Hasil untuk "Engineering geology. Rock mechanics. Soil mechanics. Underground construction"

Liyuan Liu, Shengwen Luo, Derek Elsworth et al.

The study of the effects of thermal damage on the mineral components, microstructure, and macroscopic physico-mechanical properties of rocks can provide valuable references for rock engineering design and long-term safety evaluations. In this work, we systematically study the evolution of microstructure and variations in the mechanical properties of granite under high-temperature conditions. The microstructural changes and macro-mechanical properties of rocks are investigated across a temperature range of 25 °C–1000 °C through the application of characterization techniques, macro-mechanical experiments, and numerical simulations. High temperatures induce the gradual evolution of micropores and mesopores into macropores, culminating in a significant increase in porosity, with the most rapid rate of increase occurring at 400 °C. The X-ray diffraction (XRD) results indicate that the high-temperature environment (below 1000 °C) specifically affects the intensity of the maximum diffraction peaks and the half-height width (FWHM) of each mineral component in the granite. The scanning electron microscope (SEM) observation confirms the development of fracture and the reduction in cementation between mineral particles under different temperatures. Additionally, uniaxial and triaxial compression tests were conducted using the GCTS mechanical loading system. Experimental results reveal that the threshold temperature for granite damage is 400 °C, and the temperature range for the brittle-ductile transition of granite lies roughly between 600 °C and 800 °C. Numerical simulations were performed by employing non-homogeneous rock damage theory and a thermal-mechanical-damage coupling model. Simulated results align well with experimental data. Specifically, the simulations demonstrate that high-temperature treatment causes the redistribution of microstructure in granite, resulting in increased heterogeneity and a change in the failure morphology.

Mayuranath SureshKumar, Hanumanthrao Kannan

Classic problem-space theory models problem solving as a navigation through a structured space of states, operators, goals, and constraints. Systems Engineering (SE) employs analogous constructs (functional analysis, operational analysis, scenarios, trade studies), yet still lacks a rigorous systems-theoretic representation of the problem space itself. In current practice, reasoning often proceeds directly from stakeholder goals to prescriptive artifacts. This makes foundational assumptions about the operational environment, admissible interactions, and contextual conditions implicit or prematurely embedded in architectures or requirements. This paper addresses that gap by formalizing the problem space as an explicit semantic world model containing theoretical constructs that are defined prior to requirements and solution commitments. These constructs along with the developed axioms, theorems and corollary establish a rigorous criterion for unambiguous boundary semantics, context-dependent interaction traceability to successful stakeholder goal satisfaction, and sufficiency of problem-space specification over which disciplined reasoning can occur independent of solution design. It offers a clear distinction between what is true of the problem domain and what is chosen as a solution. The paper concludes by discussing the significance of the theory on practitioners and provides a dialogue-based hypothetical case study between a stakeholder and an engineer, demonstrating how the theory guides problem framing before designing any prescriptive artifacts.

Volodymyr Tytarenko

The object of research is a section of a soil slope, prone mainly to landslides and partially to landslides, on the edge and at the foot of which administrative buildings are located. The landslide limited the full operation of the buildings, and further deformations of the landslide massif could threaten their destruction. The research aim is the applied methods development for surveying the technical state of the site and landslide prevention structures that underwent a soil accident, accident causes identification and options development for the site further stabilization and reconstruction depending on its intended use in the future. The landslide phenomena that led to a soil accident in the dense development conditions of the city of Kyiv were visually inspected. The physical and mechanical characteristics of soils composing the landslide slope, groundwater regime and other features of the site with landslide soils, as well as the landslide protection structures and drainage system state in the landslide lower part were determined. The scenario analysis based on the mathematical numerical modeling and PLAXIS software package was used to build the digital model of the landslide disaster site and identify the causes that led to the disaster. The scenario analysis combined with the mathematical numerical modeling was used to assess the possible options for stabilizing and reconstructing the soil accident site depending on its further intended use. The stabilization of the landslide slope was achieved through the use of engineering measures: removal of landslide masses, arrangement of additional retaining walls using soil anchors, terraced slope and installation of gabions, arrangement of drainage systems. The qualitative effectiveness of the results obtained by the author is based on a scenario analysis to consider from the unified positions of modern experimental and computational capabilities of soil mechanics the elimination of a soil accident, which can lead to many human losses and economic losses in further development. The main options and sequence of actions for its elimination are analyzed. The quantitative effectiveness of the proposed measures is proven by the reduction of landslide hazard, which is contained in the generally accepted in the world indicator of the coefficient of stability of a landslide-prone soil area. And, as a result, this indicator meets the regulatory requirements of Ukrainian construction standards, which confirms the effectiveness of approaches and methods for eliminating a complex soil accident. The slope stability coefficient kst was increased from 0.989…0.991 to 1.474…1.88, which exceeds the regulatory value of 1.2. Based on the research, project documentation for stabilizing the landslide slope was developed, engineering preparation of the territory was carried out, which included the installation of retaining walls with soil anchors and terracing of the slope. Construction work on the arrangement of the underground part of the building has begun

