Novel Methodologies for Preventing Crack Propagation in Steel Gas Pipelines Considering the Temperature Effect

Using the software ANSYS-19.2/Explicit Dynamics,this study performedfinite-element modeling of the large-diameter steel pipeline cross-section for the Beineu-Bozoy-Shymkent gas pipeline with a non-through straight crack,strengthened by steel wire wrapping.The effects of the thread tensile force of the steel winding in the form of single rings at the crack edges and the wires with different winding diameters and pitches were also studied.The results showed that the strengthening was preferably executed at a minimum value of the thread tensile force,which was 6.4%more effective than that at its maximum value.The analysis of the influence of the winding dia-meters showed that the equivalent stresses increased by 32%from the beginning of the crack growth until the wire broke.The increment in winding diameter decelerated the disclosure of the edge crack and reduced its length by 8.2%.The analysis of the influence of the winding pitch showed that decreasing the distance between the winding turns also led to a 33.6%reduction in the length of the straight crack and a 7.9%reduction in the maximum stres-ses on the strengthened pipeline cross-section.The analysis of the temperature effect on the pipeline material,within a range from-40℃ to+50℃,resulted in a crack length change of up to 5.8%.As the temperature dropped,the crack length decreased.Within such a temperature range,the maximum stresses were observed along the cen-tral area of the crack,which were equal to 413 MPa at+50℃ and 440 MPa at-40℃.The results also showed that the presence of the steel winding in the pipeline significantly reduced the length of crack propagation up to 8.4 times,depending on the temperature effect and design parameters of prestressing.This work integrated the existing methods for crack localization along steel gas pipelines.

Deep ResNet Strategy for the Classification of Wind Shear Intensity Near Airport Runway

Intense wind shear(I-WS)near airport runways presents a critical challenge to aviation safety,necessi-tating accurate and timely classification to mitigate risks during takeoff and landing.This study proposes the application of advanced Residual Network(ResNet)architectures including ResNet34 and ResNet50 for classifying I-WS and Non-Intense Wind Shear(NI-WS)events using Doppler Light Detection and Ranging(LiDAR)data from Hong Kong International Airport(HKIA).Unlike conventional models such as feedforward neural networks(FNNs),convolutional neural networks(CNNs),and recurrent neural networks(RNNs),ResNet provides a distinct advantage in addressing key challenges such as capturing intricate WS dynamics,mitigating vanishing gradient issues in deep architectures,and effectively handling class imbalance when combined with Synthetic Minority Oversampling Technique(SMOTE).The analysis results revealed that ResNet34 outperforms other models with a Balanced Accuracy of 0.7106,Probability of Detection of 0.8271,False Alarm Rate of 0.328,F1-score of 0.7413,Matthews Correlation Coefficient of 0.433,and Geometric Mean of 0.701,demonstrating its effectiveness in classifying I-WS events.The findings of this study not only establish ResNet as a valuable tool in the domain of WS classification but also provide a reliable framework for enhancing operational safety at airports.

Analysis on accuracy of Engineering Star RTK in cadastral survey based on SOUTH-CORS

Currently,with the fast development of GPS technique,GPS RTK is rapidly being applied to the cadastral survey. GPS RTK technique not only improves efficiency and accelerates the rate of mapping,but also increases economic efficiency. The authors introduced a method for GPS RTK rapid positioning,which uses continuous operational reference system (CORS) as a base station to change the method of previous map drawing by obtaining real-time three-dimensional coordinates of boundary points and statistically analyzing with the three-dimensional coordinates obtained by Total Station to get the accuracy of GPS RTK real-time coordinates. The application of GPS RTK in practical measurement was discussed when the data link had been lost especially. The results show that the three-dimensional coordinates measured by total station can be replaced by real-time three-dimensional coordinates measured by Engineering Star RTK in the CORS.

