土遗址楠竹锚固界面传力过程研究

竹木锚固技术近年来被广泛应用于土遗址文物加固保护中,但其锚固界面传力机理尚不明确,严重制约着锚固技术的科学化、规模化应用.相关研究成果已经证实了合理的锚固界面粘结-滑移模型对锚固系统性能预测的重要性,本文在此基础上以楠竹-改性泥浆-夯土锚固系统为例,基于考虑完全脱粘现象的三线型粘结-滑移模型开展了锚固界面传力全过程研究.首先将锚固界面传力全过程分为6个连续阶段,分别对各阶段对应的界面应力、应变分布与演化过程进行理论解析,推导了锚杆轴向变形、界面滑移量、界面剪应力、界面剪应变等参数的封闭解,同时给出了极限锚固力与有效锚固长度的计算方法.在此基础上,通过识别载荷-位移曲线特征点对粘结-滑移模型参数进行了标定.最后采用两个土遗址原位拉拔试验对理论解析模型的合理性进行了对比验证,同时着重分析了锚固长度与锚杆轴向刚度两个因素对锚固性能的影响规律.本文提出的解析模型对存在完全脱粘现象的锚固界面传力过程分析具有广泛适用性,能够为土遗址锚固工程设计提供参考与指导.      

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结合新疆高昌故城土建筑遗址保护工程，将土工长丝这种新型锚杆应用于墙体加固. 基于全长粘结式锚杆的Mindlin解，对其抗拔力进行了理论分析，推导出抗拔承载力计算公 式，通过现场试验，对理论分析结果进行了验证，并就影响锚杆抗拔承载力的因素及其破坏 模式进行探讨. 试验结果表明，土工长丝锚杆可以满足土建筑遗址墙体加固的要求；通过加 肋改变锚杆表面状态对锚固力的提高非常显著.     

夯筑遗址加固中楠竹锚杆应力分布变化规律研究

针对西北地区夯筑土遗址楠竹锚固体系,本文在对锚固体中杆体、土体和注浆体的材料性能试验基础上,研究荷载、锚固长度、锚杆直径、孔径、注浆体配比、碳纤维加肋和倾角等因素对楠竹锚杆锚固性能及应力分布规律的影响,同时对荷载、锚固长度和锚杆直径变化状况下的应力分布特征进行数值模拟分析。结果表明:锚杆受力过程中,应力沿杆体分布不均匀;随着锚固长度的增加,应力分布趋于均匀化,而应力峰值沿锚固深度向底端扩展的速度较慢;随着杆体直径和孔径的增加,锚固体应力趋于均匀化,使锚固系统更趋合理化;注浆体强度对应力分布的影响反之;碳纤维加肋可有效提高锚杆的抗劈裂性及锚固力,但会使应力分布更集中化;锚杆布置角度以10°为宜。采用接触单元,模拟杆体与注浆体、注浆体与土体界面间的黏结、咬合效应,不同工况下应力的分布规律、极值大小等参数均与原位试验结果吻合度较好,后期计算分析与研究可予以借鉴。     

全长注浆锚杆锚固段剪应力分布特征试验研究

通过WES-50B型万能材料试验机对分别模拟高、中、低三种强度围岩介质的锚杆模型进行了拉拔试验。结果表明,锚杆锚固段的轴应变及第一、第二交界面的粘结应力分布按照负指数规律衰减。根据模型试验所得锚杆体轴应变试验结果,在一定简化假设的基础上,推导了以锚杆为代表的锚固结构轴力及第一、第二界面剪应力分布公式,分析了锚固结构在均匀介质中内锚固段应力、轴力及交界面的剪应力传递、衰减规律,探讨了拉拔力及介质强度对锚固段剪应力的影响。本文的研究为通过模型试验分析以锚杆为代表的锚固结构第一、第二界面剪应力分布提供了参考。     

MFRP锚杆拉拔水工试验方案有限元分析评估

FRP包裹钢筋(MFRP)锚杆是在FRP锚杆的基础上,增加钢筋层和功能梯度层达到综合FRP锚杆和钢筋锚杆优点的目的.在界面力学模型基础上,建立三维锚杆拉拔试验有限元模型,通过设置接触单元对锚杆在混凝土中拉拔过程进行模拟.首先对钢锚杆进行了拉拔试验的测试,证明本文方法的可行性.然后将方法运用到FRP包裹钢筋(MFRP)锚杆上,通过不同直径和不同锚固长度的锚杆进行测试,得到一些可供参考的结论,评估MFRP拉拔水工试验的可行性.     

