bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design of a Meso-Scale Test of a Fracture Thermal Energy Storage (FTES) System
Authors: Burghardt J. , Lecampion B. , Möri A. , Linneman D. 
Published: August 2024

ABSTRACT:
Seasonal storage of thermal energy has the potential to both significantly reduce the total energy requirements for heating and cooling of buildings, and also allow for the flexibility to store thermal energy from intermittent sources. With approximately half of global energy consumption currently being used for heating and cooling, this represents an important path to reducing greenhouse gas (GHG) emissions. The concept of FTES is to drill two or more wells into a low permeability formation and then generate hydraulic fractures to create flow paths for water to circulate between the wells. Hot or cold thermal energy can then be stored in the surrounding rock mass by circulating hot or cold water through the fractures, which will heat or cool the rock mass. To recover the stored energy, ambient temperature water can be then circulated through the fractures, which will then be heated or cooled by the rock mass. Fractures inherently have a very large ratio of surface area to volume. This allows for very high heat fluxes to and from the rock mass to be achieved despite the relatively low thermal conductivity of most geologic formations. Because large fractures can be made with low-cost equipment and with only inexpensive and environmentally safe materials such as water and sand, the cost to construct even large FTES systems is expected to be quite low. A preliminary estimate is made for the size and operating conditions of a full-scale FTES system capable of heating a commercial building. The paper then presents a dimensional analysis of this system. The scaling parameters that come from this analysis are then used to design an intermediate-scale field test of a fracture thermal energy storage system (FTES) to be conducted at the Sanford Underground Research Facility (SURF) in Lead, SD.

1 INTRODUCTION
Approximately half of global energy consumption is used for heating and cooling. Because fossil fuels are used to meet most of this demand, heating and cooling of buildings produces a large portion of global greenhouse gas (GHG) emissions. Seasonal storage of thermal energy is a powerful technology to substantially reduce the energy required for heating and cooling of buildings and to reduce or eliminate the associated GHG emissions. This paper focuses on an emerging technology for seasonal thermal energy called fracture thermal energy storage (FTES). FTES is a technique for building a highly efficient heat exchanger by creating a carefully designed set of fractures in the ground below a building.
Semi-Automated Geotechnical Workflow Utilizing UAV Data for Enhanced Hazard Assessment and Safety at Rio Tinto Kennecott
Authors: Fahle L. , Bakken K. M. , Telfer J. , Williams C. 
Published: August 2024

ABSTRACT:
Spending time near highwalls, e.g., while performing geotechnical cell mapping, is a critical risk. Rio Tinto Kennecott (RTKC) is actively seeking out solutions to minimize this risk. To this end, RTKC employs Uncrewed Aerial Vehicles (UAVs) for 3D-model-based structural mapping. We developed a prototype of a semi-automated workflow to better utilize UAV data and enhance personnel safety near highwalls. Our workflow includes three main components: a GIS-enabled dashboard to locate relevant UAV data, semi-automated bench-scale kinematic analysis and risk evaluation, and another GIS-enabled dashboard to share results. We show that our workflow allows for efficient geotechnical data collection, enables a high-level pit-wide kinematic risk assessment, and provides data for risk communication and further analysis. Our proposed system has the potential to further enhance RTKC's geotechnical hazard assessment processes and therefore improve safety around highwalls.

INTRODUCTION

1.1. Background and Context
Safety around highwalls in mining is crucial due to the potential hazards they pose. Geotechnical hazards are one of the major risks associated with highwalls. Highwalls can collapse without warning, leading to severe injuries, loss of life, and destruction of equipment.
The Mine Safety and Health Administration (MSHA) has issued safety alerts and guidelines emphasizing the importance of conducting highwall examinations, assuring hazards are addressed, and developing plans for highwall controls to prevent accidents and fatalities (Mining Safety and Health Administration, 2024). Research projects, such as those conducted by the National Institute for Occupational Safety and Health (NIOSH), focus on addressing rockfall hazards and developing slope monitoring techniques to improve highwall safety in open pit mining (Centers for Disease Control and Prevention, 2020).
Despite these risks, access to mining highwalls is required for various tasks including installation and maintenance of infrastructure such as dewatering wells or geotechnical monitoring equipment. Proximity to highwalls is also critical for geological mapping, which provides valuable input data for geotechnical analysis. Geotechnical analyses are required in the mine design process to establish safe and efficient mining operations.
To manage highwall related hazards, Rio Tinto's safety standard "D3 – Management of slope geotechnical hazards standard" (Rio Tinto, 2019) prescribes, among other controls, training to employees to recognize and report highwall hazards, thorough geotechnical examinations, and plans for highwall control. Additionally, the integrity of highwalls is maintained through early detection of movement on highwalls and preventative measures, such as design changes and blasting and mining controls to avoid accidents and ensure productivity (Reeves et al., 2001).
Study on the Mechanism of Wellbore Stability and Drilling Fluid Technology Countermeasures in the Ultra-Deep Fractured Formation of Southwest Tarim Depression
Authors: Fang Zheng , Chen Mian , Li Jiaxin , Zhang Yingxi , Zhang Zhen , Ma Weizhen , Lu Yunhu , Wang Wansheng 
Published: August 2024

