachi, Youhei Kawamura

Minerals ( Minerals )  12 ( 2 )   2022年02月  [査読有り]

研究論文（学術雑誌）  

DOI

Exploring the optimal reverse supply chain for e-waste treatment under Chinese government subsidy

Juntao Wang, Wenhua Li, Nozomu Mishima, Tsuyoshi Adachi

Waste Management ( Waste Management )  137   128 - 138   2022年01月  [査読有り]

研究論文（学術雑誌）  

DOI

Stratified state aggregation (SSA) approach in real option valuation: combining price and grade uncertainties in tin mining projects

Mohammad Rahman Ardhiansyah, Tsuyoshi Adachi, Junichiro Oda

Mineral Economics ( Mineral Economics )  36 ( 3 ) 371 - 381   2022年  [査読有り]

研究論文（学術雑誌）  

DOI

Deep Learning as an Early Detection System for Rotary Percussion Drilling Malfunctions

Kosugi Y.

International Journal of the Society of Material Engineering for Resources ( International Journal of the Society of Material Engineering for Resources )  25 ( 2 ) 205 - 211   2022年

研究論文（学術雑誌）  

To mine in the underground, the method of blasting and blast hole drilling methods are mainly, and widely accepted. The hole drilling methods are done with rotary percussion drill. However, there are problems in terms of diffi culty of operating and mining cost resulting from its failure occurs, and thus it is hard for mining companies to fi nd a way of mining underground effi ciently, profi tability, and safely. From this background, it is necessary to build the early detection system for drill bit failure. This system needs the technology of CNN (Convolutional Neural Network Smart Mining, which is the process of using information, autonomy, and technology to improve safety, reduce operating costs, and improve mine site productivity. In this research, drilling vibration from rotary percussion drill is transmitted as acceleration waveform and used as input data for building the system. The data is collected replacing the kinds of diameter of bit or drilling condition. This data is for developing the model introduced CNN to detect the difference between Normal drilling and the other kinds of drilling with something error. For Firstly, batch of waveform data is input model as training data to make the model recognize the data pattern. Secondly, validation process confi rms the correct answer rate against the training data, and then, the test for the model is practiced. Finally, by comparing each accuracy in phase of test from 4 types of models built with diff erent kinds of data and the ideal way of the input waveform data is found.

DOI CiNii Research

Dimensional Reduction of Rock Hyperspectral Signatures and Classiflcation Based on a NCA Method used in developing a UAV Multispectral Imaging System

Sinaice B.B.

International Journal of the Society of Material Engineering for Resources ( International Journal of the Society of Material Engineering for Resources )  25 ( 1 ) 102 - 108   2022年

研究論文（学術雑誌）  

The adoption of hyperspectral imaging has had positive feedback in multiple industries, especially those heavily reliant on the visual analysis of subjects. Reasons for such are primarily due to the high accuracies achievable from processing high dimensional data. Nevertheless, hyperspectral data is said to possess a' dimensionality curse'. This phenomenon, deems it computationally demanding and difflcult to employ in rapid fleld investigations such as the use of drone-mounted spectral cameras to distinguish rocks. To counter this, this study proposes the employment of a method of reducing the number of dimensions used to highlight the most characteristic feature bands referred to as Neighbourhood Component Analysis(NCA). NCA aided in disregarding redundant bands from 204 dimensionalities, to a still highly capable 5 bands dimensionality, which coincides with the current production of 5-band detection drones. To process this data, several machine learning(ML) algorithms were run in order to perform spectral classiflcation of rocks based on the 5 NCA deflned bands. This study's novel flndings show that one is able to acquire with NCA and ML, 5 bands, with a post-optimization average global accuracy of 95.4%. Such capabilities are highly sufflcient considering the magnitude of the dimensionality reduction combined with the potential fleld drone applicability.

DOI CiNii Research

Visualization of 1D CNN Lithology Identifi cation Model from Rotary Percussion Drilling Vibration Signals Using Explainable Artifi cial Intelligence Grad-CAM

Senjoba L.

International Journal of the Society of Material Engineering for Resources ( International Journal of the Society of Material Engineering for Resources )  25 ( 2 ) 224 - 228   2022年

研究論文（学術雑誌）  

In recent years deep learning has gained a lot of popularity because of its ability to work on complex tasks. It has been used in many industries to optimize operations and to help in decision-making. Deep neural networks have often been referred to as ' Black boxes', that is they take inputs and give outputs with high accuracies without giving an insight into how they work. It is important to demystify deep neural networks to verify that they are looking at the correct patterns. This paper proposes the use of Gradient-Weighted Class Activation Mapping (Grad-CAM) to visualize the behavior of lithology identification models that use drill vibrations as input to a one- dimensional convolutional neural network (1D CNN). The lithology identifi cation models, time acceleration, and frequency model had 99.8% and 99.0% classifi cation accuracy. The models could distinguish between granite and marble rock based on vibration signatures. With the use of Grad-CAM, it was possible to make the 1D CNN models transparent by visualizing the regions of input that were important for predictions. The Grad-CAM results indicated that the lithology identifi cation models successfully learned the signifi cant frequencies contained in each rock's vibration signal.