基于流变理论的压力型锚杆锚固段荷载传递机理研究

基于荷载传递法,采用西原模型,建立了考虑锚固界面剪切流变特性的压力型锚杆荷载传递函数的微分方程;推导了压力型锚杆锚固体周围岩土体全部处于弹性时、局部处于塑性时和局部处于滑移时,考虑锚固界面流变的锚固界面位移和锚固体轴力分别与位置、时间的关系式;最后,通过拉拔蠕变试验对理论解进行了试验验证。对比分析表明,在P为40kN、100kN、120kN的不同拉拔力作用下,不同位置、不同时刻锚固体轴力的实测值与理论值均吻合较好。反映出推导的理论公式的正确性和适用性。     

基于粘聚力理论的CFRP加固钢板剥离机理研究

碳纤维增强复合材料(CFRP)加固钢板时,CFRP端部往往会发生剥离破坏而导致失效,文中重点研究了CFRP加固钢板的剥离机理.首先根据粘聚力理论建立了CFRP加固钢板的有限元模型;然后对CFRP加固钢板进行了静态拉伸试验研究,并利用试验数据验证了有限元模型;最后基于有限元模型对CFRP剥离破坏的机理进行了研究.分析结果表明:采用粘聚力理论建立的有限元模型可以准确模拟胶层剥离机理;胶层剪应力是CFRP剥离破坏的主要因素,整个过程经历了弹性变形、胶层软化和胶层剥离三个阶段;剥离破坏从CFRP端部开始,逐渐向中间发展,最终导致胶层完全失效.     

锚杆拉拔力学作用机制的DDA方法模拟研究

为研究锚杆拉拔实验中锚杆与岩体间的力学作用过程和机制,在非连续变形分析(discontinuous deformation analysis, DDA)方法中实现锚杆-岩体界面耦合作用算法,采用多线性黏结滑移界面模型模拟锚杆的滑移脱黏,并针对锚杆拉拔力作用节点和脱黏节点提出专门算法。通过模拟结果与实验结果的对比,对文献中的三线性和四线性黏结滑移界面模型进行参数修正,并验证所提锚杆-岩体界面耦合作用算法的正确性和适用性。通过基于修正参数的多线性黏结滑移界面模型的锚杆拉拔DDA模拟,研究了锚杆的拉拔力-拉拔位移关系,以及不同拉拔力条件下锚杆-岩体界面切应力和锚杆轴力的分布与变化规律,并探讨了锚杆弹性模量对锚杆拉拔力学响应的影响。锚杆-岩体界面耦合作用算法的实现,也为岩体锚固的DDA模拟分析创造了条件。     

剪切方向与锚杆倾向垂直条件下锚杆的锚固机制研究

为了探究剪切方向与锚杆倾向垂直条件下锚杆的锚固机制,基于经典梁理论,假定锚杆受"拉-剪"联合作用,锚杆受压侧岩体作用反力沿锚杆方向呈抛物线分布,建立了剪切方向与锚杆倾向垂直的力学分析模型。综合考虑锚杆的轴向变形、挠曲变形、位移变分、变形协调关系以及临界锚固状态,得出了锚杆抗剪力计算公式。分析了锚杆产生扭转变形的原因,通过公式推导得出了相对扭转角与锚杆倾角的关系式以及临界锚固状态时扭矩的表达式。通过直剪试验验证了力学分析模型的可行性,并分析了锚杆内力对锚固效应的贡献。结果表明:试验实测抗剪力符合锚杆抗剪力计算公式,锚杆剪切变形与力学模型吻合较好且存在扭转变形;锚杆倾角越大,抗剪效果越好;锚杆抗剪力主要来自剪力的贡献,随着锚杆倾角的增大,轴力对抗剪的贡献比逐渐增大,剪力对抗剪的贡献比逐渐减小。     