Impact of spatially varying rock disturbance on rock slope stability

Degradation of rock mass produced by rock blasting,stress relief,and other causes is an important factor in the assessment of rock strength.Quantified as a disturbance factor,such degradation varies depending on blasting control,stress state and stress relief,and rock mass quality.This study focuses on the impact of disturbance on the safety of slopes.The disturbance in the rock mass is characterized by the geometry of the disturbed zone,its size,the magnitude,and the decaying rate with the distance away from the slope surface.A method accounting for decay of rock disturbance is presented.A study of the impact of rock disturbance characteristics on the quantitative stability measures of slopes was carried out.These characteristics included disturbed zone geometry,its thickness,the maximum magnitude of the disturbance factor,and the rate of disturbance decaying.The thickness of the disturbed zone and the maximum factor of disturbance were found to have the greatest impact.For example,the factor of safety for a 45slope in low-quality rock mass can decrease from 1.96 to 1.09 as the thickness of the disturbed zone increases from 1/4 of slope height H to the double of H and the maximum disturbance factor increases from 0.5 to 1.Uniform thickness of a disturbed zone was found to yield more conservative outcomes than the triangular zones did.The critical failure surfaces were found to be shallow for high rates of disturbance decay,and they were the deepest for spatially uniform disturbance factors.

Deformation characteristics and damage ontologies of soft and hard composite rock masses under impact loading

As one of the most common occurring geological landforms in deep rock formations, the dynamic mechanical properties of layered composite rock bodies under impact loading have been widely studied by scholars. To study the dynamic properties of soft and hard composite rocks with different thickness ratios, this paper utilizes cement, quartz sand and gypsum powder to construct soft and hard composite rock specimens and utilizes a combination of indoor tests, numerical calculations, and theoretical analyses to investigate the mechanical properties of soft and hard composite rock bodies. The test results reveal that:(1) When the proportion of hard rock increases from 20% to 50%, the strength of the combined rock body increases by 69.14 MPa and 87 MPa when the hard rock face and soft rock face are loaded, respectively;however, when the proportion of hard rock is the same, the compressive strength of the hard rock face impact is 9%-17% greater than that of the soft rock face impact;(2) When a specimen of soft and hard combined rock body is subjected to impact loading, the damage mode involves mixed tension and shear damage, and the cracks generally first appear at the ends of the specimen, then develop on the laminar surface from the impact surface, and finally end in the overall damage of the soft rock part. The development rate and the total number of cracks in the same specimen when the hard rock face is impacted are significantly greater than those when the soft rock face is impacted;(3) By introducing Weibull’s statistical strength theory to establish the damage variables of soft-hard combined rock bodies, combined with the DP strength criterion, the damage model and the Kelvin body are concatenated to obtain a statistical damage constitutive model, which can better fit the full stress-strain curve of soft-hard combined rock body specimens under a single impact load.

Anisotropic swelling pressures of compacted GMZ bentonite infiltrated with salt solutions

In the high-level radioactive waste(HLW)deep geological repository,bentonite is compacted uniaxially,and then arranged vertically in engineered barriers.The assembly scheme induces the initial anisotropy,and with hydration,it develops more evidently under chemical conditions.To investigate the anisotropic swelling of compacted Gaomiaozi(GMZ)bentonite and the further response to saline effects,a series of constant-volume swelling pressure tests were performed.Results showed that dry density enhanced the bentonite swelling and raised the final anisotropy,whereas saline inhibited the bentonite swelling but still promoted the final anisotropy.The final anisotropy coefficient(ratio of radial to axial pressure)obeyed the Boltzmann sigmoid attenuation function,decreasing with concentration and dry density,converging to a minimum value of 0.76.The staged evolution of anisotropy coefficient was discovered,that saline inhibited the rise of the anisotropy coefficient(Dd)in the isotropic process greater than the valley(d1)in the anisotropic process,leading to the final anisotropy increasing.The isotropic stage amplified the impact of soil structure rearrangement on the macro-swelling pressure values.Thus,a new method for predicting swelling pressures of compacted bentonite was proposed,by expanding the equations of Gouy-Chapman theory with a dissipative wedge term.An evolutionary function was constructed,revealing the correlation between the occurrence time and the pressure value due to the structure rearrangement and the former crystalline swelling.Accordingly,a design reference for dry density was given,based on the chemical conditions around the pre-site in Beishan,China.The anisotropy promoted by saline would cause a greater drop of radial pressure,making the previous threshold on axial swelling fail.

