Failure characterization of fully grouted rock bolts under triaxial testing

Confining stresses serve as a pivotal determinant in shaping the behavior of grouted rock bolts.Nonetheless,prior investigations have oversimplified the three-dimensional stress state,primarily assuming hydrostatic stress conditions.Under these conditions,it is assumed that the intermediate principal stress(σ_(2))equals the minimum principal stress(σ_(3)).This assumption overlooks the potential variations in magnitudes of in situ stress conditions along all three directions near an underground opening where a rock bolt is installed.In this study,a series of push tests was meticulously conducted under triaxial conditions.These tests involved applying non-uniform confining stresses(σ_(2)≠σ_(3))to cubic specimens,aiming to unveil the previously overlooked influence of intermediate principal stresses on the strength properties of rock bolts.The results show that as the confining stresses increase from zero to higher levels,the pre-failure behavior changes from linear to nonlinear forms,resulting in an increase in initial stiffness from 2.08 kN/mm to 32.51 kN/mm.The load-displacement curves further illuminate distinct post-failure behavior at elevated levels of confining stresses,characterized by enhanced stiffness.Notably,the peak load capacity ranged from 27.9 kN to 46.5 kN as confining stresses advanced from σ_(2)=σ_(3)=0 to σ_(2)=20 MPa and σ_(3)=10 MPa.Additionally,the outcomes highlight an influence of confining stress on the lateral deformation of samples.Lower levels of confinement prompt overall dilation in lateral deformation,while higher confinements maintain a state of shrinkage.Furthermore,diverse failure modes have been identified,intricately tied to the arrangement of confining stresses.Lower confinements tend to induce a splitting mode of failure,whereas higher loads bring about a shift towards a pure interfacial shear-off and shear-crushed failure mechanism.

Limit load and failure mechanisms of a vertical Hoek-Brown rock slope

The problem considered in this short note is the limit load determination of a vertical rock slope.The classical limit theorem is employed with the use of adaptive finite elements and nonlinear programming to determine upper and lower bound limit loads of a Hoek-Brown vertical rock slope.The objective function of the mathematical programming problem is such as to optimize a boundary load,which is known as the limit load,resembling the ultimate bearing capacity of a strip footing.While focusing on the vertical slope,parametric studies are carried out for several dimensionless ratios such as the dimensionless footing distance ratio,the dimensionless height ratio,and the dimensionless rock strength ratio.A comprehensive set of design charts is presented,and failure envelopes shown with the results explained in terms of three identified failure mechanisms,i.e.the face,the toe,and the Prandtl-type failures.These novel results can be used with great confidence in design practice,in particularly noting that the current industry-based design procedures for the presented problem are rarely found.

Physics-based and data-driven modeling for stability evaluation of buried structures in natural clays

This study presents a hybrid framework to predict stability solutions of buried structures under active trapdoor conditions in natural clays with anisotropy and heterogeneity by combining physics-based and data-driven modeling.Finite-element limit analysis(FELA)with a newly developed anisotropic undrained shear(AUS)failure criterion is used to identify the underlying active failure mechanisms as well as to develop a numerical(physics-based)database of stability numbers for both planar and circular trapdoors.Practical considerations are given for natural clays to three linearly increasing shear strengths in compression,extension,and direct simple shear in the AUS material model.The obtained numerical solutions are compared and validated with published solutions in the literature.A multivariate adaptive regression splines(MARS)algorithm is further utilized to learn the numerical solutions to act as fast FELA data-driven surrogates for stability evaluation.The current MARS-based modeling provides both relative importance index and accurate design equations that can be used with confidence by practitioners.

Optimal urban EV charging station site selection and capacity determination considering comprehensive benefits of vehicle-station-grid

This paper presents an optimization model for the location and capacity of electric vehicle(EV)charging stations.The model takes the multiple factors of the“vehicle-station-grid”system into account.Then,ArcScene is used to couple the road and power grid models and ensure that the coupling system is strictly under the goal of minimizing the total social cost,which includes the operator cost,user charging cost,and power grid loss.An immune particle swarm optimization algorithm(IPSOA)is proposed in this paper to obtain the optimal coupling strategy.The simulation results show that the algorithm has good convergence and performs well in solving multi-modal problems.It also balances the interests of users,operators,and the power grid.Compared with other schemes,the grid loss cost is reduced by 11.1%and 17.8%,and the total social cost decreases by 9.96%and 3.22%.

Charging load prediction method for expressway electric vehicles considering dynamic battery state-of-charge and user decision

Accurate prediction of electric vehicle(EV)charging loads is a foundational step in the establishment of expressway charging infrastructures.This study introduces an approach to enhance the precision of expressway EV charging load predictions.The method considers both the battery dynamic state-of-charge(SOC)and user charging decisions.Expressway network nodes were first extracted using the open Gaode Map API to establish a model that incorporates the expressway network and traffic flow fea-tures.A Gaussian mixture model is then employed to construct a SOC distribution model for mixed traffic flow.An innovative SOC dynamic translation model is then introduced to capture the dynamic characteristics of traffic flow SOC values.Based on this foun-dation,an EV charging decision model was developed which considers expressway node distinctions.EV travel characteristics are extracted from the NHTS2017 datasets to assist in constructing the model.Differentiated decision-making is achieved by utilizing improved Lognormal and Sigmoid functions.Finally,the proposed method is applied to a case study of the Lian-Huo expressway.An analysis of EV charging power converges with historical data and shows that the method accurately predicts the charging loads of EVs on expressways,thus revealing the efficacy of the proposed approach in predicting EV charging dynamics under expressway scenarios.

