Physical Model Research Articles

Analyses of industrial air pollution and long-term health risk using different dispersion models and WRF physics parameters

AbstractThis study consists of three main sections. The first section delves into a performance analysis centered around modeling PM10, NOx, and CO emissions from a cement factory. It examines the effectiveness of various factors, including meteorological data, physics models, and air quality dispersion models, in producing accurate results for atmospheric simulations. The second section covers the dispersion direction and concentrations obtained by visualizing the dispersion maps. The third section covers an analysis of heavy metals emitted from the facility, taking into account potential risks in the region such as cancer, acute and chronic effects, and long-term respiratory risks. This study made use of meteorological models (WRF, AERMET, and CALMET), air quality dispersion models (AERMOD and CALPUFF), a health risk analysis model (HARP), and various sub-models (MMIF and CALWRF). Satellite meteorological data were obtained from NCEP and ERA, with the majority of meteorological data based on the Global Data Assimilation System (GDAS)/Final Operational Global Analysis (FNL) from Global Tropospheric Analyses and Forecast Grids used for the WRF model. In the daily results, AERMOD showed the highest concentration values, but CALPUFF had greater concentrations throughout the annual period. The winter season had the highest concentrations of pollutants. Although there are differences among the physics models used in this research, the conclusions produced are consistent. Analysis of the data from the HARP model suggested that cancer risk levels exceeded the threshold of one person per million. However, the proportion of exceedance instances is rather small in comparison to the receptor points.

Physical frailty identification using machine learning to explore the 5-item FRAIL scale, Cardiovascular Health Study index, and Study of Osteoporotic Fractures index.

Physical frailty is an important issue in aging societies. Three models of physical frailty assessment, the 5-Item fatigue, resistance, ambulation, illness and loss of weight (FRAIL); Cardiovascular Health Study (CHS); and Study of Osteoporotic Fractures (SOF) indices, have been regularly used in clinical and research studies. However, no previous studies have investigated the predictive ability of machine learning (ML) for physical frailty assessment. The aim was to use two ML algorithms, random forest (RF) and extreme gradient boosting (XGBoost), to predict these three physical frailty assessment models. Questionnaires regarding demographic characteristics, lifestyle habits, living environment, and physical frailty assessment were answered by 445 participants aged 60 years and above. The RF and XGBoost algorithms were used to assess their scores for the three physical frailty indices. Furthermore, feature importance and Shapley additive explanations (SHAP) were used to determine the important physical frailty factors. The XGBoost algorithm obtained higher accuracy for predicting the three physical frailty indices; the areas under the curve obtained by the XGBoost algorithm for the 5-Item FRAIL, CHS, and SOF indices were 0.84. 0.79, and 0.69, respectively. The feature importance and SHAP of the XGBoost algorithm revealed that systolic blood pressure, diastolic blood pressure, age, and body mass index play important roles in all three physical frailty models. The XGBoost algorithm has a more accurate predictive rate than RF across all three physical frailty assessments. Thus, ML can be a useful tool for the early detection of physical frailty.

Improving the Performance of Cooling and Entanglement in a Double Cavity Optomechanical System Assisted by the Quantum Coherent Feedback

AbstractA theoretical scheme is proposed for improving the performance of cooling and entanglement, where the physical model is based on a double cavity optomechanical system assisted by the field‐mediated coherent feedback. The cooling performance is evaluated by calculating the final mean phonon number. The steady‐state bipartite entanglement between the optical mode and mechanical mode is measured by the logarithmic negativity. The result manifests that with assistance of the quantum coherent feedback, the mechanical resonator (MR) can be cooled close to its quantum ground state and the steady‐state optomechanical entanglement is simultaneously created, all of which are obtained under the condition beyond the resolved sideband. The presented feedback strategy is measurement‐independent, which can effectively preserve the quantum coherence of system. The scheme is conducive to relaxing the current experimental condition and it may provide a new path for the optomechanical manipulation involving the low‐frequency MR.

