A methodology for damage evaluation of underground tunnels subjected to static loading using numerical modeling

We have proposed a methodology to assess the robustness of underground tunnels against potential failure.This involves developing vulnerability functions for various qualities of rock mass and static loading intensities.To account for these variations,we utilized a Monte Carlo Simulation(MCS)technique coupled with the finite difference code FLAC^(3D),to conduct two thousand seven hundred numerical simulations of a horseshoe tunnel located within a rock mass with different geological strength index system(GSIs)and subjected to different states of static loading.To quantify the severity of damage within the rock mass,we selected one stress-based(brittle shear ratio(BSR))and one strain-based failure criterion(plastic damage index(PDI)).Based on these criteria,we then developed fragility curves.Additionally,we used mathematical approximation techniques to produce vulnerability functions that relate the probabilities of various damage states to loading intensities for different quality classes of blocky rock mass.The results indicated that the fragility curves we obtained could accurately depict the evolution of the inner and outer shell damage around the tunnel.Therefore,we have provided engineers with a tool that can predict levels of damages associated with different failure mechanisms based on variations in rock mass quality and in situ stress state.Our method is a numerically developed,multi-variate approach that can aid engineers in making informed decisions about the robustness of underground tunnels.

3D DEM simulation of hard rock fracture in deep tunnel excavation induced by changes in principal stress magnitude and orientation

To achieve the loading of the stress path of hard rock,the spherical discrete element model(DEM)and the new flexible membrane technology were utilized to realize the transient loading of three principal stresses with arbitrary magnitudes and orientations.Furthermore,based on the deep tunnel of China Jinping Underground Laboratory II(CJPL-II),the deformation and fracture evolution characteristics of deep hard rock induced by excavation stress path were analyzed,and the mechanisms of transient loading-unloading and stress rotation-induced fractures were revealed from a mesoscopic perspective.The results indicated that the stressestrain curve exhibits different trends and degrees of sudden changes when subjected to transient changes in principal stress,accompanied by sudden changes in strain rate.Stress rotation induces spatially directional deformation,resulting in fractures of different degrees and orientations,and increasing the degree of deformation anisotropy.The correlation between the degree of induced fracture and the unloading magnitude of minimum principal stress,as well as its initial level is significant and positive.The process of mechanical response during transient unloading exhibits clear nonlinearity and directivity.After transient unloading,both the minimum principal stress and minimum principal strain rate decrease sharply and then tend to stabilize.This occurs from the edge to the interior and from the direction of the minimum principal stress to the direction of the maximum principal stress on theε1-ε3 plane.Transient unloading will induce a tensile stress wave.The ability to induce fractures due to changes in principal stress magnitude,orientation and rotation paths gradually increases.The analysis indicates a positive correlation between the abrupt change amplitude of strain rate and the maximum unloading magnitude,which is determined by the magnitude and rotation of principal stress.A high tensile strain rate is more likely to induce fractures under low minimum principal stress.

Carpel Tunnel Syndrome: A Link with Vitamin D, Body Mass Index and Hyperlipidemia

Introduction: Carbai tunnel syndrome [CTS] is compression of the median nerve at the wrist , this causes tingling in the hands, pain, numbness, tingling in the fingers particularly the thumb, index and middle fingers, loss of sensation in the hands and fingers, also weakness in the hands. The aim of the present study was to study a possible association which could be found between electrophysiological data in CTS, BMI, hyperlipidemia, and vitamin D [Vit D] levels. Methods: We used a sample of 40 females of the same age group, who were divided into Group 1 as a control consists of 18 healthy females and Group II consisted of 22 age matched females with clinical and electrophysiological evidence of CTS. We measured atherogenic index [AI] as a marker of hyperlipidemia, body mass index [BMI], Vit D status and electrophysiological tests of CTS. Results: Subjects with CTS had deficient Vit D status, they had significantly high atherogenic index (AI), and significant high BMI all compared to control Group I. Median sensory conduction velocity was significantly correlated negatively with BMI and atherogenic index, and positively correlated significantly with Vit D status. But median sensory and motor action potential latency were significantly correlated positively with BMI and atherogenic index, and negatively correlated significantly with Vit D status The analysis revealed BMI, atherogenic index and Vit D status as predictors of median nerve sensory and motor action potential latency and sensory nerve conduction velocity in CTS. Conclusion: The results of this study suggest that obesity and hyperlipidemia are potent CTS risk factors and declared the direct association between Vit D status and CTS occurrence. Our study supports the notion of the compensatory neuroprotective role of Vit D which could have a direct impact on the nerves integrity as it has an anti-inflammatory property which acts in relieving nervous insults and stress. .