Raafat Aborafia, Amir Hossein Saeedi Dehaghani

This study investigates the phase behavior of methane, ethane, and their binary mixture in both bulk and 5 nm slit-like pores with silica, anhydrite, calcite, dolomite, and montmorillonite walls using grand canonical Monte Carlo simulation (GCMC). The results show that vapor densities increase and, liquid densities decrease with the reduction of the pore width for both pure components and binary mixtures. The critical pressure and temperature decrease significantly in confined systems compared to bulk systems, with the rate of decrease varying depending on the type of surface. The response of critical density to surface type is distinct, and the critical density can be higher or lower than that in bulk systems. Furthermore, the dew point pressure of the confined binary mixture between two surfaces of silica, anhydrite, calcite, dolomite, and montmorillonite is higher than its value in bulk systems, while the bubble point pressure in confined systems can be lower, equal, or more than its value in bulk systems, depending on the pore surface and temperature.

Joyce Nakayenga, Toshiro Hata, Alexandra Clarà Saracho et al.

Sporosarcina newyorkensis is an indigenous microbe found in sedimentary layers bearing methane hydrates in the oceans around Japan’s main islands. It can survive extremely cold temperatures and precipitate calcium carbonate (CaCO3). This has led to interest in applying the microbe in microbiologically induced calcium carbonate precipitation (MICP) to improve the properties of the surrounding sand and to facilitate the exploration of methane hydrates. Using the injection method, a large-scale laboratory experiment was conducted in this study on sand columns with a diameter of 60 cm and a height of 70 cm to evaluate the MICP performance of S. newyorkensis under high overburden pressures of 3.5 and 20 MPa. The results indicated that S. newyorkensis can precipitate CaCO3 at high overburden pressures and reduce the permeability of sand. The unconfined compressive strength and amount of precipitated CaCO3 were seen to decrease with the distance from the injection well, but they remained sufficient to distances of up to 20 cm. S. newyorkensis was also found to increase the pH level, which would further promote CaCO3 precipitation and, in turn, lower hydraulic conductivity and stabilize hydrate-bearing sand formations.

Chukwuemeka Daniel, Shouye Cheng, Xin Yin et al.

Rockbursts pose severe risks to underground engineering projects, including mining and tunnelling, where sudden rock failures can lead to substantial infrastructure damage and loss of human lives. An accurate assessment of rockburst damage is essential for safety and effective risk mitigation. This study investigates the effectiveness of ensemble machine learning models optimized through Bayesian optimization (BO) in predicting rockburst damage scales. Nine classifier algorithms, including random forest (RF), were evaluated using a dataset of 254 samples. The research considered factors such as stress conditions, support system capacity, excavation span, geological characteristics, seismic magnitude, peak particle velocity, and rock density as input variables. The rockburst damage scale, categorized into four severity levels based on displaced rock mass, served as the target variable. Among the models evaluated, BO-RF model demonstrated the highest predictive accuracy and generalization capability, achieving 92% testing accuracy. BO-RF model also ranked top in a multi-criteria evaluation framework. This devised ranking system underscores the importance of evaluating model performance on both training and unseen testing data to ensure robust generalization. The findings underscore the effectiveness of BO-RF in enhancing rockburst risk assessment and providing reliable predictive insights for underground engineering applications.

Feifei Zhao, Manchao He, Qiru Sui et al.