Effect of silica fume and glass powder for enhanced impact resistance in GGBFS-based ultra high-performance geopolymer fibrous concrete:An experimental and statistical analysis

Solid waste recycling is an economically sound strategy for preserving the environment,safeguarding natural resources,and diminishing the reliance on raw material consumption.Geopolymer technology offers a significant advantage by enabling the reuse and recycling of diverse materials.This research assesses how including silica fume and glass powder enhances the impact resistance of ultra-high-performance geopolymer concrete(UHPGC).In total,18 distinct mixtures were formulated by substituting ground granulated blast furnace slag with varying proportions of silica fume and glass powder,ranging from 10%to 40%.Similarly,for each of the mixtures above,steel fibre was added at a dosage of 1.5%to address the inherent brittleness of UHPGC.The mixtures were activated by combining sodium hydroxide and sodium silicate solution to generate geopolymer binders.The specimens were subjected to drop-weight impact testing,wherein an examination was carried out to evaluate various parameters,including flowability,density at fresh and hardened state,compressive strength,impact numbers indicative of cracking and failure occurrences,ductility index,and analysis of failure modes.Additionally,the variations in the impact test outcomes were analyzed using the Weibull distribution,and the findings corresponding to survival probability were offered.Furthermore,the microstructure of UHPGC was scrutinized through scanning electron microscopy.Findings reveal that the specimens incorporating glass powder exhibited lower cracking impact number values than those utilizing silica fume,with reductions ranging from 18.63%to 34.31%.Similarly,failure impact number values decreased from 8.26%to 28.46%across glass powder contents.The maximum compressive and impact strength was recorded in UHPGC,comprising 10%silica fume with fibres.

Context-Aware Feature Extraction Network for High-Precision UAV-Based Vehicle Detection in Urban Environments

The integration of Unmanned Aerial Vehicles(UAVs)into Intelligent Transportation Systems(ITS)holds trans-formative potential for real-time traffic monitoring,a critical component of emerging smart city infrastructure.UAVs offer unique advantages over stationary traffic cameras,including greater flexibility in monitoring large and dynamic urban areas.However,detecting small,densely packed vehicles in UAV imagery remains a significant challenge due to occlusion,variations in lighting,and the complexity of urban landscapes.Conventional models often struggle with these issues,leading to inaccurate detections and reduced performance in practical applications.To address these challenges,this paper introduces CFEMNet,an advanced deep learning model specifically designed for high-precision vehicle detection in complex urban environments.CFEMNet is built on the High-Resolution Network(HRNet)architecture and integrates a Context-aware Feature Extraction Module(CFEM),which combines multi-scale feature learning with a novel Self-Attention and Convolution layer setup within a Multi-scale Feature Block(MFB).This combination allows CFEMNet to accurately capture fine-grained details across varying scales,crucial for detecting small or partially occluded vehicles.Furthermore,the model incorporates an Equivalent Feed-Forward Network(EFFN)Block to ensure robust extraction of both spatial and semantic features,enhancing its ability to distinguish vehicles from similar objects.To optimize computational efficiency,CFEMNet employs a local window adaptation of Multi-head Self-Attention(MSA),which reduces memory overhead without sacrificing detection accuracy.Extensive experimental evaluations on the UAVDT and VisDrone-DET2018 datasets confirm CFEMNet’s superior performance in vehicle detection compared to existing models.This new architecture establishes CFEMNet as a benchmark for UAV-enabled traffic management,offering enhanced precision,reduced computational demands,and scalability for deployment in smart city applications.The advancements presented in CFEMNet contribute significantly to the evolution of smart city technologies,providing a foundation for intelligent and responsive traffic management systems that can adapt to the dynamic demands of urban environments.