ABSTRACT:
The Southwest Tarim Depression is an important production succession area of the Tarim oilfield, but in the course of exploration, due to the complexity of ultra-deep fracture formation, the mechanism of wellbore instability is not clear, and reasonable drilling fluid technology countermeasures cannot be proposed, which brings great challenges to efficient drilling. In this paper, starting from the lithology, physical and chemical properties, microstructure and rock mechanics characteristics of ultra-deep fracture formation in southwest depression, the mechanism of wellbore instability is clarified, a suitable calculation model of wellbore stability is established, and a reasonable safe drilling fluid density window is proposed. Technical countermeasures of drilling fluid are put forward from the perspective of engineering geomechanics. The results show that the ultra-deep fractured formation is dominated by dolomite, showing weak water-sensitive characteristics and developing multi-scale fractures, which makes drilling fluid invade the formation and cause wellbore collapse. By calculating the corresponding sealing coefficient plate and safe drilling fluid density window, it is clear that when the sealing coefficient is above 0.7, the diameter expansion ratio (≤ 15%) can be kept within the safe construction range. The appropriate sealing materials can be selected to improve the sealing coefficient of drilling fluid, which can also increase the sealing property of drilling fluid and improve the condition of wellbore instability in ultra-deep fractured formations.

1. INTRODUCTION
The geological conditions of the ultra-deep fractured formations in the southwest Tarim depression are highly complex, characterized by widespread natural fractures and significant differences in structural stress. The presence of fractures, joints, and other weak planes exacerbates the anisotropy of formation strength, affecting wellbore stability. Instabilities in wellbores within fractured reservoirs are more severe compared to conventional reservoirs [1-2]. The lubricating effect generated by drilling fluid infiltration into the formation leads to a reduction in rock strength [3]. Consequently, frequent occurrences of wellbore instability accidents when drilling encounters such formations greatly impede the efficient development of oil and gas resources in the southwest Tarim depression.
Wavelet Transform and Hidden Markov Model Application for Unsupervised Clustering of Microseismic Events from CO2 Injection
Authors: Willis R. M. , Yoon H. , Harding J. L. 
Published: August 2024

ABSTRACT:
Injection of CO2 increases pore pressures that trigger complex subsurface interactions, leading to slip or reactivation of pre-existing faults/fractures and/or creating fractures. Understanding the subsurface response is key to preserving the integrity of the storage site, optimizing the injection, and mitigating the risk of seismic hazards. Machine learning (ML) models have shown the potential to improve our understanding of complex earth systems by identifying subtle changes in patterns of seismic and microseismic data. Furthermore, ML has shown great promise in revealing patterns in time-dependent spectral properties of (micro-) seismic data to identify differences in subsurface response. In this study, a wavelet transform scheme is implemented to isolate high energy frequency bands within microseismic data from the Illinois Basin Decatur Project (IBDP). Subsequently, an unsupervised ML method is used to identify various subsurface responses attributed to CO2 injection at the IBDP. Specifically, the unsupervised method known as SpecUFEx is used to further reduce the dimensionality of the reconstructed high energy micro-seismic spectrograms, solve for characteristic fingerprints, and apply K-means clustering to group similar fingerprints. Finally, a comparison is made between our clustering results and an implementation of SpecUFEx where the full frequency spectrum of the microseismic data was used.