DOI CiNii Research

Closed-loop supply chain under different channel leaderships: considering different deposit–refund systems practically applied in China

Juntao Wang, Wenhua Li, Nozomu Mishima, Tsuyoshi Adachi

Journal of Material Cycles and Waste Management ( Journal of Material Cycles and Waste Management )  23 ( 5 ) 1765 - 1776   2021年09月  [査読有り]

研究論文（学術雑誌）  

DOI

Optimal channel configuration for implementing remanufacturing business in a closed-loop supply chain

Juntao Wang, Wenhua Li, Nozomu Mishima, Tsuyoshi Adachi

International Journal of Information Systems and Supply Chain Management ( International Journal of Information Systems and Supply Chain Management )  14 ( 1 ) 113 - 132   2021年01月  [査読有り]

研究論文（学術雑誌）  

DOI

Japan’s critical metals in the medium term: a quasi-dynamic approach incorporating probability

Ojiambo N. Malala, Tsuyoshi Adachi

Mineral Economics ( Mineral Economics )  35 ( 1 ) 87 - 101   2021年  [査読有り]

研究論文（学術雑誌）  

DOI

Future availability of mineral resources: ultimate reserves and total material requirement

Larona Teseletso, Tsuyoshi Adachi

Mineral Economics ( Mineral Economics )  36 ( 2 ) 189 - 206   2021年  [査読有り]

研究論文（学術雑誌）  

DOI

Ecological footprint and total material requirement as environmental indicators of mining activities: Case studies of copper mines

Shinsuke Murakami, Taiga Takasu, Kamrul Islam, Eiji Yamasue, Tsuyoshi Adachi

Environmental and Sustainability Indicators ( Environmental and Sustainability Indicators )  8   2020年12月  [査読有り]

研究論文（学術雑誌）  

DOI

Portfolio optimization of electricity generating resources in Kenya

Ojiambo N. Malala, Tsuyoshi Adachi

Electricity Journal ( Electricity Journal )  33 ( 4 )   2020年05月  [査読有り]

研究論文（学術雑誌）  

DOI

Formalisation of informal collectors under a dual-recycling channel: A game theoretic approach

Juntao Wang, Wenhua Li, Nozomu Mishima, Tsuyoshi Adachi

Waste Management and Research ( Waste Management and Research )  38 ( 5 ) 576 - 587   2020年05月  [査読有り]

研究論文（学術雑誌）  

DOI

An input - output approach in analyzing Indonesia’s mineral export policy

Rini Novrianti Sutardjo Tui, Tsuyoshi Adachi

Mineral Economics ( Mineral Economics )  34 ( 1 ) 105 - 112   2020年  [査読有り]

研究論文（学術雑誌）  

DOI

Evaluation of long-term silver supply shortage for c-Si PV under different technological scenarios

Wenhua Li, Tsuyoshi Adachi

Natural Resource Modeling ( Natural Resource Modeling )  32 ( 1 )   2019年02月  [査読有り]

研究論文（学術雑誌）  

DOI

Optimization on long term supply allocation of Indonesian coal to domestic market

Fadhila Achmadi Rosyid, Tsuyoshi Adachi

Energy Systems ( Energy Systems )  9 ( 2 ) 385 - 414   2018年05月  [査読有り]

研究論文（学術雑誌）  

DOI

Optimization on long term supply allocation of Indonesian coal to domestic market

Fadhila Achmadi Rosyid, Tsuyoshi Adachi

Energy Systems     pp. 385-414   2018年01月  [査読有り]

研究論文（学術雑誌）   国内共著

Evaluation of long-term silver supply shortage for c-Si PV under different technological scenarios

Wenhua Li, Tsuyoshi Adachi

Natural Resource Modeling     2018年01月  [査読有り]

研究論文（学術雑誌）   国内共著

The Role of Financial Speculation in Copper Price

Kegomoditswe Koitsiwe, Tsuyoshi Adachi

Applied Economics and Finance     pp.87-94   2018年01月  [査読有り]

研究論文（学術雑誌）   国内共著

DOI

Evaluation of long-term silver supply shortage for c-Si PV under different technological scenarios

Wenhua Li, Tsuyoshi Adachi

Natural Resource Modeling     2018年01月  [査読有り]

研究論文（学術雑誌）  

Optimization on long term supply allocation of Indonesian coal to domestic market

Fadhila Achmadi Rosyid, Tsuyoshi Adachi

Energy Systems     pp. 385-414   2018年01月  [査読有り]