The impacts of natural boulders carried by debris flows pose serious risks to the safety and reliability of structures and buildings. Natural boulders can be highly random and unpredictable. Consequently, boulder control during debris flows is crucial but difficult. Herein, an eco-friendly control system featuring anchoring natural boulders (NBs) with (negative Poisson's ratio) NPR anchor cables is proposed to form an NB-NPR baffle. A series of flume experiments are conducted to verify the effect of NB-NPR baffles on controlling debris flow impact. The deployment of NB-NPR baffles substantially influences the kinematic behavior of a debris flow, primarily in the form of changes in the depositional properties and impact intensities. The results show that the NB-NPR baffle matrix successfully controls boulder mobility and exhibits positive feedback on solid particle deposition. The NB-NPR baffle group exhibits a reduction in peak impact force ranging from 29% to 79% compared to that of the control group in the basic experiment. The NPR anchor cables play a significant role in the NB-NPR baffle by demonstrating particular characteristics, including consistent resistance, large deformation, and substantial energy absorption. The NB-NPR baffle innovatively utilizes the natural boulders in a debris flow gully by converting destructive boulders into constructive boulders. Overall, this research serves as a basis for future field experiments and applications.

Shi Liu, Yu Jia, Yue Zhai et al.

Understanding the mesoscopic tensile fracture damage of rock is the basis of evaluating the deterioration process of mechanical properties of heat-damaged rock. For this, tensile tests of rocks under high-temperature treatment were conducted with a ϕ75 mm split Hopkinson tension bar (SHTB) to investigate the mesoscopic fracture and damage properties of rock. An improved scanning electron microscopy (SEM) experimental method was used to analyze the tensile fracture surfaces of rock samples. Qualitative and quantitative analyses were performed to assess evolution of mesoscopic damage of heat-damaged rock under tensile loading. A constitutive model describing the mesoscopic fractal damage under thermo-mechanical coupling was established. The results showed that the high temperatures significantly reduced the tensile strength and fracture surface roughness of the red sandstone. The three-dimensional (3D) reconstruction of the fracture surface of the samples that experienced tensile failure at 900 °C showed a flat surface. The standard deviation of elevation and slope angle of specimen fracture surface first increased and then decreased with increasing temperature. The threshold for brittle fracture of the heat-damaged red sandstone specimens was 600 °C. Beyond this threshold temperature, local ductile fracture occurred, resulting in plastic deformation of the fracture surface during tensile fracturing. With increase of temperature, the internal meso-structure of samples was strengthened slightly at first and then deteriorated gradually, which was consistent with the change of macroscopic mechanical properties of red sandstone. The mesoscopic characteristics, such as the number, mean side length, maximum area, porosity, and fractal dimension of crack, exhibited an initial decline, followed by a gradual increase. The development of microcracks in samples had significant influence on mesoscopic fractal dimension. The mesoscopic fractal characteristics were used to establish a mesoscopic fractal damage constitutive model for red sandstone, and the agreement between the theoretical and experimental results validated the proposed model.

Derick E. Ober, Sesha Sai Behara, Anton Van der Ven

First-principles statistical mechanics enables the prediction of thermodynamic and kinetic properties of materials, but is computationally expensive. Many approaches require surrogate models to calculate energies within Monte Carlo or molecular dynamics simulations. Inexpensive surrogates such as cluster expansions enable otherwise intractable calculations by interpolating data from higher accuracy methods, such as Density Functional Theory (DFT). Surrogate models introduce uncertainty into downstream calculations, in addition to any uncertainty inherent to DFT calculations. Bayesian frameworks address this by quantifying uncertainty and incorporating expert knowledge through priors. However, constructing effective priors remains challenging. This work introduces and describes practical strategies for building Bayesian cluster expansions, focusing on basis truncation, hyperparameter selection, and ground state replication. We analyze multiple basis truncation schemes, compare cross-validation to the evidence-approximation for hyperparameter optimization, and provide methods to find and enforce ground-state-preserving models through priors. Additionally, we compare the uncertainties between different approximations to DFT (LDA, PBE, SCAN) against the uncertainty introduced with the use of cluster expansion surrogate models. These approaches are demonstrated on the BCC Li$_x$Mg$_{1-x}$ and Li$_x$Al$_{1-x}$ alloys, which are both of interest for solid-state Li batteries. Our results provide guidelines for constructing and utilizing Bayesian cluster expansions, thereby improving the transparency of materials modeling. The approaches and insights developed in this work can be transferred to a wide range of cluster expansion surrogate models, including the atomic cluster expansion and related machine-learned interatomic potential architectures.

