Accurate prediction of concealed water-bearing structures ahead of tunnel excavation is a critical challenge for ensuring construction safety, controlling project costs and maintaining schedules. Conventional geophysical exploration methods often fail to satisfy engineering requirements under complex geological conditions due to limited accuracy and low resolution. This study aims to address the bottleneck of insufficient detection accuracy in single geophysical methods. To achieve this goal, a high-precision three-dimensional (3D) geological advance prediction technical framework is constructed and validated. The method systematically integrates advanced wavelet-based signal filtering and denoising techniques, efficient two-dimensional inversion algorithms and 3D visualization imaging. Of particular importance is that this study utilizes magnetic prospecting data as a multi-physical field joint constraint. It performs collaborative inversion with Transient Electromagnetic Method (TEM) data to overcome the multi-solution nature of single physical field interpretation. Validation in an actual tunnel engineering project demonstrates outstanding predictive performance: the root mean square error (RMSE) is reduced to 8%, the correlation coefficient (R) reaches 0.92, the signal-to-noise ratio (SNR) achieves 30 dB and spatial resolution is improved by approximately 30% compared with conventional two-dimensional inversion methods. The research results indicate that the proposed method significantly enhances the accuracy, reliability and detailed resolution capability of water-rich structure detection. It successfully overcomes the precision limitations of traditional geological prediction techniques. The novelty of this study lies in establishing a complete high-precision technical system from signal processing to multi-physical field comprehensive inversion. This provides a more reliable scientific basis for the safe construction of tunnels under complex geological conditions and surpasses the traditional prediction model that relies on a single physical field.
A simple method is outlined for the assessment of overbreak in raise bores and other circular excavations that specifically includes consideration of the spacing of discontinuities. The two-dimensional method is based on considerations of stress anisotropy implemented with a transversely isotropic continuum rather than by strength anisotropy; in fact, the rock strength is modelled as isotropic. The shear stiffness of discontinuities is stress dependent and this means that the method is statically indeterminate. A work-around is presented based on defining design sectors based on the orientation of the excavation faces relative to the orientation of the discontinuity set. When faces are parallel to the discontinuity set the rock mass is modelled as being transversely isotropic and for faces normal to the discontinuity the rock mass is modelled as isotropic. A brittle failure criterion incorporating damage initiation and a spalling limit allows the production of design charts that can be used as a complement to existing methods to identify potential hazards before excavation.
The effects of silicon (Si) doping on the microstructure, mechanical properties, and electrochemical corrosion behavior of dual-phase eutectic high-entropy alloys (AlCrFeNi)<sub>100-x</sub>Si<sub>x</sub> (x = 2, 4, 6 at.%) were systematically investigated. The results reveal that with increasing Si content, all three alloys maintain a sunflower-like eutectic microstructure composed of A2 and B2 phases, characterized by an expanding central region and a densification and refinement of the lamellar two-phase structure in the petal regions; the volume of phase B2 gradually increases, accompanied by the precipitation of nanoscale B2 particles. The test results of mechanical properties show that Si doping enhances the compressive strength and Vickers hardness but significantly reduces ductility, exhibiting a typical inverse strength–ductility relationship. Electrochemical corrosion tests demonstrate that higher Si content deteriorates corrosion resistance, with corrosion predominantly occurring in the B2 phase. Among the studied alloys, the Si<sub>2</sub> variant exhibits the most balanced overall performance. This work provides valuable insights into the role of Si in tuning the microstructure and properties of eutectic high-entropy alloys and methodology for their compositional design and engineering applications.