A cross layer graphical neural network based convolutional neural network framework for image dehazing

The current version of imaging equipment cannot quickly and effectively make up for the reduction of visibility triggered by bad weather. Traditional strategies minimize hazy impacts by employing an image depth model and a physical model. Following experts, erroneous depth data reduces the efficacy of the dehazing algorithm. Dehazing methods based on CNN are imperfect to handle region which is bright or similar to atmospheric light and thus leads to oversaturation of pixels. These challenges can be addressed by proposing a novel model that incorporates the idea of a Graphical Neural Network. The amount of light coming from the atmosphere is estimated using normalization where the contrast of the image gets adjusted using Bias Contrast stretch Histogram Equalization. An enhanced Transmission map estimator is used to render the hazy scene. Finally, the cross-layer graphical neural network-based CNN model is applied to produce a haze-free image and eliminate the over-saturation of pixels. Extensive evaluation findings show that the proposed approach can significantly recuperate misty imagery, even if the images have a substantial amount of haze.

A Unified Model of Particle Physics and Cosmology: Origin of Inflation, Baryogenesis, Neutrino Mass, CDM and Dark Energy

A Unified Model of Particle Physics and Cosmology: Origin of Inflation, Baryogenesis, Neutrino Mass, CDM and Dark Energy

Planing Hull Hydrodynamic Performance Prediction Using LincoSim Virtual Towing Tank

This work shows the performance of LincoSim, a web-based virtual towing tank enabling automated and standardized calm water computational fluid dynamics (CFD) data sampling, extending previous published applications to the case of a high-speed hull. The calculations are performed for a 1:10 scale model of a 43 ft powerboat hull form in the Froude number range from 0.3 to 2.0. The counterpart physical model is the experimental fluid dynamics (EFD) campaign performed at the University of Naples Federico II, where the resistance, sinkage and trim data have been measured. The EFD/CFD data comparison is performed and shown with a discussion of the spotted differences. The average percentage differences between the EFD and CFD data for the whole speed range are 1.84, 6.87 and 6.94 for the resistance, dynamic trim, and sinkage, respectively. These results confirm the maturity of the standardized and automated CFD modeling for calm water hydrodynamic analysis included in LincoSim, even at very high Froude numbers. The wetted length of the keel and chine and the wetted surface are calculated from numerical data using the advanced post-processing. Finally, as a work in progress, we test a first comparison for the same hull of the EFD and CFD data, considering two seakeeping conditions for head waves at a given wavelength for two velocity conditions. Also, this kind of analysis confirms the tight correlation between the measured and computed outcomes. This synergic interplay of EFD and CFD can link the advantages of both methods to support hull design but also requires experiment planning and final data analysis to obtain physical parameters not easily measurable in laboratory, such as the wetted surface, wetted lengths, proper viscous contribution, and pressure distribution both in calm water and in waves.

Physical Analysis and Mathematical Modeling of the Hydrogen Storage Process in the MmNi4.2Mn0.8 Compound.

The results of an experimental and mathematical study into the MmNi4.2Mn0.8 compound's hydrogen storage properties are presented in the present research. Plotting and discussion of the experimental isotherms (P-C-T) for different starting temperatures (288 K, 298 K, 308 K, and 318 K) were carried out first. Then, the enthalpy and entropy of formation (ΔH0, ΔS0) were deduced from the plot of van't Hoff. Following that, the P-C-T were contrasted with a mathematical model developed via statistical physics modeling. The steric and energetic parameters, such as the number of the receiving sites (n1, n2), their densities (Nm1, Nm2), and the energy parameters (P1, P2) of the system, were calculated thanks to the excellent agreement between the numerical and experimental results. Therefore, plotting and discussing these parameters in relation to temperature preceded their application in determining the amount of hydrogen in each type of site per unit of metal ([H/M]1, [H/M]2) as well as for the entire system [H/M] versus temperature and pressure besides the absorption energies associated with each kind of site (ΔE1, ΔE2) and the thermodynamic functions (free energy, Gibbs energy, and entropy) that control the absorption reaction.