Max Neuwinger, Dirk Riehle

The design of effective programming languages, libraries, frameworks, tools, and platforms for data engineering strongly depends on their ease and correctness of use. Anyone who ignores that it is humans who use these tools risks building tools that are useless, or worse, harmful. To ensure our data engineering tools are based on solid foundations, we performed a systematic review of common programming mistakes in data engineering. We focus on programming beginners (students) by analyzing both the limited literature specific to data engineering mistakes and general programming mistakes in languages commonly used in data engineering (Python, SQL, Java). Through analysis of 21 publications spanning from 2003 to 2024, we synthesized these complementary sources into a comprehensive classification that captures both general programming challenges and domain-specific data engineering mistakes. This classification provides an empirical foundation for future tool development and educational strategies. We believe our systematic categorization will help researchers, practitioners, and educators better understand and address the challenges faced by novice data engineers.

Amirmasoud Molaei, Mohammad Heravi, Reza Ghabcheloo

Rock capturing with standard excavator buckets is a challenging task typically requiring the expertise of skilled operators. Unlike soil digging, it involves manipulating large, irregular rocks in unstructured environments where complex contact interactions with granular material make model-based control impractical. Existing autonomous excavation methods focus mainly on continuous media or rely on specialized grippers, limiting their applicability to real-world construction sites. This paper introduces a fully data-driven control framework for rock capturing that eliminates the need for explicit modeling of rock or soil properties. A model-free reinforcement learning agent is trained in the AGX Dynamics simulator using the Proximal Policy Optimization (PPO) algorithm and a guiding reward formulation. The learned policy outputs joint velocity commands directly to the boom, arm, and bucket of a CAT365 excavator model. Robustness is enhanced through extensive domain randomization of rock geometry, density, and mass, as well as the initial configurations of the bucket, rock, and goal position. To the best of our knowledge, this is the first study to develop and evaluate an RL-based controller for the rock capturing task. Experimental results show that the policy generalizes well to unseen rocks and varying soil conditions, achieving high success rates comparable to those of human participants while maintaining machine stability. These findings demonstrate the feasibility of learning-based excavation strategies for discrete object manipulation without requiring specialized hardware or detailed material models.

Shiwei Liu, Jiaxin Zhao, Fang Cheng et al.

Surrounding rock and lining are composite structures with asymmetric mechanical properties. Understanding the mechanical properties and failure characteristics of rock–concrete composites is crucial for gaining insights into the mechanisms that induce disasters in deep-underground environments. Uniaxial compression and acoustic emission tests were conducted on rock–concrete composite specimens cured at temperatures of 20 °C, 40 °C, 60 °C, and 80 °C, with interface angles of 15°, 30°, 45°, 60°, 75°, and 90° respectively. The results indicated that the specimens’ strength decreased at increasing geothermal temperatures. The composites with an 80 °C curing temperature and a 60° interface angle exhibited the lowest strength. A higher geothermal temperature significantly reduced the number of cracks in the concrete component during composite failure and mitigated the influence of the inclined interface angle. The failure modes of the specimens include axial penetration splitting, interface shear, Y-shaped fracture, and interface splitting–concrete shear failure. Finally, a model relating the strength of the rock–concrete composite to the inclined interface angle and the geothermal temperature was derived and verified against the experimental results with a relative error of 9.8%. The findings have significant implications for the safety and stability of tunnels in high-temperature conditions.