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.

Total removal of a large esophageal schwannoma by submucosal tunneling endoscopic resection:A case report and review of literature

BACKGROUND Primary schwannoma is a rare submucosal tumor of the esophagus,which is most often benign,and surgery is the only effective treatment.So far,only a few cases have been reported.Herein,we reported a single case diagnosed with primary esophageal schwannoma that was totally removed by submucosal tunneling endoscopic resection(STER).CASE SUMMARY A 62-year-old man presented to the hospital with a history of resection of a malignant gastric tumor and mild dysphagia.Endoscopic examination revealed a large submucosal elevated lesion in the esophagus 25-30 cm from the incisors.Endoscopic ultrasonography detected a 45 mm×35 mm×31 mm hypoechoic lesion;chest computed tomography showed a mass of approximately 55 mm×35 mm×29 mm.A preliminary examination showed features suggestive of a stromal tumor.Pathological findings indicated esophageal schwannoma.Next,STER alone was performed to completely resect the mass,and the patient recovered well post-surgery.Afterward,the patient was discharged and showed no tumor recurrence at 33 mo of follow-up.CONCLUSION Endoscopic resection is still an effective treatment for large esophageal schwannomas(>30 mm)under meticulous morphological evaluation.

基于D-S证据理论的岩爆预测方法研究

为了有效预测岩爆,提出基于D-S证据理论的岩爆预测方法.首先,选取与岩爆发生相关的6个指标因素作为证据体,并通过模糊物元框架和正态型隶属度函数构建证据体的基本概率分配.然后,利用K均值将证据体分类,并提出簇内证据用传统方式融合而簇间证据用权重方式融合的组合融合规则,以减轻高冲突证据融合的不利影响.最后,将模型应用在秦岭终南山公路隧道2号竖井工程,且与经验方法对比.为了分析预测过程的不确定性和估计岩爆发生概率,采用蒙特卡洛模拟进行抽样仿真,并通过Spearman秩相关系数衡量输入指标的全局敏感性.研究结果表明:输入指标在不同的岩爆案例的影响程度差异较大且方向不同;5个岩爆案例的发生概率在40.8%~70.1%之间.该模型表现出优异的预测分类性能,可为深埋地下工程岩爆预测提供参考.

基于1DCNN和D-S多信息融合的光伏系统直流母线串联电弧故障检测

直流母线是光伏系统输出能源的主干道,由于长期曝晒、风化等作用,电缆、连接器等组件劣化,光伏系统直流母线中发生电弧的可能性急剧上升,极易引发火灾、触电等事故。在光伏系统中,串联电弧故障将使回路电流下降,传统的过流保护无法识别。因此,本文提出基于深度学习和证据理论(D-S)的方法来识别串联电弧故障,该方法基于并联电容器电流和电压信号,采用一维卷积神经网络(1DCNN)对检测数据进行电弧识别;在此基础上将基于单个传感数据的识别结果作为证据,运用D-S多信息合成法则计算得到信度分配,最后利用决策规则判断是否发生串联电弧故障。搭建多参数可调模型获取数据进行测试,结果表明:使用1DCNN识别方法,基于并联电容器电流和电压信号的串联电弧识别准确率分别为97.19%和94.98%,而基于1DCNN和D-S多信息融合的光伏系统直流串联电弧故障检测的识别准确率可提升至99%以上。