In this study, the effect of solution and aging heat treatments on the microstructure and mechanical properties of GH4099 superalloy fabricated lase powder bed fusion (L-PBF) was investigated.The microstructure of as-manufactured L-PBF-built GH4099 alloy has elongation grain with an average grain size of 33.8 μm and fine cellular dendrites with an average spacing of 400 nm along the building direction. The grain structure of L-PBF-built GH4099 alloy is relatively complex and the grain boundary morphology is irregular. The morphology factor of the grains in the as-built GH4099 alloy primarily ranges between 0.3 and 0.4, with a notable inverse relationship between grain size and morphology factor. The L-PBF-built GH4099 alloy underwent complete recrystallization recrystallization when the solution temperature is 1120 °C and the holding time is extended to 4 h. With an increase in the solution temperature, the time required for complete recrystallization decreases progressively. As epitaxial columnar crystals are progressively replaced by recrystallized grains, the correlation between grain size and morphology factor gradually diminishes. The selection of solution and aging treatment parameters is based on the established relationships between the minimum solution time, temperature, and recrystallization behavior as well as the aging time, temperature, and hardness. The hardness of the fully recrystallized sample reached 460HV after solution at 1140 °C for 2 h and aging at 800 °C for 6 h, while the non-fully recrystallized sample reached 470HV after solution at 1120 °C for 1 h and aging at 750 °C for 8 h.The optimal heat treatment system of L-PBF-built GH4099 alloy is solid solution at 1140 °C for 2h, then aging at 800 for 6h. Under the optimal heat treatment regime, the longitudinal and transverse specimens have yield strengths of 714 and 753 MPa, tensile strengths of 1197 and 1230 MPa, and elongations of 45.3% and 38.9%, respectively.
The layout of coal mining in our country is gradually being optimized towards the western regions, the mining depth is constantly extending deeper, and the hazards of rock burst are becoming increasingly severe. To achieve effective prevention and control of rock bursts in Ordos Mining Area, the primary task is to clarify the main controlling factors causing these rock bursts. Based on the investigation of 28 coal mines in Ordos Mining Area with burial depth of over 400 meters, four geological controlling factors causing rock bursts have been summarized: mining depth, coal seam thickness, coal-rock burst tendency, and roof rock structure, along with two engineering controlling factors: coal pillar design and extraction speed. The mechanical mechanisms of each main controlling factor influencing the occurrence of rock burst were analyzed. In terms of the main geological control factors, the increase in mining depth leads to an increase in coal and rock stress, and the thickness of coal seams shows a positive correlation with the possibility of rock burst. The burst tendency of coal and rock is an inherent factor measuring the ability of coal and rock to accumulate and release energy during burst events. A hard and thick suspended roof structure can lead to increased pre-stress concentration, and when the roof breaks, it releases a large amount of kinetic energy, forming dynamic loads that act on the surrounding rock of tunnels, inducing rock bursts. In terms of the main control factors of the project, unreasonable width of coal pillars leads to stress concentration and energy accumulation which are significant factors causing rock bursts in retreating tunnels. Excessive extraction speed increases the length of suspended roofs and the degree of stress concentration, resulting in more kinetic energy being released when the roof breaks. Based on these findings, a comparative analysis of dynamic events during the mining operations of Hongqinghe Coal Mine, Menkeqing Coal Mine, Nalinhe Coal Mine, and Shilawusu Coal Mine was conducted. Recommendations for coordinated regional and local prevention and control strategies for rock bursts are proposed.
Owing to high thermal stability and large reaction enthalpy, MgH2 has high reaction temperatures and sluggish reaction kinetics in the dehydrogenation process, which consumes lots of energy. To achieve hydrogen release with low energy consumption, accelerated reaction rate, and high heating uniformity, this paper proposes a novel method of graphite responsive microwave-assisted thermal management with NaTiOxH catalyst. A multi-physics model of the 5 wt% NaTiOxH catalyzed MgH2 reactor integrated with a microwave generator is developed to investigate the reaction, heat and mass transfer process of hydrogen release. It is found that the graphite responsive microwave heating method could improve the temperature uniformity of reaction bed, reduce the energy consumption by at least 10.71% and save the hydrogen release time by 53.49% compared with the traditional electric heating method. Moreover, the hydrogen desorption thermodynamics could be improved with the increase of microwave power. The hydrogen release time is shortened by 19.55% with the increase of 20 W microwave power. Meanwhile, it is also concluded that the microwave excitation frequency of 2.1 GHz and the graphite content of 2 wt% have better heating performance. Therefore, it can be verified that the graphite responsive microwave heating helps to low-energy and accelerated hydrogen release from MgH2 hydrogen storage reactor.