研究論文（学術雑誌）  

Long-term demand and supply of non-ferrous mineral resources by a mineral balance model

Koji Tokimatsu, Shinsuke Murakami, Tsuyoshi Adachi, Ryota Ii, Rieko Yasuoka, Masahiro Nishio

Mineral Economics ( Mineral Economics )  30 ( 3 ) 193 - 206   2017年10月  [査読有り]

研究論文（学術雑誌）   国内共著

DOI

Quantitative estimation of resource nationalism by binary choice logit model for panel data

Wenhua Li, Tsuyoshi Adachi

Resources Policy ( Resources Policy )  53   247 - 258   2017年09月  [査読有り]

研究論文（学術雑誌）   国内共著

DOI

Linkages between mining and non-mining sectors in Botswana

Kegomoditswe Koitsiwe, Tsuyoshi Adachi

Mineral Economics ( Mineral Economics )  30 ( 2 ) 95 - 105   2017年07月  [査読有り]

研究論文（学術雑誌）   国内共著

DOI

Estimation of the Environmental Impact for Recycling Blast Furnace Slag with a Hydrothermal Reaction Based on Life Cycle Inventory Data

Soon-Jae Tae, Tsuyoshi Adachi, Kazuki Morita

ISIJ International   Vol. 57 ( Issue 1 ) pp. 189-192   2017年01月  [査読有り]

研究論文（学術雑誌）   国内共著

Estimation of the Environmental Impact for Recycling Blast Furnace Slag with a Hydrothermal Reaction Based on Life Cycle Inventory Data

Soon-Jae Tae, Tsuyoshi Adachi, Kazuki Morita

ISIJ International   Vol. 57 ( Issue 1 ) pp. 189-192   2017年01月  [査読有り]

研究論文（学術雑誌）  

Coal Mining in Indonesia: Forecasting by the Growth Curve

Fadhila Achmadi Rosyid, Tsuyoshi Adachi

Mineral Economics   Vol. 29 ( Issue 2 ) pp.71-85   2016年01月  [査読有り]

研究論文（学術雑誌）   国内共著

DOI

Forecasting on Indonesian Coal Production and Future Extraction Cost: A Tool for Formulating Policy on Coal Marketing

Fadhila Achmadi Rosyid, Tsuyoshi Adachi

Natural Resources   Vol. 7   pp. 677-696   2016年01月  [査読有り]

研究論文（学術雑誌）   国内共著

エネルギー消費を考慮した長期需給モデルの開発と非鉄金属資源の持続的供給評価に関する研究

安達毅, 時松宏治, 村上進亮, 安岡理恵子, 井伊亮太

Journal of Japan Society of Energy and Resources   Vol.36 ( No.3 ) pp.1-10   2015年01月  [査読有り]

研究論文（学術雑誌）   国内共著

Relationship between Mining Revenue, Government Consumption, Exchange Rate and Economic Growth in Botswana

Kegomoditswe Koitsiwe, Tsuyoshi Adachi

Accounting and Administration (Contadur?a y Administraci?n)   Vol.60 (S1)   pp. 133-148   2015年01月  [査読有り]

研究論文（学術雑誌）   国内共著

東京大学における実験系不明廃棄物の回収と処理

冨安文武乃進, 野原正雄, 安達毅, 布浦鉄兵, 中島典之, 戸野倉賢一, 苅間理介, 横山道子, 吉川健, 辻佳子, 山本和夫, 新井充, 尾張真則

環境と安全   Vol.4 ( No.1 ) pp25-37   2013年01月  [査読有り]

研究論文（学術雑誌）   国内共著

A Model for Prediction of Neutralizer Usage and Sludge Generation in the Treatment of Acid Mine Drainage from Abandoned Mines: Case Studies in JapanJapan

Ryu Koide, Chiharu Tokoro, Shinsuke Murakami, Tsuyoshi Adachi and Akihiro Takahashi

Mine Water and the Environment   Vol. 31 ( No.4 ) pp. 287-296   2012年01月  [査読有り]

研究論文（学術雑誌）   国内共著

日本近海における海底熱水鉱床開発のリアルオプション分析

小濱　真, 安達　毅, 所　千晴

リアルオプション研究   4(1)   pp.101-116   2011年01月  [査読有り]

研究論文（学術雑誌）   国内共著

改良SSA 法による多段階投資からなる資源開発プロジェクトのリアルオプション分析

安達　毅, 茂木源人, 足達哲男

Journal of MMIJ   124(9)   pp.576-582   2008年01月  [査読有り]

研究論文（学術雑誌）   国内共著

Life cycle inventory for base metal ingots production in Japan including mining and mineral processing processes by cost estimating system database

Tsuyoshi Adachi, Gento Mogi

Trans. Nonferrous Met. Soc. China   Vol.17   pp.s131-135   2007年01月  [査読有り]