1 INTRODUCTION
Carbon capture and storage (CCS) has emerged as a critical technology in the global effort to mitigate climate change by reducing carbon dioxide (CO2) emissions from industrial processes and power generation. The concept of CCS involves capturing CO2 emissions from large point sources such as power plants and industrial facilities, transporting the captured CO2 to suitable storage sites, and securely storing it underground in geological formations to prevent its release into the atmosphere. This approach not only helps reduce greenhouse gas emissions but also enables the continued use of fossil fuels while transitioning to cleaner energy sources. As nations strive to meet ambitious climate targets, CCS is increasingly recognized as a key component of the portfolio of solutions needed to achieve significant and sustained reductions in CO2 emissions on a global scale. However, one primary risk of CCS is induced seismicity. Injecting CO2 into subsurface formations increases pore pressure, leading to changes in local stress conditions and potentially triggering micro-seismic events within basement rock. Accurate monitoring and modeling are imperative for the success of CCS projects, ensuring efficiency, safety, and societal acceptance.
Growth Parameter Conditioning of Genetic Discrete Fracture Networks to Trace Length Distribution Data
Authors: Kim J. , Choi J. , Leem J. , Song J.-J. 
Published: August 2024

ABSTRACT:
Genetic discrete fracture network (DFN) modeling is a dynamic modeling approach that simulates fracture growth processes in DFN generation. The modeling procedure incorporates growth-related input parameters, such as the growth time, step size, and coefficients of nucleation and growth, which are difficult to deduce directly from survey data. This study introduces a method for conditioning these growth parameters to trace length distribution using an optimization procedure, where the growth parameters are fine-tuned to produce genetic DFNs that align with the observed distribution. The optimization objective function is formulated to minimize the difference in trace length distributions and the number of traces between the observed trace map and that derived from a genetic DFN. In case studies involving synthetic and real trace maps, the conditioned genetic DFNs reproduced trace maps that exhibit a high level of fitness to the observed data. Considering the representation of complex growth processes and fracture interactions, these models can provide a robust foundation for DFN extrapolation beyond the surveyed areas.

1. INTRODUCTION
Natural fracture networks can be modeled in the form of discrete fracture networks (DFNs) that capture the spatial characteristics of the observed network. In DFNs, fractures are simplified as discrete objects, commonly as disks, which replicate the geometric properties of fractures. The geometric properties of these fracture disks, including size and orientation, can be determined by analyzing the distributions of surveyed data and directly sampling values from these distributions to match the observed data. This modeling approach is termed as "stochastic" or "Poisson" DFN modeling, where fractures are randomly generated by corresponding statistical models (Lei et al., 2017).
While stochastic DFNs follow the statistical models of geometric properties, such as size and orientation, they exhibit clear limitations in reproducing the organization or topology of fracture networks, especially the specific fracture terminations in the form of T-type intersections (Selroos et al., 2022). The stochastic DFN approach fundamentally lacks physical integrity as it is purely statistical and does not consider the physical process involved in fracture network generation.
Numerical Simulation of Toppling Failure in Sedimentary Rock Slope Cuts with Alternating Soft and Hard Bands
Authors: Ashraf M. , Emad M. Z. , Waqas M. , Moazzam H. 
Published: August 2024

ABSTRACT:
Rock slope stability is a major concern in mega engineering projects, and it has been a major focus of engineering geology for over three centuries. The toppling failure mode is considered as one of the most complex and challenging to stabilize. The complexity of slope failures increases with a combination of mechanisms and the presence of different geological units and structures. Numerical modeling is a powerful tool for analyzing and simulating rock slope stability. This paper aims to assess and propose stabilization methods for toppling failure and complex toppling failure, using numerical modeling software FLAC 2D. The paper introduces a technique for assessing toppling failure in alternating soft-hard rock bands during adverse conditions and proposes innovative stabilization methods based on simulation effects. The results suggest that numerical modeling software can effectively assess toppling failure, design support, and reinforcement methods, and help predict and reduce the risk of slope collapse in toppling zones through appropriate stabilization techniques.

1. INTRODUCTION
The stability of rock slopes, as explained by Hoek (1981), is a critical aspect of numerous geotechnical engineering endeavors, owing to their susceptibility to failure triggered by diverse factors such as geological conditions (Wyllie and Mah, 2004), environmental forces, and material properties. Rock slope failures can result in severe consequences, encompassing loss of life, infrastructure damage, and economic disruption (Evans, 2006).
For over three centuries, engineering geology has been truly involved in investigating slope stability, which delineates capacity of a slope to withstand internal and external forces without collapsing (Azarafza, 2021). This aspect remains a captivating and expansive domain, encompassing various manifestations of rock and soil failures, as expounded by Li (2022). Ensuring the safety of individuals and structures necessitates the deployment of dependable and precise methods for assessing rock slope stability. Understanding the prevailing failure mechanisms and delineating stable and unstable zones within a slope are pivotal prerequisites for accurate assessment (Mat, 2023). Hoek (1981) categorizes simplified failure modes into four types: plane, wedge, circular, and toppling.
Examining the Anisotropic Strength of Plain and Steel Fiber Reinforced Shotcrete
Authors: Khan M. U. , Waqas M. , Emad M. Z. , Tahir M. U. 
Published: August 2024