D19S433稀有等位基因8.2的分子生物学确认与分析

目的对D19S433基因座稀有等位基因8.2,用分子生物学方法,验证其命名,对突变发生的位置进行确认和分析。方法设计引物对目的基因进行扩增和测序,验证常规命名法。将测序所得序列与D19S433基因座的基础序列进行比对分析。结果Goldeneye DNA 20A和AGCU EX22亲子鉴定系统联合检测,相互比对,确定在检案中发现的分型标准物之外(Off⁃ladder,OL)的等位基因为D19S433基因座的稀有等位基因。经常规漂移校正计算该等位基因为8.2。测序后分析其重复序列确定该等位基因为8.2无误。结论对STR分型中发现的稀有等位基因进行测序,分析其重复序列,可以准确的对其进行命名,确定稀有等位基因突变的位置,丰富中国人群STR数据信息。

改进YOLOv5s的小样本3D打印点阵结构表面缺陷检测

3D打印点阵结构已经广泛应用于航空航天、机械和建筑等行业,其表面缺陷分布不均匀且特征微弱,常常造成漏检和误检。针对这一问题,提出了一种YOLOv5s-PD模型。在该模型中,添加了XSPPF模块和空洞金字塔池化模块,提高了模型对不同缺陷特征的获取能力;针对3D打印点阵结构表面缺陷分布杂乱导致误检率高的问题,在YOLOv5s模型中加入了ECA模块;考虑到3D打印点阵结构表面缺陷尺寸信息无规律并且差异较大而导致的预测框与真实框间方向不一致,采用了SIoU损失函数。采用改进模型对制作的3D打印点阵结构表面缺陷数据集进行检测,结果表明:缺陷检测的召回率达到94.0%,平均精度mAP@0.5达到96.2%,所提出的改进算法可以实现对3D打印点阵结构表面缺陷自动检测。

D-S证据理论在空中目标识别中的应用现状与展望

D-S证据理论作为一种多源信息融合工具,在空中目标识别领域中得到了广泛应用。对D-S证据理论进行了概述;简要梳理了D-S证据理论在空中目标识别领域中的发展脉络,并提出应用中需要解决的三类关键问题;围绕上述问题,重点对该领域中的BPA获取、证据冲突度量、证据融合的应用现状进行综述;最后,基于空域控制视角,对D-S证据理论在该领域中的应用进行了展望。研究可为空中目标识别领域的理论发展和工程应用提供参考。

D-S理论和Markov链组合的桥梁性能退化预测研究

为准确预测桥梁性能退化,考虑到数据随机性和微小扰动发生状态跳跃,提出了一种D-S(Dempster-Shafer)证据理论和Markov链组合的桥梁性能退化组合预测模型和性能退化率的概念.该模型基于指数平滑(exponential smoothing,ES)方法获得新的预测数据序列,并利用Markov链和D-S理论不断进行优化,从而实现桥梁性能退化的组合预测.实际工程的应用结果表明:性能退化率可以直观地表征在梁性能退化的速度.其次,该模型的平均相对误差为1.54%,较于回归、灰色和模糊加权Markov链模型,精度分别提高了1.11%,0.88%和2.8%,而后验差比值为0.242,小于0.35;模型的标准差为9.021,相比其他模型分别减小了3.978,3.405和7.500,而变异系数为0.109,均小于其他模型,验证了组合预测模型在精度和稳定性方面的优越性,可为在役桥梁结构性能退化预测与维护提供理论基础.