Artificial Intelligence Prediction of One-Part Geopolymer Compressive Strength for Sustainable Concrete

Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological concrete.In terms of performance,fly ash and slag are preferredmaterials for precursors for developing a one-part geopolymer.However,determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely reported.Therefore,in this study,machine learning methods such as artificial neural networks(ANN)and gene expression programming(GEP)models were developed usingMATLAB and GeneXprotools,respectively,for the prediction of compressive strength under variable input materials and content for fly ash and slag-based one-part geopolymer.The database for this study contains 171 points extracted from literature with input parameters:fly ash concentration,slag content,calcium hydroxide content,sodium oxide dose,water binder ratio,and curing temperature.The performance of the two models was evaluated under various statistical indices,namely correlation coefficient(R),mean absolute error(MAE),and rootmean square error(RMSE).In terms of the strength prediction efficacy of a one-part geopolymer,ANN outperformed GEP.Sensitivity and parametric analysis were also performed to identify the significant contributor to strength.According to a sensitivity analysis,the activator and slag contents had the most effects on the compressive strength at 28 days.The water binder ratio was shown to be directly connected to activator percentage,slag percentage,and calcium hydroxide percentage and inversely related to compressive strength at 28 days and curing temperature.

Comprehensive analysis of multiple machine learning techniques for rock slope failure prediction

In this study,twelve machine learning(ML)techniques are used to accurately estimate the safety factor of rock slopes(SFRS).The dataset used for developing these models consists of 344 rock slopes from various open-pit mines around Iran,evenly distributed between the training(80%)and testing(20%)datasets.The models are evaluated for accuracy using Janbu's limit equilibrium method(LEM)and commercial tool GeoStudio methods.Statistical assessment metrics show that the random forest model is the most accurate in estimating the SFRS(MSE=0.0182,R2=0.8319)and shows high agreement with the results from the LEM method.The results from the long-short-term memory(LSTM)model are the least accurate(MSE=0.037,R2=0.6618)of all the models tested.However,only the null space support vector regression(NuSVR)model performs accurately compared to the practice mode by altering the value of one parameter while maintaining the other parameters constant.It is suggested that this model would be the best one to use to calculate the SFRS.A graphical user interface for the proposed models is developed to further assist in the calculation of the SFRS for engineering difficulties.In this study,we attempt to bridge the gap between modern slope stability evaluation techniques and more conventional analysis methods.

A novel solution of rectangular composite laminates under oblique low-velocity impacts

An analytical solution for the responses of composite laminates under oblique low-velocity impacts is presented for a cross-ply,orthotropic,and rectangular plate under oblique low-velocity impacts.The plate is under simply-supported edge conditions,and the dynamic displacement field is expressed in a mixed form by in-plane double Fourier series and cubic polynomials through the thickness as 12 variables for each layer.A system of modified Lagrange equations is derived with all interface constraints.The Hertz and Cattaneo-Mindlin theories are used to solve for the normal and tangential contact forces during the impacts.By further discretizing in the time domain,the oblique impact problem is solved iteratively.While the numerical results clearly show the influence of impact velocity,stacking sequence,mechanical parameters,and geometric parameters,the proposed analytical approach could serve as a theoretical basis for the laminate analysis and design when it is under low-velocity impacts.

AI-Based Helmet Violation Detection for Traffic Management System

Enhancing road safety globally is imperative,especially given the significant portion of traffic-related fatalities attributed to motorcycle accidents resulting from non-compliance with helmet regulations.Acknowledging the critical role of helmets in rider protection,this paper presents an innovative approach to helmet violation detection using deep learning methodologies.The primary innovation involves the adaptation of the PerspectiveNet architecture,transitioning from the original Res2Net to the more efficient EfficientNet v2 backbone,aimed at bolstering detection capabilities.Through rigorous optimization techniques and extensive experimentation utilizing the India driving dataset(IDD)for training and validation,the system demonstrates exceptional performance,achieving an impressive detection accuracy of 95.2%,surpassing existing benchmarks.Furthermore,the optimized PerspectiveNet model showcases reduced computational complexity,marking a significant stride in real-time helmet violation detection for enhanced traffic management and road safety measures.