J. Li, Shuai Chen, Sijin Qin et al.

This paper presents a comprehensive analysis of the correlation between static and dynamic mechanical properties of sedimentary rocks from Australian basins. By leveraging both empirical and machine learning techniques, we provide valuable insights into the predictive capabilities of different methodologies and identify the most effective approaches for capturing the intricate relationships within the dataset. The results of our study reveal that several machine learning methods consistently outperform empirical approaches, yielding lower error values and providing more accurate predictions of the correlation between static and dynamic properties. Furthermore, we rank these machine learning methods based on their respective error values, offering insights into the relative performance of each algorithm. Our findings not only advance our understanding of sedimentary rock mechanics but also offer practical recommendations for improving reservoir characterization, enhancing geotechnical assessments, and optimizing engineering design. Sedimentary rocks are integral components of the Earth's crust, encompassing a vast array of lithological compositions, depositional environments, and geologic histories (Miall, 2013; Milliken, 2014). Their mechanical properties play a critical role in various engineering and geotechnical applications, including reservoir characterization, wellbore stability assessment, and underground mining design (Abdollahipour et al., 2019; Ghassemi, 2012; Li et al., 2022; Li et al., 2023; Zhong et al., 2021). Understanding the relationship between the static and dynamic mechanical properties of sedimentary rocks is fundamental for accurately predicting their behavior under different loading conditions and is also crucial for interpreting subsurface geology, predicting reservoir behavior, and designing resilient infrastructure (Chang et al., 2006; Sone & Zoback, 2013; Wang et al., 2016). The Australian continent hosts a diverse range of sedimentary basins, each with unique depositional settings and rock types (Huston et al., 2016; Jaireth et al., 2014; Jaques et al., 2002). These basins offer an excellent opportunity to investigate the correlation between static and dynamic mechanical properties across different geological contexts. Despite the significance of such correlations, comprehensive studies addressing this relationship in the Australian sedimentary basins are limited.

Kannan Pandian, Shanmugam Vijayakumar, Mohamed Roshan Abu Firnass Mustaffa et al.

Land degradation and climate change, two intricately intertwined phenomena, demand appropriate management solutions to effectively tackle the escalating issues of food and nutritional security. In this context, the realm of agriculture confronts formidable challenges in its pursuit of soil resource reclamation, improving water quality, mitigating climate change, and maintaining soil and natural resources for posterity. Central to these aspirations is the preservation of an optimum organic matter, serving as a linchpin threshold is crucial for protecting the physical, chemical, and biological integrity of the soil, while simultaneously sustaining agricultural productivity. To address these multifaceted challenges, the introduction of diverse organic amendments has emerged as a crucial strategy. Noteworthy among these is the application of biochar, which functions as a soil conditioner capable of bolstering soil health, mitigating the impact of climate change, and securing global food security. Biochar is a carbon-enriched substance produced through pyrolysis of assorted biomass waste. It has a larger surface area, higher cation exchange capacity, and an extended carbon storage capability. The strategic integration of biochar production and subsequent soil application engenders an array of benefits, encompassing the amelioration of soil physical properties, augmented retention and the availability of nutrients, and the enhancement of biological activity, resulting in higher agricultural yields and societal benefits through the curtailment of soil to atmosphere greenhouse gas emissions. Additionally, biochar demonstrates its efficacy in the realm of environmental restoration by serving as a medium for extraction and elimination of heavy metals, which often pervade aquatic ecosystems and soil matrices. This review addressed the need for biochar production, characterization, soil health, the possibility for environmental restoration, and crop yield fluctuations owing to climate change.

Said H. Marzouk, Hamis J. Tindwa, Boniface H. J. Massawe et al.