Aging Mechanism and Life Estimation of Photovoltaic Inverter DC-link Capacitors in Alternating Humid and Thermal Environment

DC-link capacitors play a vital role in managing ripple voltage and current in converters and various devices.This study focuses on exploring the aging characteristics of DC-link capacitors in alternating humid and thermal environments aligned with the operational conditions in photovoltaic and wind power applications.Adhering to relevant power equipment standards,we designed a 24-h alternating humid and thermal aging environment tailored to the requirements of DC-link capacitors.An aging test platform is established,and 20 widely used metallized polypropylene film capacitors are selected for evaluation.Parameters such as the capacitance,equivalent series resistance(ESR),and phase angle are assessed during aging,as well as the onset time and extent of aging at various intervals.This study focuses on the aging mechanisms,analyzing electrode corrosion,the self-healing process,and dielectric aging.Fitting the aging characteristics enabled us to calculate the lifespan of the capacitor and predict it under different degrees of capacitance decay.The results show that under alternating humid and thermal conditions,capacitance attenuation and ESR increase exhibit exponential nonlinearity,influenced by factors such as the oxidation and self-healing of capacitive metal electrodes,dielectric main-chain fracture,and crystal transformation.This study underscores the pivotal role of encapsulation in determining the aging decay time.

Rectangular tunnel heading stability in three dimensions and its predictive machine learning models

Tunnel heading stability in two dimensions(2D)has been extensively investigated by numerous scholars in the past decade.One significant limitation of 2D analysis is the absence of actual tunnel geometry modeling with a considerable degree of idealization.Nevertheless,it is possible to study the stability of tunnels in three dimensions(3D)with a rectangular shape using finite element limit analysis(FELA)and a nonlinear programming technique.This paper employs 3D FELA to generate rigorous solutions for stability numbers,failure mechanisms,and safety factors for rectangular-shaped tunnels.To further explore the usefulness of the produced results,multivariate adaptive regression spline(MARS)is used for machine learning of big dataset and development of design equations for practical design applications.The study should be of great benefit to tunnel design practices using the developed equations provided in the paper.

Manipulation of metavalent bonding to stabilize metastable phase:A strategy for enhancing zT in GeSe

Exploration of metastable phases holds profound implications for functional materials.Herein,we engineer the metastable phase to enhance the thermo-electric performance of germanium selenide(GeSe)through tailoring the chemical bonding mechanism.Initially,AgInTe2 alloying fosters a transition from stable orthorhombic to metastable rhombohedral phase in GeSe by substantially promoting p-state electron bonding to form metavalent bonding(MVB).Besides,extra Pb is employed to prevent a transition into a stable hexagonal phase at elevated temperatures by moderately enhancing the degree of MVB.The stabilization of the metastable rhombohedral phase generates an optimized bandgap,sharpened valence band edge,and stimulative band convergence compared to stable phases.This leads to decent carrier concentra-tion,improved carrier mobility,and enhanced density-of-state effective mass,culminating in a superior power factor.Moreover,lattice thermal conductivity is suppressed by pronounced lattice anharmonicity,low sound velocity,and strong phonon scattering induced by multiple defects.Consequently,a maximum zT of 1.0 at 773 K is achieved in(Ge_(0.98)Pb_(0.02)Se)_(0.875)(AgInTe_(2))_(0.125),resulting in a maximum energy conversion efficiency of 4.90%under the temperature difference of 500 K.This work underscores the significance of regulating MVB to stabilize metastable phases in chalcogenides.

Review of condition monitoring and defect inspection methods for composited cable terminals

Composited cable terminals are critical for reliable power delivery of traditional and traction power supply systems.During its operation,the cable terminal continually undergoes complex circumstances where humidity,temperature,field strength,mechanical stress and other factors continuously change at different levels.In extreme cases,defects or even failures may occur in the cable terminals,leading to catastrophic accidents.This makes the condition monitoring and defect detection of composited cable terminals become an imperative need for the power industry.The“state-of-the-art”technology of cable terminal condition assessment is focussed and a systematic review of the current research progress of cable terminal condition assessment from the operation circumstances to failure mechanisms is provided.It covers both online/offline condition monitoring methods and defects detection approaches.In addition,challenges and future research directions for cable terminal condition assessment are also addressed.It is concluded that the non-destructive and non-intrusive methods like terahertz imaging and ultrasonic testing,and multi-source information fusion methods as well as the digital twin technology have been gaining popularity for cable terminal defect inspection.It is expected the presented work can provide a global field of vision for further advancement of both scholars and industrial engineers in this field.