FEA-Based Fatigue Life Assessment of Cycloidal Displacement Cam and Flat-Faced Follower Mechanism

Cam-followers provide reliable and controlled motions in various mechanical systems. Due to the highly fluctuating load between the cam and follower in operation, the cam-follower may be subjected to a high risk of contact fatigue failure. This paper assesses the fatigue life of a cycloidal displacement cam and a flat-faced follower under the defined loads and constraints. Computer-aided design (CAD) model of the cam-follower is developed in CATIA software and imported to ANSYS software for finite element analysis (FEA) of fatigue life. MATLAB programming is developed for determining the appropriate spring constant and pre-load force to always keep the cam and follower in contact. The fatigue life of the cam-follower has been estimated under the specified operating conditions. The analysis method can be applied to investigate the fatigue life of cams with other profiles, including the modified trapezoidal functions, polynomial functions, etc.

Fuzzy-based approach to quantify the downtime of buildings in developing countries

Earthquake is one of the natural disasters that affects the buildings and communities in developing countries.It causes different levels of damages to the buildings,making them uninhabitable for a period of time,called downtime(DT).This paper proposes a Fuzzy Logic hierarchical method to estimate the downtime of residential buildings in developing countries after an earthquake.The use of expert-based systems allows quantifying the indicators involved in the model using descriptive knowledge instead of hard data,accounting also for the un-certainties that may affect the analysis.The applicability of the methodology is illustrated using the information gathered after the 2015 Gorkha,Nepal,earthquake as a case study.On April 25,2015,Nepal was hit by the Mw 7.8 Gorkha earthquake,which damaged and destroyed more than 500.000 residential buildings.Information obtained from a Rapid Visual Damage Assessment(RVDA)is used through a hierarchical scheme to evaluate the building damageability.Sensitivity analysis based on Sobol method is implemented to evaluate the impor-tance of parameters gathered in the RVDA for building damage estimation.The findings of this work may be used to estimate the restoration time of damaged buildings in developing countries and to plan preventive safety measures.

Prediction of seismic-induced bending moment and lateral displacement in closed and open-ended pipe piles:A genetic programming approach

Ensuring the reliability of pipe pile designs under earthquake loading necessitates an accurate determination of lateral displacement and bending moment,typically achieved through complex numerical modeling to address the intricacies of soil-pile interaction.Despite recent advancements in machine learning techniques,there is a persistent need to establish data-driven models that can predict these parameters without using numerical simulations due to the difficulties in conducting correct numerical simulations and the need for constitutive modelling parameters that are not readily available.This research presents novel lateral displacement and bending moment predictive models for closed and open-ended pipe piles,employing a Genetic Programming(GP)approach.Utilizing a soil dataset extracted from existing literature,comprising 392 data points for both pile types embedded in cohesionless soil and subjected to earthquake loading,the study intentionally limited input parameters to three features to enhance model simplicity:Standard Penetration Test(SPT)corrected blow count(N60),Peak Ground Acceleration(PGA),and pile slenderness ratio(L/D).Model performance was assessed via coefficient of determination(R^(2)),Root Mean Squared Error(RMSE),and Mean Absolute Error(MAE),with R^(2) values ranging from 0.95 to 0.99 for the training set,and from 0.92 to 0.98 for the testing set,which indicate of high accuracy of prediction.Finally,the study concludes with a sensitivity analysis,evaluating the influence of each input parameter across different pile types.

N2气氛下煅烧的黄铁矿对As(Ⅲ)的吸附作用

微量As（Ⅲ）是水中较难去除的毒性物质,天然黄铁矿对水中As（Ⅲ）的去除能力低于磁黄铁矿.将黄铁矿在氮气下高温煅烧使其转变为由磁黄铁矿构成的多孔结构化材料,具有较高的比表面积和表面化学反应活性,在地下水As（Ⅲ）去除方面具有潜在的应用前景.考察了煅烧温度、煅烧时间、pH值以及溶解氧对去除水中As（Ⅲ）的影响.结果表明黄铁矿煅烧生成的单斜磁黄铁矿对As（Ⅲ）具有最佳去除效果,最佳煅烧条件为600℃煅烧1 h;在水溶液有溶解氧条件下煅烧黄铁矿去除As（Ⅲ）的适宜p H值范围较宽（4～10）,而在水溶液缺少溶解氧条件下适宜pH值则变为7～10;煅烧黄铁矿在有氧水溶液中对As（Ⅲ）的吸附比缺氧水溶液中的吸附效果好.