基于CD-BSMOTE的D-S证据融合变压器故障诊断

针对变压器油中溶解气体数据集不均衡特性对故障诊断结果的影响,提出一种基于清除临界点改进的边界合成少数类过采样算法均衡数据集和Pearson冲突距离改进D-S证据融合的变压器故障诊断模型。首先,对少数类样本进行均衡化处理,根据K-means聚类结果清除处于临界位置的样本;其次,搭建梯度提升树、随机森林、BP神经网络的故障诊断模型,实现变压器故障初步诊断;接着引入Pearson冲突距离改进D-S证据融合模型,实现诊断结果的融合决策;最后,经实际算例分析,诊断精确率达到92.65%。结果表明,所建模型能有效解决数据不平衡对诊断结果的影响,提高故障诊断精度。

基于D-S证据理论的农作物气候品质预测方法研究:以晚熟杂交柑橘春见为例

【目的】基于多源气象数据构建果实品质(糖含量等级)预测模型,为科学评价果实气候品质及深入挖掘农产品气候资源提供科学依据。【方法】以晚熟柑橘春见果实为研究对象,利用多源数据融合技术、人工神经网络(BP神经网络、RBF神经网络和Elman神经网络)和D-S证据理论,包括气象数据质量控制、特征选取、特征级融合、决策级融合4个步骤,构建基于多源气象数据的果实品质(糖含量等级)预测模型。【结果】春见果实品质预测模型采用BP神经网络预测结果总体准确率为87.50%,平均绝对误差(MAE)为0.150,均方根误差(RMSE)为0.447;RBF神经网络预测结果总体准确率为85.00%,MAE为0.175,RMSE为0.474;Elman神经网络预测结果总体准确率为87.50%,MAE为0.150,RMSE为0.447;D-S证据理论决策融合总体预测准确率达95.20%,分别较BP神经网络、RBF神经网络和Elman神经网络提升7.7百分点、10.2百分点和7.7百分点,MAE和RMSE分别为0.040和0.214,均明显降低。【结论】D-S证据理论决策融合后的果实品质预测准确率相比单一神经网络预测更高、误差更小。

基于加权D-S证据理论的旋翼故障诊断

旋翼作为直升机的升力面和操作面,其健康状态对直升机的安全至关重要。旋翼故障诊断技术仍是直升机健康与使用监测系统(Health and usage monitoring system, HUMS)领域的薄弱环节,开发旋翼故障诊断技术具有重要价值。基于信息融合技术,首先分析了旋翼故障的诊断机理,建立了旋翼故障模型,通过流固耦合仿真获取了不同故障下桨叶、轮毂和机身的故障特征信息,生成数据集进行网络训练和验证。然后,利用遗传算法反向传播(Genetic algorithm-backpropagation, GA-BP)优化神经网络诊断3种类型的直升机旋翼故障,即后缘调整片误调、变距拉杆误调和桨叶质量不平衡。3个逐级神经网络分别对旋翼故障类型、故障位置和故障程度进行了诊断识别。最后采用加权的Dempster-Shafer(D-S)证据理论对旋翼故障进行诊断和分析。结果证明基于改进D-S证据理论的旋翼故障诊断方法能够成功应用到旋翼故障诊断中,并具有良好的识别效果。

基于D-S证据理论改进AHP-熵权的流域洪涝灾害评估研究

考虑致灾因子危险性、孕灾环境敏感性以及承灾体易损性,选取指标构建小清河流域洪涝灾害风险评估指标体系,提出一种基于D-S证据理论的改进AHP-熵权法计算指标权重,求取洪涝灾害风险指数,运用自然断点分级法确定洪涝灾害风险等级,分析小清河流域洪涝灾害风险空间分布情况。结果表明:小清河流域洪涝灾害风险总体上表现出南低北高的趋势,其中高风险区和较高风险区分别占流域面积的8.7%和14.3%,主要分布在小清河干流以及主要支流两岸。所得评估结果同“利奇马”台风发生期间实际洪灾风险分布情况一致,对比证明基于D-S证据理论的改进AHP-熵权法优于AHP和熵权法,可为小清河流域防洪减灾决策提供依据。