ABSTRACT: Petroleum contamination in soils, often resulting from mining operations and oil and gas drilling activities, poses a serious threat to environmental and human health. These industrial activities can lead to the release of petroleum hydrocarbons into terrestrial ecosystems, where they persist for extended periods, disrupt ecological functions, and degrade soil and water quality. Therefore, the development of sustainable and effective remediation strategies is essential for mitigating the environmental footprint of resource extraction processes. However, the efficiency of conventional soil remediation methods varies widely – from 20% to over 90% – depending on hydrocarbon type, soil properties, and environmental conditions. These traditional approaches suffer from several critical limitations, including prolonged treatment durations, non-selectivity, high operational and logistical costs, and large land requirements. Moreover, they can disrupt the native soil microbiome, reduce soil fertility, and degrade organic matter, potentially leading to the accumulation of toxic or persistent intermediate compounds. To address these challenges, this paper aims to develop an eco-sustainable and cost-effective alternative remediation method. The study investigates the application of magnetic nanoparticles – specifically magnetite (Fe₃O₄) – as a promising remediation approach for oil-contaminated soils. Due to their high surface area, hydrophobicity, strong adsorption capacity, and ease of recovery via magnetic separation, Fe₃O₄ nanoparticles offer distinct advantages over conventional remediation techniques. The proposed mechanism involves the desorption of hydrocarbons from soil organic matter, subsequent adsorption onto nanoparticle surfaces, and recovery through magnetic separation. Batch experiments were conducted to evaluate the effects of nanoparticle concentration, contact time, and soil pH on the removal efficiency of petroleum hydrocarbons. Results demonstrate that nanoparticles sized between 15–20 nm can achieve up to 47–48% removal of nonpolar petroleum constituents—particularly heavy aliphatic and aromatic hydrocarbons – under optimised conditions. Furthermore, a synergistic approach combining Fe₃O₄ with oxidising agents such as persulphate was explored to enhance the degradation of recalcitrant hydrocarbons and improve nanoparticle recyclability. This research supports the integration of nanotechnology into sustainable remediation practices, particularly within the broader context of environmentally responsible raw material extraction, and contributes to the advancement of clean-up technologies for contamination originating from extractive industries.
Key words: Petroleum spills, contaminated soils, iron oxide nanoparticles, remediation
Dislocations are line defects in crystalline solids and often exert a significant influence on the mechanical properties of metals. Recently, there has been a growing interest in using dislocations in ceramics to enhance materials performance. However, dislocation engineering has frequently been deemed uncommon in ceramics owing to the brittle nature of ceramics. Contradicting this conventional view, various approaches have been used to introduce dislocations into ceramic materials without crack formation, thereby paving the way for controlled ceramics performance. However, the influence of dislocations on functional properties is equally complicated owing to the intricate structure of ceramic materials. Furthermore, despite numerous experiments and simulations investigating dislocation-controlled properties in ceramics, comprehensive reviews summarizing the effects of dislocations on ceramics are still lacking. This review focuses on some representative dislocation-controlled properties of ceramic materials, including mechanical and some key functional properties, such as transport, ferroelectricity, thermal conductivity, and superconducting properties. A brief integration of dislocations in ceramic is anticipated to offer new insights for the advancement of dislocation engineering across various disciplines.
Mikio Nakano, Hironori Takeuchi, Sadahiro Yoshikawa
et al.
This paper proposes to refer to the field of software engineering related to the life cycle of dialogue systems as Dialogue Systems Engineering, and surveys this field while also discussing its future directions. With the advancement of large language models, the core technologies underlying dialogue systems have significantly progressed. As a result, dialogue system technology is now expected to be applied to solving various societal issues and in business contexts. To achieve this, it is important to build, operate, and continuously improve dialogue systems correctly and efficiently. Accordingly, in addition to applying existing software engineering knowledge, it is becoming increasingly important to evolve software engineering tailored specifically to dialogue systems. In this paper, we enumerate the knowledge areas of dialogue systems engineering based on those of software engineering, as defined in the Software Engineering Body of Knowledge (SWEBOK) Version 4.0, and survey each area. Based on this survey, we identify unexplored topics in each area and discuss the future direction of dialogue systems engineering.
Hendrik Reiter, Janick Edinger, Martin Kabierski
et al.
Process mining traditionally assumes centralized event data collection and analysis. However, modern Industrial Internet of Things systems increasingly operate over distributed, resource-constrained edge-cloud infrastructures. This paper proposes a structured approach for decentralizing process mining by enabling event data to be mined directly within the IoT systems edge-cloud continuum. We introduce ContinuumConductor a layered decision framework that guides when to perform process mining tasks such as preprocessing, correlation, and discovery centrally or decentrally. Thus, enabling privacy, responsive and resource-efficient process mining. For each step in the process mining pipeline, we analyze the trade-offs of decentralization versus centralization across these layers and propose decision criteria. We demonstrate ContinuumConductor at a real-world use-case of process optimazition in inland ports. Our contributions lay the foundation for computing-aware process mining in cyber-physical and IIoT systems.