氮气气氛下黄铁矿热分解的矿物相变研究

通过差热-热重分析、X射线粉末衍射(XRD)及磁化率分析等手段,对天然黄铁矿样品在氮气中受热发生的矿物相变过程进行了综合研究。不同温度下黄铁矿煅烧产物的XRD物相分析结果显示,低于500℃时,黄铁矿无显著变化;随着温度的升高(500~600℃),黄铁矿开始转变为单斜磁黄铁矿,进而生成六方磁黄铁矿,磁化率显著升高;700℃~800℃的煅烧产物主要为六方磁黄铁矿,磁化率明显下降,直至900℃进一步形成更稳定的陨硫铁(Fe S),磁化率接近于零。在黄铁矿物相开始转变的温度(500~600℃)区间,黄铁矿生成单斜磁黄铁矿的速率大于单斜磁黄铁矿转化为六方磁黄铁矿的速率;高温(700~900℃)时,黄铁矿转化为单斜磁黄铁矿的速率低于单斜磁黄铁矿转化为六方磁黄铁矿的速率,表现为黄铁矿直接生成六方磁黄铁矿。

地道桥竖向自振频率研究

结合现场实测资料，提出了地道桥动力特性分析的有限元模型。运用有限元计算模型。计算了各种类型地道桥的竖向基频，并提出正常状态地道桥竖向基频的经验表达式，为估算正常状态地道桥基频提供一种参考方法。

Constitutive relation of an orthorhombic polycrystal with the shape coefficients

An orthorhombic polycrystal is an orthorhombic aggregate of tiny crystallites. In this paper, we study the effect of the crystalline mean shape on the constitutive relation of the orthorhombic polycrystal. The crystalline mean shape and the crystalline orientation arrangement are described by the crystalline shape function （CSF） and the orientation distribution function （ODF）, respectively. The CSF and the ODF are expanded as an infinite series in terms of the Wigner D-functions. The expanded coefficients of the CSF and the ODF are called the shape coefficients s^lm0 and the texture coefficients c^lmn respectively. Assuming that Ceff in the constitutive relation depends on the shape coefficients s^lm0 and the texture coefficients c^lmn by the principle of material frame-indifference we derive an analytical expression for C^eff up to terms linear in s^lmo and c^lmn and the expression would be applicable to the polycrystal whose texture is weak and whose crystalline mean shape has weak anisotropy. C^cff contains six unspecified material constants （λ, μ, c, s1, s2, s3）, five shape coefficients （s^2 00, s^2 20, s^4 00, s^4 20, s^4 40）, and three texture coefficients （c^4 99,c^4 20, c^4 40）, The results based on the perturbation approach are used to determine the five material constants approximately. We also find that the shape coefficients 2 and a s^2mo and s^4m0 are all zero if the crystalline mean shape is a cuboid. Some examples are given to compare our computational results.

Centrifuge modeling of buried continuous pipelines subjected to normal faulting

Seismic ground faulting is the greatest hazard for continuous buried pipelines.Over the years,researchers have attempted to understand pipeline behavior mostly via numerical modeling such as the finite element method.The lack of well-documented field case histories of pipeline failure from seismic ground faulting and the cost and complicated facilities needed for full-scale experimental simulation mean that a centrifuge-based method to determine the behavior of pipelines subjected to faulting is best to verify numerical approaches.This paper presents results from three centrifuge tests designed to investigate continuous buried steel pipeline behavior subjected to normal faulting.The experimental setup and procedure are described and the recorded axial and bending strains induced in a pipeline are presented and compared to those obtained via analytical methods.The influence of factors such as faulting offset,burial depth and pipe diameter on the axial and bending strains of pipes and on ground soil failure and pipeline deformation patterns are also investigated.Finally,the tensile rupture of a pipeline due to normal faulting is investigated.