Comparison of CNN-based deep learning architectures for rice diseases classification

Although convolutional neural network(CNN)paradigms have expanded to transfer learning and ensemble models from original individual CNN architectures,few studies have focused on the performance comparison of the applicability of these techniques in detecting and localizing rice diseases.Moreover,most CNN-based rice disease detection studies only considered a small number of diseases in their experiments.Both these shortcomings were addressed in this study.In this study,a rice disease classification comparison of six CNN-based deep-learning architectures(DenseNet121,Inceptionv3,MobileNetV2,resNext101,Resnet152V,and Seresnext101)was conducted using a database of nine of the most epidemic rice diseases in Bangladesh.In addition,we applied a transfer learning approach to DenseNet121,MobileNetV2,Resnet152V,Seresnext101,and an ensemble model called DEX(Densenet121,EfficientNetB7,and Xception)to compare the six individual CNN networks,transfer learning,and ensemble techniques.The results suggest that the ensemble framework provides the best accuracy of 98%,and transfer learning can increase the accuracy by 17%from the results obtained by Seresnext101 in detecting and localizing rice leaf diseases.The high accuracy in detecting and categorisation rice leaf diseases using CNN suggests that the deep CNN model is promising in the plant disease detection domain and can significantly impact the detection of diseases in real-time agricultural systems.This research is significant for farmers in rice-growing countries,as like many other plant diseases,rice diseases require timely and early identification of infected diseases and this research develops a rice leaf detection system based on CNN that is expected to help farmers to make fast decisions to protect their agricultural yields and quality.

Undrained stability of pit-in-pit braced excavations under hydraulic uplift

Pit-in-pit(PIP)excavations in an aquifer–aquitard system likely undergo catastrophic failures under the hydraulic uplift,the associated undrained stability problem,however,has not been well analyzed in the past.To this end,a hypothetical model of PIP braced excavation in typical soil layers of Shanghai,China is developed using the finite element limit analysis(FELA)tool.The FELA solutions of safety factors(FSs)against hydraulic uplift are verified with the results from the finite element analysis with strength reduction technique(SRFEA)and existing design approaches.Subsequently,FELA is employed to identify the triggering and failure mechanisms of PIP braced excavations subjected to hydraulic uplift.A series of parametric studies considering the various geometric configurations of the PIP excavation,undrained shear strengths of aquitard,and artesian pressures are carried out.The sensitivities of relevant design parameters are further assessed using a multivariate adaptive regression splines(MARS)model that is capable of accurately capturing the nonlinear relationships between a set of input variables and output variables in multi-dimensions.A MARS-based design equation used for predicting FS is finally presented using the artificial dataset from FELA for practical design uses.

Assessment of densified fuel quality parameters:A case study for wheat straw pellet

An investigation was conducted to examine the impact of additive mixing with wheat straw(WS)for pellet making.This study manufactured seven types of pellets with different additive combina-tions to evaluate pellet quality characteristics and their relationships.A laboratory-type hammer mill and a pellet mill were used for feedstock preparation and pellet production.Experimental investigations showed that the lignin content increased from 7.0%to 13.1%,which was a primary need for pelletization.Also,the heating value rose from 17.02 to 20.36 MJ/kg.However,the ash content also increased from 7.09%to 16.2%.Results showed that dimension(length and diame-ter),durability,and tensile strength increased significantly with additives while the fines content decreased.The fines content had an inverse relationship with durability and strength.Wheat straw(60%),together with 10%sawdust(SD),10%corn starch(CS),10%bentonite clay(BC),and 10%biochar(BiC),was optimal with good pellet performance(T7).In addition,both the T5 pellets(70%WS,10%SD,10%BiC,and 10%BC)and the T6 pellets(70%WS,10%SD,10%BiC,and 10%CS)provide suitable quality according to EN plus 2015 standard requirements.The ash content of produced pellet was higher than the recommended value,which suggests that further research onto the alternative additive use for ash reduction is needed.

Underground storage tank blowout analysis:Stability prediction using an artificial neural network

Most geotechnical stability research is linked to“active”failures,in which soil instability occurs due to soil self-weight and external surcharge applications.In contrast,research on passive failure is not common,as it is predominately caused by external loads that act against the soil self-weight.An earlier active trapdoor stability investigation using the Terzaghi’s three stability factor approach was shown to be a feasible method for evaluating cohesive-frictional soil stability.Therefore,this technical note aims to expand“active”trapdoor research to assess drained circular trapdoor passive stability(blowout condition)in cohesive-frictional soil under axisymmetric conditions.Using numerical finite element limit analysis(FELA)simulations,soil cohesion,surcharge,and soil unit weight effects are considered using three stability factors(Fc,Fs,and Fγ),which are all associated with the cover-depth ratio and soil internal friction angle.Both upper-bound(UB)and lower-bound(LB)results are presented in design charts and tables,and the large dataset is further studied using an artificial neural network(ANN)as a predictive model to produce accurate design equations.The proposed passive trapdoor problem under axisymmetric conditions is significant when considering soil blowout stability owing to faulty underground storage tanks or pipelines with high internal pressures.