Autonomous and smart mines are predicted to become more prevalent. Automation has undeniable benefits in the mining industry, especially in terms of safety. However, automation has also led to unforeseen implications for individuals, organisations and communities. This study undertakes a systematic review of research on the impacts of automation in the mining context. A total of 94 documents that dealt with issues related to humans, safety and communities were found. Documents were analysed using both manual and natural language processing techniques. The review revealed the main concerns the industry must face for the successful implementation of automation, with interoperability and inadequate wireless networks identified as the most significant challenges. Key themes for individuals were workload, cognitive load, communication, acceptance of automation and trust. Task changes and culture were the most predominant issues at the organisational level. Impacts on employment and indigenous communities were highlighted at the community level. The emergence of advanced technologies and interoperability issues have implications for implementing of smart or intelligent mining. Human factors, precisely situation awareness and workload, have far-reaching consequences for safety and productivity because automation is becoming more complex. Moreover, not quantifying community impacts affects how companies can meet their corporate social responsibility commitments.
Joel de Jesus, José A. M. Ferreira, Carlos Capela
et al.
In the present work, a study of the fatigue strength of two materials widely used in the production of molds, namely, the AISI P20 and AISI H13 steels, is presented. The tests were performed at a constant amplitude with a stress ratio of R = 0 using samples where U-shaped notches were filled with laser beam fusion deposition. Three different sets of deposition parameters for each material were analyzed. Fatigue strength results are presented as S-N curves obtained for filled and non-filled materials. In addition to the assessment of the fatigue strength, metallography, hardness, and the fracture surface of the specimens tested were also evaluated. In general, a high number of metallurgic defects was detected, and consequently, a decrease in the mechanical properties of the materials was observed, especially the fatigue strength. However, the parameter optimization of the repairing laser process produced repaired zones with good metallurgical quality, leading to higher fatigue strength in both of the high-strength steels analyzed.
In the construction process of tunnel (roadway) drilling and blasting, intelligent charging can replace manual operation and reduce the occurrence of dangerous accidents in charging operation. However, some factors such as poor light conditions in the tunnel, small blasthole targets, and cracks in the tunnel face will cause the misdetection and missed detection of blastholes during intelligent charging. At the same time, the limited computing power of the vehicle-mounted computer is also a difficulty that restricts the use of large models for blasthole identification. The MCIW-2 deep learning model can solve the problem of high-precision blasthole detection and real-time deployment in the tunnel excavation environment. According to the size characteristics of the collected blasthole images, the model adopts the adaptive anchor frame clustering algorithm module to optimize the aspect ratio size parameters of the detection frame. The loss function WIoU (Wise Intersection over Union) with a dynamic non-monotonic focusing mechanism is used to deal with the challenge of low-quality blasthole images for achieving a high-precision detection. The MobileNetv3-Small network and CBAM (Convolutional Block Attention Module) are used to build a backbone network structure, reducing model parameters to ensure detection accuracy and meet the lightweight deployment requirements of vehicle equipment. Experiments have proved that the MCIW-2 model has reached 96.18% accuracy in blasthole recognition, and the detection speed has reached 59 fps. Compared with the benchmark YOLO (You Only Look Once) series target detection model with the smallest file, the lightweight blasthole intelligent detection model constructed is reduced by 75.86%, and the model file is only 2.80 Mb, which is better than the benchmark target detection model of the YOLO series. The MCIW-2 deep learning model is used to test the live video of the working face, and the rapid and accurate detection of blasthole is realized. The test results show that the model is suitable for the lightweight deployment requirements of intelligent charge engineering, has a good adaptability, and some significant advantages in comprehensive performance.
In this manuscript we will present a brief overview of the comorbidity concept. We will start by laying its foundations and its definitions and then describing the role that machine learning can hold in mining and defining it. The purpose of this short survey is to present a brief overview of the definition of comorbidity as a concept, and showing some of the latest applications and potentialities for the application of natural language processing and text mining techniques.