ABSTRACT:
Shotcrete, a crucial component for initial rock support in underground mining and civil projects, significantly influences the adequacy of support systems. Evaluating the strength of shotcrete involves conducting uniaxial compressive strength (UCS) tests on the extracted core samples. Typically, shotcrete cores are obtained along the direction of application, leading to an oversight of its strength perpendicular to this direction. Consequently, the true bearing strength of shotcrete is neglected, despite higher stresses acting normal to the application direction. While the anisotropic behavior of shotcrete is documented, comprehensive studies on this scale and nature are lacking. This research addresses this gap by presenting extensive testing of shotcrete cores extracted from tunnels of a mega hydroelectric project in Pakistan. The study encompasses 7 and 28-day strength tests of shotcrete cores, shedding light on the often-overlooked perpendicular strength. Additionally, the investigation extends to the anisotropic behavior of steel fiber-reinforced shotcrete, revealing that shotcrete exhibits increased strength perpendicular to its application direction. This exploration contributes valuable insights to the understanding of shotcrete behavior, emphasizing its multifaceted strength characteristics.

1. INTRODUCTION
The New Austrian Tunneling Method (NATM) has revolutionized underground construction by advocating for controlled deformation of the host ground during excavation (Boldini et al. 2005; Zhang et al. 2018). This innovative approach employs a thin layer of sprayed concrete, commonly known as shotcrete, to reinforce the tunnel walls and reduce the load on the final linings or subsequent rock support elements. Shotcrete serves as a versatile and effective initial rock support component, sealing fissures and cracks while preventing further disintegration and weathering of the surrounding rocks (Galan et al. 2019a; Galan et al. 2019b). By providing a protective barrier, shotcrete enhances safety for workers and facilitates smoother excavation cycles, instilling confidence in laborers as they advance into new phase of construction (Reny and Ginouse 2014) (Ginouse et al. 2015; Lemay et al. 2014).
The significance of shotcrete in tunneling cannot be overstated. Shotcrete acts as a critical component in the initial support system for underground constructions, offering stability and durability to tunnel walls. Its versatility and adaptability make shotcrete an indispensable tool for tunneling operations, particularly in challenging geological conditions where ground stability is a concern.
Multi-Source Data Fusion and Knowledge Presentation for Shield Tunnel Maintenance Work Based on Knowledge Graph
Authors: Pang H. J. , Zha C. C. , Jia F. , Xue Y. D. 
Published: August 2024

ABSTRACT:
The study tackles operational challenges in shield tunnel maintenance, focusing on multi-source date fusion and decision-making suggestion. Employing a multi-disciplinary approach spanning civil engineering, artificial intelligence, and engineering management, the study establishes an intelligent decision-making system based on knowledge graph for shield tunnel maintenance work. Firstly, a multi-data processing program is developed for dealing with multi-source tunnel data, encompassing diverse dimensions reflecting the structural state of shield tunnels. Comprehensive perception of tunnel structure state is achieved through the integration of tunnel internal surface images and 3D point cloud information. Secondly, to address the specialized expert knowledge and experience in tunnel maintenance work, the study explores methods for knowledge representation, extraction, and integration. Leveraging several natural language processing techniques, including ontology and semantic rules. Lastly, a knowledge graph for shield tunnel maintenance is then constructed by Neo4j, integrating data from both multi-source tunnel data and expert knowledge to quantify tunnel structural health and formulate operational strategies for maintenance work. Through the case study, the tunnel knowledge graph is demonstrated to be effective and advanced in tunnel maintenance work.

1. INTRODUCTION AND BACKGROUND
In recent years, China's major cities have experienced significant and rapid development in their urban metro systems, aiming to enhance urban transport capacity and address the commuting needs of residents. Zhang et al. (2023) reported, by December 31, 2023, the urban metro network in China extended to 11,232 km, facilitating 23.71 billion passenger trips, resulting in an annual passenger turnover of 197.8 billion trips per kilometer. The growth in urban metro mileage is visually represented in Fig.1. Since 2013, the operational mileage of China's urban metro systems has consistently increased, demonstrating a rising annual growth rate and robust development in domestic urban rail transit. Fig.2. illustrates the total operational rail transit mileage for major cities in 2023, with Shanghai leading with a total of 965 km.
Situated on the eastern coast of China, Shanghai, as a developed city, relies significantly on its transportation infrastructure to ensure normal urban operations. The operation of the urban metro service exposes the metro tunnel structure to various risk factors, including periodic train loads, segment aging, and surrounding construction activities.