A physical simulation-machine learning model for optimal process schemes in laser-based directed energy deposition process

The major challenge faced is the definition of optimal process variables for rich quality of fabricated parts in laser-based directed energy deposition (DED-LB) processes. However, predicting the optimal process scheme using machine learning models is still challenging owing to the need for a large amount of training experimental data with high costs in DED-LB. In view of this, a physical simulation (PS)-machine learning (ML) model using a relatively small accurate data set of 31 simulation data for optimal process schemes is proposed in DED-LB process in this study. Firstly, a powder-scale high precision phenomenon model incorporating the mass transfer, phase transformations and heat transfer and using Lagrangian particle model to add mass is developed to depict the DED-LB process. Then, a Gaussian process regression (GPR) agent model is established to rapidly and accurately predict the geometry and dilution rate of deposition tracks under different manufacturing parameters based on the high precision simulation results. Finally, a PS-ML model for process parameter optimization is developed using the particle swarm algorithm (PSO), and the optimized parameters are experimentally validated. The results show that the developed powder-scale model and GPR model results are consistent with the Inconel 718 alloy experimental results. The proposed PS-ML model can increase the accuracy of the ML models even with a small simulation data set. None of PS-ML model optimization results has a relative error of more than 3% to the experimental results, and the dilution rate is reduced by up to 61.66% compared to the experimental design parameters without optimization. The proposed physical simulation-machine learning model in this study enables inexpensive and accurate optimization of DED-LB process parameters.

ReconU-Net: a direct PET image reconstruction using U-Net architecture with back projection-induced skip connection

Objective. This study aims to introduce a novel back projection-induced U-Net-shaped architecture, called ReconU-Net, based on the original U-Net architecture for deep learning-based direct positron emission tomography (PET) image reconstruction. Additionally, our objective is to visualize the behavior of direct PET image reconstruction by comparing the proposed ReconU-Net architecture with the original U-Net architecture and existing DeepPET encoder–decoder architecture without skip connections. Approach. The proposed ReconU-Net architecture uniquely integrates the physical model of the back projection operation into the skip connection. This distinctive feature facilitates the effective transfer of intrinsic spatial information from the input sinogram to the reconstructed image via an embedded physical model. The proposed ReconU-Net was trained using Monte Carlo simulation data from the Brainweb phantom and tested on both simulated and real Hoffman brain phantom data. Main results. The proposed ReconU-Net method provided better reconstructed image in terms of the peak signal-to-noise ratio and contrast recovery coefficient than the original U-Net and DeepPET methods. Further analysis shows that the proposed ReconU-Net architecture has the ability to transfer features of multiple resolutions, especially non-abstract high-resolution information, through skip connections. Unlike the U-Net and DeepPET methods, the proposed ReconU-Net successfully reconstructed the real Hoffman brain phantom, despite limited training on simulated data. Significance. The proposed ReconU-Net can improve the fidelity of direct PET image reconstruction, even with small training datasets, by leveraging the synergistic relationship between data-driven modeling and the physics model of the imaging process.

Large-Scale Laboratory Experiments On The Wave Generation Due To The Collapse Of Partially And Fully Submerged Granular Columns