2D-STI评价妊娠期高血压患者左心功能与脐动脉S/D的相关性

分析妊娠期高血压患者通过二维斑点追踪成像技术(2D-STI)评价左心功能与脐动脉S/D的相关性。方法 抽取50例院内心脏彩超科在2022年6月~2023年6月间(12个月)接受临床诊断的妊娠期高血压患者为观察组,均在用降压药物治疗之前进行图像的采集,并根据病情程度分成18例的妊娠期高血压组、17例的轻度子痫前期组与15例的重度子痫前期组,再以同期来诊的正常孕妇50例为对照组,研究左心功能与脐动脉S/D的相关性。结果 各组的超声心动图参数LVEDD、LVESD、LVEF、SV、LVMI、IVRT值相比无差异P>0.05,而妊娠期高血压组、轻度子痫前期组与重度子痫前期组的左室应变参数GLS、GLS-Avg值相比对照组差异显著P<0.05,最后轻度子痫前期组与重度子痫前期组的脐动脉S/D值相比对照组更高,具体水平随血压升高而上升P<0.05。对照组的GLS-Avg与脐动脉S/D存在的负相关性P<0.05,其他各组的LVEDD、LVESD、LVEF、SV、LVMI、IVRT、GLS与脐动脉S/D均无相关性P>0.05。妊娠期高血压的病情严重程度与脐动脉 S/D呈正相关P<0.05,与LVEDD、LVESD、LVEF、SV、LVMI、IVRT、GLS、GLS-Avg均无相关性P>0.05。结论 2D-STI可作为评估妊娠期高血压患者左心功能与脐动脉S/D的相关性的手段,正常孕妇的左室收缩功能随血压升高而减弱,同时脐动脉S/D随之升高,GLS-Avg与妊娠期高血压的脐动脉S/D有负相关性。

基于D-S证据理论的配电网接地故障原因综合辨识模型

单相接地故障(single-phase-to-ground fault,SPGF)是配电网中最常见的故障,严重影响配电系统的可靠性和安全性,准确辨识SPGF可以提高配电网接地故障处理的精细化水平。首先,从故障波形中提取能有效反映不同接地故障原因的多域特征组成候选波形特征集,通过多元方差法分析波形特征与接地故障原因的相关性,筛选识别接地故障原因的有效特征;然后,分别设计基于极限学习机和支持向量机的故障原因辨识模型,利用Dempster-Shafer(D-S)证据融合理论对模型的识别结果进行融合,建立了接地故障原因综合辨识模型;最后,基于现场数据对所建立的综合辨识模型的有效性进行了验证,结果表明综合辨识模型优于任何单一辨识模型,验证了该模型的优势和可行性。

改进YOLOv7-tiny与D-S理论结合的实验室人员行为检测研究

针对目前实验室场景缺少对人员行为检测的方法,且主流算法精度低、误检率高的问题,文中提出一种改进YOLOv7-tiny的人员行为检测算法,并通过多源信息融合,提高人员行为在实际实验室场景中的识别准确率。首先,在检测算法主干网络引入GhostNetV2轻量化网络,进一步降低模型计算量和复杂度;其次,在颈部网络嵌入改进后的CBAM_E注意力模块,加强目标重要特征的提取;再次,在预测端使用SIoU替换原有的损失函数,减少角度因素和边界框回归精度的影响。检测结果表明,相较于YOLOv7-tiny,文中算法精度提升10.08%,模型参数量和复杂度分别下降36.45%和46.76%。最后通过将检测数据与传感器采集数据运用D-S证据理论进行信息融合后发现,人员不规范行为检测的误检率得到有效降低。结果表明,该方法可实现对实验室人员不规范行为的有效检测。

基于改进D-S理论的多时刻空中目标威胁评估

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D610含H_(2)S天然气集输管道材料选择

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