Rice (Oryza sativa L.) is the second cereal food crop grown in Tanzania after maize (Zea mays L.) and covers approximately 18% of the agricultural land. Soil degradation due to intensive cultivation along with low organic matter input and nutrient imbalance has led to a decline in rice crop yields. This study was conducted to characterize, classify, and assess the fertility status of soils in two rice irrigation schemes of Morogoro region in Tanzania. The data obtained through this study will contribute significantly to land use planning and will facilitate the transfer of agro-technology and other development of the regions with similar ecological conditions. The studied pedons were named MKU-P1 and MKD-P1 for Mkula and Mkindo irrigation schemes, respectively. A total of seven composite soil samples (0–20 cm) were collected for soil fertility assessments. Landform, soil morphological features, parent material, natural vegetation, drainage, erosion, and laboratory data were used to classify the soils in their respective order as per the United States Department of Agriculture (USDA) Soil Taxonomy and the World Reference Base (WRB) soil classification systems. Results showed that the pedons were sandy clay loam in the topsoil and sandy clay to clay in the subsoil; soil reaction ranged from medium acid (pH 5.7) to strongly alkaline (pH 8.6). The topsoil and subsoil nutrients of the studied pedons including available K+, total N, soil organic matter, and organic carbon are low. Based on the USDA Soil Taxonomy, MKU-P1 is classified as Inceptisols cumulic humaquepts and MKD-P1 as Vertisols Fluvaquentic endoaquerts corresponding to Subaquatic fluvisols (loamic, oxyaquic) and Irragric vertisols (gleyic) in the WRB, respectively. The pedons were ranked as suitable for rice production. However, the chemical fertility of the soil is ranked as low fertile associated with deficient in total N; available P, K+, and Ca2+ with excessive iron and manganese; and likely to pose toxicity to crops. The application of organic and mineral amendments in recommended rates and timing for N and P is therefore essential to increase the nutrient content of these soils and minimize losses. Salinity in the subsurface pedon MKD-P1 needs to be taken into future consideration.

Haoyan Wei, C. T. Wu, Wei Hu et al.

Short-fiber-reinforced composites (SFRC) are high-performance engineering materials for lightweight structural applications in the automotive and electronics industries. Typically, SFRC structures are manufactured by injection molding, which induces heterogeneous microstructures, and the resulting nonlinear anisotropic behaviors are challenging to predict by conventional micromechanical analyses. In this work, we present a machine learning-based multiscale method by integrating injection molding-induced microstructures, material homogenization, and Deep Material Network (DMN) in the finite element simulation software LS-DYNA for structural analysis of SFRC. DMN is a physics-embedded machine learning model that learns the microscale material morphologies hidden in representative volume elements of composites through offline training. By coupling DMN with finite elements, we have developed a highly accurate and efficient data-driven approach, which predicts nonlinear behaviors of composite materials and structures at a computational speed orders-of-magnitude faster than the high-fidelity direct numerical simulation. To model industrial-scale SFRC products, transfer learning is utilized to generate a unified DMN database, which effectively captures the effects of injection molding-induced fiber orientations and volume fractions on the overall composite properties. Numerical examples are presented to demonstrate the promising performance of this LS-DYNA machine learning-based multiscale method for SFRC modeling.

Muhammad Zubair Khan, Oleg E. Peil, Apoorva Sharma et al.

In the rapidly expanding field of two-dimensional materials, magnetic monolayers show great promise for the future applications in nanoelectronics, data storage, and sensing. The research in intrinsically magnetic two-dimensional materials mainly focuses on synthetic iodide and telluride based compounds, which inherently suffer from the lack of ambient stability. So far, naturally occurring layered magnetic materials have been vastly overlooked. These minerals offer a unique opportunity to explore air-stable complex layered systems with high concentration of local moment bearing ions. We demonstrate magnetic ordering in iron-rich two-dimensional phyllosilicates, focusing on mineral species of minnesotaite, annite, and biotite. These are naturally occurring van der Waals magnetic materials which integrate local moment baring ions of iron via magnesium/aluminium substitution in their octahedral sites. Due to self-inherent capping by silicate/aluminate tetrahedral groups, ultra-thin layers are air-stable. Chemical characterization, quantitative elemental analysis, and iron oxidation states were determined via Raman spectroscopy, wavelength disperse X-ray spectroscopy, X-ray absorption spectroscopy, and X-ray photoelectron spectroscopy. Superconducting quantum interference device magnetometry measurements were performed to examine the magnetic ordering. These layered materials exhibit paramagnetic or superparamagnetic characteristics at room temperature. At low temperature ferrimagnetic or antiferromagnetic ordering occurs, with the critical ordering temperature of 38.7 K for minnesotaite, 36.1 K for annite, and 4.9 K for biotite. In-field magnetic force microscopy on iron bearing phyllosilicates confirmed the paramagnetic response at room temperature, present down to monolayers.