Landslides that occur in coastal environments can drive cascading consequences such as wave forces, flooding, and infrastructure damage to coastal communities. It can be difficult to classify these slides as subaerial or submarine, and the mechanics of wave generation associated with partially submerged failures are not well understood. Limited physical modelling has been conducted that encompasses both the triggering of granular landslides and subsequent waves associated with partially and fully submerged mass movements. To date, laboratory work investigating tsunamis generated by submarine landslides has focused on the wave formed in the direction of the mass movement (seaward direction) for rigid block experiments (eg. Rzadkiewicz, 1997) and deformable slide masses (e.g. Grilli et al., 2017, Takabatake, 2020, Bullard et al., 2023). From these experimental data sets, predictive relationships connecting slide acceleration, mass, and initial submergence depth to the amplitude of the wave formed have been presented for the seaward wave. Such relationships have not been presented for the landward directed wave, which propagates in the opposite direction of the submarine landslide motion. Further, not all landslides are easily classified as either subaerial or submarine. Consider the 2018 Anak Krakatoa landslide in which the sliding surface was estimated to be 100 m below sea level (Pakoksung et al., 2020), resulting in one third of the total collapse being submerged. In comparison to the end-member conditions of subaerial and submarine failures, the mechanics of wave generation associated with partially submerged failures is much less clear. Granular column collapse experiments provide an idealized experimental framework to explore momentum transfer processes and the resulting waves generated in partially submerged and fully submerged conditions. Work by Cabrera et al., (2020) made use of granular collapse experiments of partially to fully submerged columns to derive a continuous momentum-based function to estimate the maximum seaward wave amplitude based on the initial column submergence ratio (Hw/Ho). However, these experiments were conducted at a small-scale (Ho = 0.15 m) with a width of one particle (2.4 mm diameter). To address this research gap, a series of 22 large-scale granular collapse experiments were conducted by releasing columns of river stone (0.75 m and 0.50 m high) into a laboratory flume reservoir with water depths ranging up to 1.10 m to explore the wave generation and runup processes in both seaward and landward directions. The columns were released by a rapid pneumatically actuated vertical rising gate designed to enable the near instantaneous loss of support of the source volumes resulting in granular collapse. The failure mechanics were captured with high-speed cameras (Figure 1a,b) and wave amplitudes were measured using wave capacitance gauges (Figure 1c,d). This work also provides the first experimental data set of the landward propagating wave and runup associated with submerged granular collapse experiments. Overall, the seaward wave amplitudes measured in these highly-instrumented, large-scale physical models agree with empirical relationships developed in a previous study using smaller-scale models.

Design, analysis, and manufacturing of a glass-plastic hybrid minimalist aspheric panoramic annular lens

We propose a high-performance glass-plastic hybrid minimalist aspheric panoramic annular lens (ASPAL) to solve several major limitations of the traditional panoramic annular lens (PAL), such as large size, high weight, and complex system. The field of view (FoV) of the ASPAL is 360° × (35° ∼ 110°) and the imaging quality is close to the diffraction limit. This large FoV ASPAL is composed of only 4 lenses. Moreover, we establish a physical structure model of PAL using the ray tracing method and study the influence of its physical parameters on compactness ratio. In addition, for the evaluation of local tolerances of annular surfaces, we propose a tolerance analysis method suitable for ASPAL. This analytical method can effectively analyze surface irregularities on annular surfaces and provide clear guidance on manufacturing tolerances for ASPAL. Benefiting from high-precision glass molding and injection molding aspheric lens manufacturing techniques, we finally manufactured 20 ASPALs in small batches. The weight of an ASPAL prototype is only 8.5 g. Our framework provides promising insights for the application of panoramic systems in space and weight-constrained environmental sensing scenarios such as intelligent security, micro-UAVs, and micro-robots.

Enhanced degradation of enrofloxacin in mariculture wastewater based on marine bacteria and microbial carrier