爆炸荷载作用下锚固洞室的损伤演化及锚杆参数的影响

基于模型试验的结果,利用数值分析方法研究了锚固洞室在爆炸荷载作用下的损伤演化过程,并探讨了锚杆间距和长度对地下洞室稳定性的影响。结果表明:数值分析和模型试验中相同位置的压应力时程曲线相似且数值分析的损伤分布和试验结果中的裂纹区域相同,证明数值分析中的数据比较可靠。随着爆炸应力波的传播,在锚固洞室的拱顶中间自由面和拱顶加固区边界形成较大拉伸损伤区域。随着锚杆长度的增加,拱顶中间自由面附近最大损伤区域向拱顶上方发展,逐渐增加;拱顶加固区边界的最大损伤区域向洞室自由面方向发展,并且两边的损伤区域厚度逐渐减小。当锚杆间距相同时,随着锚杆长度的增加,拱顶中间自由面和拱顶加固区边界的最大拉伸损伤区域先减小后增加;当锚杆长度相同时,随着锚杆间距的减小,中间自由面和拱顶加固区边界最大拉伸损伤区域面积逐渐减小,并得到最佳锚杆间距和长度分别为4cm和24cm。     

Using a Lumped Parameter Dynamic Model of a Rock Bolt to Produce Training Data for a Neural Network for Diagnosis of Real Data

Ground anchorage systems are used extensively throughout the world as supporting devices for civil engineering structures such as bridges and tunnels. The condition monitoring of ground anchorages is a new area of research, with the long term objective being a wholly automated or semi-automated condition monitoring system capable of repeatable and accurate diagnosis of faults and anchorage post-tension levels. The ground anchorage integrity testing (GRANIT) system operates by applying an impulse of known force by means of an impact device that is attached to the tendon of the anchorage. The vibration signals that arise from this impulse are complex in nature and require analysis to be undertaken in order to extract information from the vibrational response signatures that is relevant to the condition of the anchorage. Novel artificial intelligence techniques are used in order to learn the complicated relationship that exists between an anchorage and its response to an impulse. The system has a worldwide patent and is currently licensed commercially.A lumped parameter dynamic model has been developed which is capable of describing the general frequency relationship with increasing post-tension level as exhibited by the signals captured from real anchorages. The normal procedure with the system is to train a neural network on data that has been taken from an anchorage over a range of post-tension levels. Further data is needed in order to test the neural network. This process can be time consuming, and the lumped parameter dynamic model has the potential of producing data that could be used for training purposes, thereby reducing the amount of time needed on site, and reducing the overall cost of the system's operation.This paper presents data that has been produced by the lumped parameter dynamic model and compares it with data from a real anchorage. Noise is added to the results produced by the lumped parameter dynamic model in order to match more closely the experimental data. A neural network is trained on the data produced by the model, and the results of diagnosis of real data are presented. Problems are encountered with the diagnosis of the neural network with experimental data, and a new method for the training of the neural network is explored. The improved results of the neural network trained on data produced by the lumped parameter dynamic model to experimental data are shown. It is shown how the results from the lumped parameter dynamic model correspond well to the experimental results.     

Condition Monitoring of Ground Anchorages by Dynamic Impulses: GRANIT System

The GRANIT system operates by applying an impulse of known force by means of an impact device that is attached to the tendon of the anchorage. The vibration response signals resulting from this impulse are complex in nature and require analysis to be undertaken in order to extract information from the vibrational response signatures that is relevant to the condition of the anchorage. In the system, the complicated relationship that exists between characteristics of an anchorage and its response to an impulse is identified and learned by a novel artificial intelligence network based on artificial intelligence techniques.The results presented in this paper demonstrate the potential of the GRANIT system to diagnose the integrity of ground anchorages at a site near Stone, England, by using a trained neural network capable of diagnosing the post-tension level of the anchorage. This neural network was used for the diagnosis of load in a second ground anchorage adjacent to the original anchorage used for the training of the neural network. Further tests were taken with a different anchor head configuration of the anchorage and a different relationship between the signature response of the anchorage to an applied impulse and its post-tension level was found.Problems encountered during the diagnosis of this second set of test signatures by the trained neural network are investigated with the use of a lumped parameter dynamic model. This model is able to identify the parameters in the anchorage system that affect this change in response signature. The results from the investigation lead to a new form of classification for the installed anchorages, based on their anchor head configuration.Laboratory strand anchorage tests were undertaken in order to compare with and validate the results obtained from the field tests and the lumped parameter dynamic model.     

