About

Moe Momayez is a Professor at the School of Mining Engineering & Mineral Resources and a Senior Member of the National Academy of Inventors at the University of Arizona. His research focuses on developing technologies to improve mine safety and productivity, with specific attention to miners' health and safety, geomaterials characterization, rock breakage, energy efficiency, and renewable energy. He leads efforts to address heat-related emergencies in hot underground mines through adaptive ventilation systems, heat strain risk factor identification, development of low thermal conductivity geo-foams using recycled mine tailings, and contingency cooling measures. His physics background enables him to explore low-temperature geothermal energy extraction from underground mines, electricity generation from photovoltaic panels on tailings, evaporation reduction in semiarid environments, and thermal efficiency improvements for solar power systems. Additionally, he investigates the effects of canceling surface charges in geomaterials on rock fragmentation and has extensive experience in geosensing and non-contact sensing systems for measuring properties of geomaterials. Since joining the University of Arizona in 2007, he has contributed significantly to research in mine ventilation, geomechanics, rock physics, and renewable energy, holding 14 patents related to thermally insulating materials, heat stress prediction, rock slope stability, and renewable energy from mines.

Research topics

• Environmental science
• Engineering
• Waste management
• Composite material
• Materials science
• Forensic engineering
• Mechanical engineering
• Mining engineering
• Geology

Selected publications

• Application of Machine Learning Methods for Predicting the Factor of Safety in Rock Slopes

  Geotechnics · 2026-02-03 · 1 citations

  articleOpen accessSenior author

  Factor of Safety (FOS) is a significant index to measure the stability condition of a rock slope in mining or civil engineering. In this paper, we evaluate and compare four different machine learning models, Gaussian Process Regressor (GPR), Support Vector Regressor (SVR), Random Forest (RF), and a hybrid genetic algorithm–multi-layer perceptron (GA-MLP), using two separate real-world datasets. The two separate datasets used in this study are from a previously conducted study on highway excavation with rock cutting in China, and another one in a mining site in Peru, with five geotechnical properties used as inputs, including slope height, slope angle, unit weight, cohesion, and friction angle. The two separate datasets were separated into training, validation, and testing datasets. The testing dataset of the models is unseen data used to assess model performance in an unbiased manner. The result shows that the SVR had the highest prediction accuracy, followed by GPR for the mining dataset, and GPR had the highest performance among all the models for the highway excavation dataset. From the boxplot, we can see that SVR, while having the highest predictive accuracy, has a larger variance in prediction compared to GPR for the mining dataset.

  PublisherDOI

• Surface Mine Planning Adaptations for the Integration of Autonomous Haulage Systems: A Review

  Preprints.org · 2026-05-19

  preprintOpen accessSenior author

  Autonomous Haulage Systems (AHS) are becoming increasingly popular in recent years as mining operations seek to improve productivity and remove workers from hazardous environments. The integration of this technology in a systematic manner implies not only change management in operations, but also deeper perspective into mine planning implications. Currently, existing literature describes AHS and their implementation guidelines with focus on operational safety and autonomous system architecture, without systematically addressing required planning-level adaptations. This study aims to identify how mine planning frameworks must evolve to accommodate autonomy in open-pit metal mining operations. A systematic review is conducted using the PRISMA methodology with emphasis on identifying the principal aspects of AHS that must be considered in mine planning strategies. Findings reveal major shifts in workforce dynamics, communication infrastructure, and haul road geometry, alongside ongoing debates regarding optimal road width and load channelization. The study highlights the need for (i) holistic approaches to haul road and mine design, that are aware of technology, geotechnical, and mineral aspects with a data driven perspective (ii) human-systems integration and new needs in human-autonomous collaboration, and (iii) empirical validation of workforce transition strategies for more effective and safe deployment.

  PublisherDOI

• Real-time contamination monitoring platform using fixed air sensing in underground mining environments

  Stochastic Environmental Research and Risk Assessment · 2026-03-31

  articleSenior author
  PublisherDOI

• Integrating Artificial Intelligence into Ventilation on Demand: Current Practice and Future Promises

  Sensors · 2026-02-05

  articleOpen accessSenior author

  The increasing depth and complexity of underground metal mining has raised ventilation energy demands and safety risks, driving the need for intelligent and more adaptive ventilation systems. Ventilation on Demand (VOD) systems dynamically adjust airflow using real-time operational and environmental data to improve energy efficiency while maintaining safety. Although VOD has been applied for over a decade, deeper and more extreme mining environments associated with critical minerals extraction introduce new challenges and opportunities. VOD systems rely on the tight integration of hardware, sensing, optimization-based control, and flexible infrastructure as mining operations evolve. The application of Artificial Intelligence (AI) introduces significant opportunities to further enhance and adapt VOD systems to these emerging challenges. This work presents a comprehensive review of the state of the art in AI integration within VOD technologies, covering sensing and prediction models, control strategies, and optimization frameworks aimed at improving energy efficiency, safety, and overall system performance. Findings show an increasing use of hybrid deep learning architectures, such as CNN-LSTM and Bi-LSTM, for forecasting, as well as AI-enabled optimization methods for sensor and actuator placement. Key research gaps include a reliance on narrow AI models, limited long-term predictive capabilities for maintenance and strategic planning, and a predominance of simulation-based validation over real-world field deployment. Future research directions include the integration of generative and generalized AI approaches, along with human-cyber-physical system (Human-CPS) designs, to enhance robustness and reliability under the uncertain and dynamic conditions characteristic of deep underground mining environments.