研究論文（学術雑誌）   国内共著

鉱山費用推定システムによる採掘・選鉱プロセスを考慮した銅地金生産のCO2排出に関するインベントリ分析

安達 毅, 茂木源人

日本LCA学会誌   2(3)   pp.238-245   2006年01月  [査読有り]

研究論文（学術雑誌）   国内共著

面内等方性を考慮した膨潤構成モデルについて

金 相泰, 山冨二郎, 茂木源人, 安達 毅, 玉田康二

資源と素材   121(10,11)   pp.498-505   2005年01月  [査読有り]

研究論文（学術雑誌）   国内共著

費用推定データベースによる採掘・選鉱プロセスのライフサイクルインベントリの推計

安達 毅, 茂木源人

資源と素材   121(12)   pp.590-596   2005年01月  [査読有り]

研究論文（学術雑誌）   国内共著

Simulation of Blast Vibration Controlled by Delay Blasting

Mogi G., Adachi T., Tamada K., Ihara A., Arimatsu T., Kaburaki H., Eguchi K., Ogata Y.

Science and Technology of Energetic Materials   65(2)   pp.48-53   2004年01月  [査読有り]

研究論文（学術雑誌）   国内共著

Simulation of Blast Vibration Controlled by Delay Blasting

Mogi G., Adachi T., Tamada K., Ihara A., Arimatsu T., Kaburaki H., Eguchi K., Ogata Y.

Science and Technology of Energetic Materials   65(2)   pp.48-53   2004年01月  [査読有り]

研究論文（学術雑誌）   国内共著

Material Flow Accounting for Metals in Japan

Shinsuke Murakami, Megumi Yamanoi, Tsuyoshi Adachi, Gento Mogi and Jiro Yamatomi

Materials Transactions   45(11)   pp.3184-3193   2004年01月  [査読有り]

研究論文（学術雑誌）   国内共著

石灰石鉱山における採掘プロセスのCO2排出量に関するインベントリ分析

安達 毅, 茂木源人, 山冨二郎

資源と素材   117(6)   pp.520-526   2001年01月  [査読有り]

研究論文（学術雑誌）   国内共著

海外の露天掘鉱山に関する現状分析とその背景

増田信行, 安達 毅, 山冨二郎

資源と素材   117(7)   pp.591-598   2001年01月  [査読有り]

研究論文（学術雑誌）   国内共著

改良４次元ネットワーク緩和法を用いた露天掘り鉱山の最適生産規模と生産計画

茂木源人, 安達 毅, 赤池敦史, 山冨二郎

資源と素材   117(7)   pp.599-603   2001年01月  [査読有り]

研究論文（学術雑誌）   国内共著

オープンピット最終形状の最適化手法(BPITC法)の提案と金属品位推計シミュレーションデータを用いた初期F/S

佐々木久郎, ディンディウェ・チャールズ, 安達 毅

資源と素材   117(7)   pp.604-612   2001年01月  [査読有り]

研究論文（学術雑誌）   国内共著

非鉄金属代替の最適化モデルの開発

安達 毅, 塙 隆二, 茂木源人, 山冨二郎

資源と素材   117(11)   pp.847-854   2001年01月  [査読有り]

研究論文（学術雑誌）   国内共著

鉱物資源グローバルモデルの開発　－　銅供給の超長期シミュレーション　－

安達 毅, 茂木源人, 山冨二郎, 村上進亮, 中山 徹

資源と素材   117(12)   pp.931-939   2001年01月  [査読有り]

研究論文（学術雑誌）   国内共著

東京都23区における排出物処理経路の最適化

村上進亮, 安達 毅, 茂木源人, 山冨二郎

資源と素材   117(12)   pp.955-960   2001年01月  [査読有り]

研究論文（学術雑誌）   国内共著

一般廃棄物リサイクリングの産業間における最適負担配分モデル

安達 毅, 茂木源人, 小谷明生, 山冨二郎

資源と素材   115(13)   pp.951-957   1999年01月  [査読有り]

研究論文（学術雑誌）   国内共著

段発発破による局地的振動制御に関する一考察

茂木源人, 星野達也, 安達 毅, 山冨二郎, Kou S.

火薬学会誌   60(5)   pp.233-239   1999年01月  [査読有り]

研究論文（学術雑誌）   国内共著

Resource Depletion Calculated by the Ratio of the Reserve Plus Cumulative Consumption to the Crustal Abundance for Gold

Takashi Nishiyama, Tsuyoshi Adachi

Nonrenewable Resources   4   pp.253-261   1995年01月  [査読有り]

研究論文（学術雑誌）   国内共著

鉱物資源枯渇と耐用年数

西山 孝, 安達 毅

資源と素材   109(6)   pp.473-477   1993年01月  [査読有り]

研究論文（学術雑誌）   国内共著