This study aimed to isolate marine bacteria to investigate their stress response, inhibition mechanisms, and degradation processes under high-load conditions of salinity and enrofloxacin (ENR). The results demonstrated that marine bacteria exhibited efficient pollutant removal efficiency even under high ENR stress (up to 10 mg/L), with chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN) and ENR removal efficiencies reaching approximately 88%, 83%, 61%, and 73%, respectively. The predominant families of marine bacteria were Bacillaceae (50.46%), Alcanivoracaceae (32.30%), and Rhodobacteraceae (13.36%). They responded to ENR removal by altering cell membrane properties, stimulating the activity of xenobiotic-metabolizing enzymes and antioxidant systems, and mitigating ENR stress through the secretion of extracellular polymeric substance (EPS). The marine bacteria exhibited robust adaptability to environmental factors and effective detoxification of ENR, simultaneously removing carbon, nitrogen, phosphorus, and antibiotics from the wastewater. The attapulgite carrier enhanced the bacteria's resistance to the environment. When treating actual mariculture wastewater, the removal efficiencies of COD and TN exceeded 80%, TP removal efficiency exceeded 90%, and ENR removal efficiency approached 100%, significantly higher than reported values in similar salinity reactors. Combining the constructed physical and mathematical models of tolerant bacterial, this study will promote the practical implementation of marine bacterial-based biotechnologies in high-loading saline wastewater treatment.

Calculation of the energy dependence of fission fragments yields and kinetic energies distributions for neutron-induced 235U fission

Abstract Fission fragments yields and average total kinetic energy are the fundamental nuclear data essential for applications in nuclear energy and the study of nuclear devices. Certain fission products, such as 95Zr, 99Mo, 140Ba, 144Ce and 147Nd, serve as burnup monitors, assessing the number of fissions induced by neutrons on 235U. However, current experimental data for these fission products worldwide are inconsistent, introducing significant uncertainty into related scientific research. This study employs the Potential-driving Model to calculate the independent yields of 235U and evaluates its advantages in such calculations. Additionally, we investigate the energy dependence of independent yields for select important products. Furthermore, we calculate the cumulative yields of 95Zr, 99Mo, 140Ba, 144Ce, and 147Nd, and compare them with existing literature data to explore the energy dependence of fission products for 235U. Given the lack of fission product yield data above 14.8 MeV, we extend our calculated incident neutron energy to 20 MeV, aiming to support future scientific research. The Geant4 physical model does not consider the influence of incident neutron energy on the average total kinetic energy of fission fragments; thus, we introduce the excitation function of the total kinetic energy of fission fragments recommended by Madland et al., which effectively describes the experimental data of the average total kinetic energy of fragments formed in 235U fission. This study offers a detailed discussion on the energy dependence of fission product yield and average total kinetic energy.

Physical Model Study Of Standing Wave Impact Loads On Gates And Decks Of The Existing Discharge Sluices In The Afsluitdijk The Netherlands

To anticipate the rising sea level and to meet the increased flood safety standards the Afsluitdijk in the northwest of The Netherlands is currently being reconstructed. The Afsluitdijk is the dam, with a length of 30 km, that closes the IJsselmeer basin, the former Zuiderzee. Outside the dam lie the Waddenzee and the North Sea. The reconstruction of the dam includes the reinforcement of the dam and the raising of the crest level, the renovation of the existing discharge sluices and the building of new pumping stations, discharge sluices and flood gates. Higher design waves and water levels, including the effects of sea level rise, present determining loads for drawing up the strength and stability of these hydraulic structures. One of these loads is the wave impact load on the gates, supporting beams and bridge decks of the discharge sluices. At first, these loads have been calculated using a design approach which has been based on analytical models, only partially validated (Almeida and Hofland, 2020). It showed that these loads due to vertical wave impacts can be higher than the strength of the gates and the existing bridge decks. Therefore, load reduction measures are required. To better predict the wave impact loads, which strongly depend on the configuration of the gates and decks, a physical model study in a wave flume has been carried out at Deltares (Capel and van der Werf, 2023). This abstract is about the results of this model study, focusing on the wave impacts on the existing discharge sluices near Kornwerderzand, because at this location these loads are the highest.