  PublisherDOI

• Impact-Echo: A technique for determining the mechanical properties of rocks

  2026-02-18 · 3 citations

  article1st authorCorresponding

  This paper presents the first application of the Impact-Echo (IE) technique for determining the elastic properties of rocks in the laboratory. The technique was developed at the United States National Institute of Standards and Technology for measuring the thickness and detecting flaws in concrete. It is based on transient stress wave propagation where the stress pulses generated by a mechanical impact at the surface undergo multiple reflections between the top and bottom of a slab. Knowing the thickness of the slab, the P and S wave velocities can be calculated with great accuracy. The dynamic elastic moduli can then be calculated from the measured P and S wave velocities using standard equations. Comparing the results of the Impact-Echo tests with standard static tests and accepted dynamic tests such as ultrasonic and resonance frequency, it is shown that Impact-Echo is the only dynamic testing method that produces the most consistent set of values for elastic properties such as Young’s modulus, bulk modulus, shear modulus and Poisson’s ratio. In addition, Impact-Echo data are obtained in a fraction of time compared to other testing methods because of minimal sample preparation requirements and data processing techniques. The authors provide a detailed description of the Impact-Echo method along with the results of experiments carried out on five different rock types using static and dynamic testing techniques.

  PublisherDOI

• Mine Planning Adaptations for the Integration of Autonomous Haulage Systems

  Preprints.org · 2026-02-26

  preprintOpen accessSenior author

  Autonomous Haulage Systems (AHS) are becoming increasingly popular in recent years as mining operations seek to improve productivity and remove workers from hazardous environments. The integration of this technology in a systematic manner implies not only change management in operations, but also deeper perspective into mine planning implications. Currently, existing literature describes AHS and their implementation guidelines with focus on operational safety and autonomous system architecture, without systematically addressing required planning-level adaptations. This study aims to identify how mine planning frameworks must evolve to accommodate autonomy in open-pit metal mining operations. A systematic review is conducted using the PRISMA methodology with emphasis on identifying the principal aspects of AHS that must be considered in mine planning strategies. Findings reveal major shifts in workforce dynamics, communication infrastructure, and haul road geometry, alongside ongoing debates regarding optimal road width and load channelization. The study highlights the need for (i) holistic approaches to haul road and mine design, that are aware of technology, geotechnical, and mineral aspects with a data driven perspective (ii) human-systems integration and new needs in human-autonomous collaboration, and (iii) empirical validation of workforce transition strategies for more effective and safe deployment.

  PublisherOA PDFDOI

• Monitoring System Guidance, Innovation and Future Applications

  2026-01-15

  book-chapter
  PublisherDOI

• MINDS: A Modular Multi-Agent Decision-Support Framework for Dynamic Strategic Mine Planning

  Mining · 2026-04-02

  articleOpen accessSenior author

  Strategic Mine Planning (SMP) creates the long-term economic baseline for mining operations, yet economic variability necessitates Dynamic Mine Planning (DMP) to rapidly stress-test those financial assumptions. Currently, this capability is hindered by fragmented software ecosystems that require manual data handoffs, slowing iteration and breaking the audit trail between market data and valuation models. While Generative AI affords an opportunity to automate these workflows, its adoption in the mining industry is stalled by concerns over data quality and the risk of uncritical acceptance of automated outputs. Addressing these challenges, this paper describes the Mine Intelligence and Decision Support (MINDS) framework. We present MINDS as a modular reference architecture that uses Large Language Model (LLM) agents to orchestrate the economic evaluation process while maintaining strict engineering oversight. The system integrates a conversational interface with a multi-agent assessment layer that acts as an adversarial review, assessing price assumptions against market intelligence before generating economic valuation scenarios. A proof-of-concept using the Marvin copper benchmark evaluates the framework, demonstrating automated request-to-report orchestration, execution stability with an average debate latency of 10.69 s and a transparent decision audit trail. These findings show that MINDS can systematize economic scenario analysis without sacrificing the governance and verification required for definitive feasibility studies.

  PublisherOA PDFDOI

• Cluster-Based Machine Learning Modeling for Particle Size Variability in SAG Mill Feed

  Mining Metallurgy & Exploration · 2026-04-22

  article
  PublisherDOI

• Blending Characterization for Effective Management in Mining Operations

  Minerals · 2025-08-22 · 2 citations

  articleOpen access

  Ore blending plays a critical role in ensuring feed consistency and optimizing downstream processes in the mining industry. Despite its importance, effective blending remains challenging due to ore variability and operational constraints. This review focuses exclusively on modern, data-driven blending methodologies, with particular emphasis on the application of data science and machine learning (ML) in predicting key process variables and supporting real-time decision-making. It discusses core challenges such as data quality, feature engineering, and model generalization, alongside enabling technologies including sensor integration, automation platforms, and real-time data acquisition systems. By consolidating the recent literature and highlighting emerging trends, this work outlines future directions for advancing intelligent blending systems and underscores the importance of standardized, high-quality data in the development of robust digital solutions for mineral processing.

  PublisherOA PDFDOI

Frequent coauthors

• Ferri Hassani

  32 shared

• Paloma Lazaro

  Arizona Geological Survey

  22 shared

• Krishna Muralidharan

  18 shared

• Zahra Hosseini

  Kyushu University

  17 shared

• Keith Runge

  University of Arizona

  17 shared

• Daniel Lévesque

  16 shared

• Kate Brown Requist

  Arizona Geological Survey

  13 shared

• Pampana Ratnaprasad Rao

  International Institute of Information Technology

  13 shared

Education

• PhD, Mining and Materials Engineering

  McGill University Faculty of Engineering

  1994

Awards & honors

• Senior Member, National Academy of Inventors
• President of the Environmental and Engineering Geophysical S…
• 14 patents and patent applications