Yaohou Fan, Chetan Arora, Christoph Treude

Stop words, which are considered non-predictive, are often eliminated in natural language processing tasks. However, the definition of uninformative vocabulary is vague, so most algorithms use general knowledge-based stop lists to remove stop words. There is an ongoing debate among academics about the usefulness of stop word elimination, especially in domain-specific settings. In this work, we investigate the usefulness of stop word removal in a software engineering context. To do this, we replicate and experiment with three software engineering research tools from related work. Additionally, we construct a corpus of software engineering domain-related text from 10,000 Stack Overflow questions and identify 200 domain-specific stop words using traditional information-theoretic methods. Our results show that the use of domain-specific stop words significantly improved the performance of research tools compared to the use of a general stop list and that 17 out of 19 evaluation measures showed better performance. Online appendix: https://zenodo.org/record/7865748

Renyou Ruan, Li Gao

Abstract Currently, information processing of tunnel engineering has mainly adopted conventional mathematical statistics-based methods. Moreover, some nonlinear processing methods are implemented to derive more insights, even though the degree of research is not deep enough. In the research, the rock mechanics test is carried out by drilling a method and taking samples in situ according to the construction technology of tunnels in complex geological conditions and implementing computer information-based methods. Also, rock mechanics tests are carried out in the excavation test area of the flat tunnel. Based on the tests using physical properties, such as deformation, tensile, uniaxial compression, triaxial compression, and longitudinal wave velocity, the physical and mechanical characteristics of the surrounding rock in the tunnel area are comprehensively evaluated, and the stability of the tunnel rock mass is assessed to devise convenient conditions for the subsequent research of the complex geological tunnels based on green excavation. The particle density of sandy mudstone, the bulk density, the porosity, and the natural water content are represented by 2.67 ± 0.61 g/cm3, 2.56 ± 1.42 g/cm3, 7.45%, and 2.86%, respectively, in terms of physical characteristics. These indicate that the sandy mudstone structure is relatively loose, with relatively large pores, micro-fractures, and a high degree of natural water content. The representative deformation test curve of the rock block shows that the ratio of deformation modulus to the compressive strength of the rock block is 650 on average, and Poisson’s ratio ranges from 0.21 to 0.38. These show that sandy mudstone has deformation properties after compression. The tensile strength of sandy mudstone, the shear strength, and c are represented by 1.25 ± 0.23 MPa, f = 1.32, and = 2.35 MPa, respectively. The stated test results can provide a scientific basis for selecting engineering design and its construction parameters in similar areas. In addition, the measurement results show that the surrounding rock will gradually increase, and the tunnel space will gradually become shorter with the increase of buried depth when the gravity stress field occurs. The linear elastic displacement of soft rock is smaller than that of elastic–plastic analysis, and the deeper the tunnel is buried, the larger the displacement difference would be. Therefore, establishing a stable and orderly monitoring and detection system could fully understand the intrinsic law between surrounding rock stress release and surrounding rock pressure and obtain accurate monitoring and measured data to evaluate the grading management standard of a tunnel at the ultimate displacement. In a word, this research provides a feasible idea to study the decision process of green excavation and deformation control technology of tunnels in complex strata.

Yifan Chen, Hang Lin, Baohua Liu

Geothermal disaster caused by high geotemperature is a commonly encountered geological problem in tunnel engineering, especially in large-buried tunnels, which is directly related to the safety, technology, and economy of tunnel construction. It seriously affects the personnel security and the performances of construction equipment and building materials, greatly increasing the construction difficulty, and extending the total construction period, which has become a major issue to be urgently solved in the tunnel construction. This paper first briefly introduces the formation mechanism of the high-geotemperature environment of a large-buried tunnel and analyzes the significant influences of high-temperature on personnel, equipment, and materials in the construction process of tunnel engineering. Then, the worldwide research progress of rock mechanics in high-temperature large-buried tunnels is systematically described, including the thermo-mechanical properties of rock mass, the thermo-mechanical properties of shotcrete, and the rheological mechanism and control technology of surrounding rock. Subsequently, the previous geothermal disaster classification of large-buried tunnels is summarized and evaluated. Finally, the research findings of the key technologies of geothermal disaster prevention and control are presented in detail from three aspects of temperature reduction, thermal insulation, and personal protection, which are of great theoretical and practical significance for ensuring the safety design and construction of tunnels in similar geological environment.