Limited effect of the confluence angle and tributary gradient on Alpine confluence morphodynamics under intense sediment loads

Abstract. Confluences are dynamic morphological nodes that are found in all river networks. In mountain regions, they are influenced by hydraulic and sedimentary processes that occur in steep channels during extreme events in small watersheds. Sediment transport in the tributary channel and aggradation in the confluence can be massive, potentially causing overbank flooding and sedimentation into adjacent settlement areas. Previous works dealing with confluences have mainly focused on lowland regions, and those that have focused on mountain areas have used sediment concentrations and channel gradients that are largely under-representative of mountain river conditions. The presented work contributes to filling this research gap with 45 experiments that use a large-scale physical model. Geometric model parameters, the applied grain size distribution, and the considered discharges represent the conditions at 135 confluences in South Tyrol (Italy) and Tyrol (Austria). The experimental program allowed for a comprehensive analysis of the effects of (i) the confluence angle, (ii) the tributary gradient, (iii) the channel discharges, and (iv) the tributary sediment concentration. In contrast to most research dealing with confluences, results indicate that, in the presence of an intense tributary sediment supply and a small tributary-to-main-channel discharge ratio (0.1), the confluence angle does not have a decisive effect on confluence morphology. Adjustments to the tributary channel gradient yielded the same results. A reoccurring range of depositional geomorphic units was observed in which a deposition cone transitioned to a bank-attached bar. The confluence morphology and tributary channel gradient rapidly adjusted, tending towards an equilibrium state to accommodate both water discharges and the sediment load from the tributary. Statistical analyses demonstrated that the confluence morphology was controlled by the combined channel discharge and the depositional or erosional extent was controlled by the sediment concentration. Applying conclusions drawn from lowland confluence dynamics could misrepresent depositional and erosional patterns and the related flood hazard at mountain river confluences.

Rehabilitation Of The Afsluitdijk

The ‘Afsluitdijk’ is a 32 km long dam, which divides the Wadden Sea from the Lake IJssel. The dam has been rehabilitated by increasing the crest level to reduce the wave overtopping and reinforce the armour layers on the seaward and lake side of the dam. The Dutch Ministry of infrastructure and Water Management (Rijkswaterstaat division) commissioned Levvel, a consortium of BAM, Van Oord and Rebel, to carry out this renovation. Rijkswaterstaat encouraged contractors to offer innovative design solutions for the rehabilitation works as the dam is one of the icons in Dutch hydraulic engineering history. Levvel proposed two new armour materials to protect the dam against wave action and reduce the wave overtopping over the crest. A combination of Quattroblocks® a product of Holcim Coastal and Levvel-blocs, internationally known as Xblocplus® by BAM have both been introduced for the first time for the design of the dam. The application of innovative materials and/or techniques may incorporate uncertainties and because of the Afsluitdijk is one of the primary sea defenses preventing the Netherlands for flooding the contract included requirements for the Contractor to carry out physical model tests on the main failure mechanisms of the dam. Besides the contractual requirements, Levvel also used physical model tests to optimize the armour elements and the geometry of the dam. The verification of the design has been carried out with large scale 2D physical model tests in the Delta Flume of Deltares and small scale 2D model tests have been used to optimize design solutions.

Minimal mass of thermal dark matter and the viability of millicharged particles affecting 21-cm cosmology

Thermal freeze-out offers an attractive explanation of the dark matter density free from fine-tuning of initial conditions. For dark matter with a mass below tens of MeV, photons, electrons, and neutrinos are the only available direct Standard Model annihilation products. Using a full three-sector abundance calculation, we determine the minimal mass of dark matter, allowing for an arbitrary branching into electrons/photons and neutrinos that is compatible with current cosmological observations. The analysis takes into account the heat transfer between the various sectors from annihilation elastic scattering, representing the first fully self-consistent analysis that tracks the respective sectors’ temperatures. We thereby provide accurate thermal annihilation cross sections, particularly for velocity-dependent cases, and deduce the sensitivity of current and upcoming CMB experiments to MeV thermal dark matter. In the latter context, we also establish the fine-tuned parameter region where a tiny admixture of neutrinos in the final states rules in MeV-scale p-wave annihilating DM into electrons. Finally, we show that a sub-% millicharged dark matter with an interaction strength that interferes with 21-cm cosmology is still allowed when freeze-out is supplemented with annihilation into neutrinos. For all cases considered, we provide concrete particle physics models and supplement our findings with a discussion of other relevant experimental results. Published by the American Physical Society 2024

A micromechanical scheme with nonlinear concentration functions by physics-guided neural network

The mechanical behavior of heterogeneous materials has been reported to be significantly influenced by the nonlinear properties of both the matrix and interface. However, the micromechanical homogenization methods for predicting the effective properties are challenged in nonlinear problems due to the difficulties in solving the analytical form of the concentration tensors. In this study, we develop a nonlinear micromechanical scheme for heterogeneous materials with complex interfacial behaviors, where the key component, namely nonlinear concentration functions, is determined by the devised physics-guided neural network. In particular, the nonlinear Mori–Tanaka method (NMT) implemented within this new micromechanical scheme yields accurate solutions to axisymmetric nonlinear homogenization problems considering the effects of finite deformation, loading conditions, volume fraction, etc. Furthermore, the NMT is equivalent to the linear Mori–Tanaka method in the condition of the small deformation. Notably, this neural-network-based micromechanical scheme shows good generalization for different types of nonlinear interfaces, while the corresponding approach for generating training data via the finite element method (FEM) is cost-effective. This theoretical framework introduces a novel approach to nonlinear physical modeling, namely, not by the direct regression from the dataset but by deeply embedding neural networks in physical laws.

Shear wave anisotropy response characteristics of laminated shale and its correction methods

Laminated shale exhibits strong anisotropic characteristics in horizontal wells, significantly affecting the application of shear wave travel time data. Using equivalent medium theory and the elastic wave equation, we constructed a physical model for laminated shale, simulating the impact of lamination angle and lamination density on shear wave velocity and shear wave anisotropy coefficient. We established the relationship between the shear wave anisotropy coefficient and the cosine of the lamination angle. The simulation results indicate that shear wave velocity decreases with an increase in lamination angle and density, while the shear wave anisotropy coefficient gradually decreases with an increase in lamination angle and initially increases and then decreases with an increase in lamination density. Moreover, it exhibits a power exponential relationship with the cosine of the lamination angle. After correction, the minimum horizontal principal stress and fracture pressure calculated from shear wave data show an average relative error reduction of 6.72% and 7.77%, respectively, compared to measured data. This demonstrates that the correction method effectively eliminates the impact of laminated shale anisotropy on shear waves.

Investigating Load Arches and the Uplift Capacity of Rock Anchors: A Numerical Approach

AbstractRock anchors, which can withstand substantial forces and are essential for securing the foundations of the built environment, can fail due to steel tensile failure, bond detachment, or rock mass fracturing through uplift. In this study, uplift failure of the rock mass surrounding rock anchors was simulated using both physical and numerical models. The physical models used a specially designed testing rig with predefined sizes and different joint layouts to simulate rock mass uplift failure. The numerical models were calibrated using the physical models and used to investigate scenarios involving increased complexity and greater depths. This paper presents the load distribution within rock masses with orthogonal joints when exposed to an anchor load. Extensive analysis of the numerical models revealed that symmetrical load arches were induced in rock masses that contained a joint set oriented parallel to the loading direction. However, joints that were obliquely oriented relative to the anchor axis induced asymmetrical load arching. The greatest displacement occurred in the direction parallel to the joint set with the smallest angle to the anchor loading axis. Large-scale models with zero or near-zero in situ horizontal stress revealed that the load capacity in a rock mass with continuous vertical joints was relatively low, especially when compared to rock masses with discontinuous vertical joints. It was observed that the uplift capacity of the rock mass around an anchor decreased if the load arches of adjacent anchors overlapped; consequently, a decrease in anchor spacing was found to correspond to a decrease in uplift capacity.