土遗址楠竹锚固界面力学行为与传力机理研究

楠竹锚固技术是处理纵向裂隙引起的大型土遗址稳定问题的重要手段,但此类锚固系统传力机理尚不明确,成为制约其科学化、规模化应用的瓶颈。通过现场拉拔试验研究锚固界面力学行为,将识别出的完全脱粘现象通过摩擦段后的零剪应力段进行表征,建立适用于此类锚固界面的修正三线型粘结-滑移模型。在此基础上,提出基于ANSYS中非线性弹簧单元的锚固界面力学行为数值模拟方法,系统研究锚固系统传力机理。最后,通过与试验结果的对比验证了该模拟方法的可靠性。结果表明：锚杆/浆体界面的滑移失效是锚固系统破坏的主要原因,此类界面微段的受力过程可分为弹性、软化、摩擦和完全脱粘四个阶段,随荷载增加锚固界面高应力区逐渐由加载端向锚固端转移,界面进入完全脱粘阶段后剪应力趋近于零,极限锚固力和有效锚固长度均存在临界值;第一锚固界面传递至第二锚固界面的剪应力非常有限,遗址土体应力处于较低水平。研究结果能够为基于“安全第一,最小干预”原则的土遗址文物锚固优化设计提供参考。     

The prediction of maximum temperature for single chips’ cooling using artificial neural networks

A CFD simulation usually requires extensive computer storage and lengthy computational time. The application of artificial neural network models to thermal management of chips is still limited. In this study, the main objective is to find a neural network solution for obtaining suitable thickness levels and material for a chip subjected to a constant heat power. To achieve this aim a neural network is trained and tested using the results of the CFD program package Fluent. The back-propagation learning algorithm with three different variants, single layer and logistic sigmoid transfer function is employed in the network. By using the weights of the network, various formulations are designed for the output. The network has resulted in R ² values of 0.999, and the mean% errors smaller than 0.8 and 0.7 for the training and test data, respectively. The analysis is extended for different thickness and input power values. Comparison of some randomly selected results obtained by the neural network model and the CFD program has yielded a maximum error of 1.8%, mean absolute percentage error of 0.55% and R ² of 0.99994.     

高速公路高填方路基沉降量的神经网络预测

利用BP神经网络较强的高次非线性映射能力和学习功能 ,建立了基于人工神经网络的高速公路路基沉降量的预测模型。该模型依据现场实测资料 ,避免了计算过程中各种人为因素的影响。通过对某高速公路高填方路基沉降量的现场监测成果的学习与预测检验 ,证明其预测精度与适用性良好 ,具有较大的工程实用价值     

Artificial neural network prediction to the hot compressive deformation behavior of Al–Cu–Mg–Ag heat-resistant aluminum alloy

The behavior of the flow stress of Al-Cu-Mg-Ag heat-resistant aluminum alloys during hot compression deformation was studied by thermal simulation test. The temperature and the strain rate during hot compression were 340-500 °C, 0.001 s⁻¹ to 10 s⁻¹, respectively. Constitutive equations and an artificial neural network (ANN) model were developed for the analysis and simulation of the flow behavior of the Al-Cu-Mg-Ag alloys. The inputs of the model are temperature, strain rate and strain. The output of the model is the flow stress. Comparison between constitutive equations and ANN results shows that ANN model has a better prediction power than the constitutive equations.     

Large-scale vortical structure,supported by small-scale turbulent motions. Helicity as a cause for inverse energy cascade

In this article we demonstrate that turbulent stress contributions which depend on the rotation of the frame of reference (and therefore are system dependent) give rise to the inverse energy cascade, and thus introduce an ordering in the structure of turbulence.We first demonstrate that a non-rotating Boussinesq fluid subject to an artificial force that is not invariant under parity changes of the orthogonal group has a destabilizing effect in the B'enard problem. This destabilization is due to helicity and the stability regimes are divided into two regions: (1) If the helicitys is below a threshold values ^*, then long and very short wavelength disturbances at Rayleigh numbers Ra > Ra^crit are stable whereas those with intermediate wavelengths are unstable. (2) If the helicitys >s _* then all disturbances are unstable.For a rotating turbulent Boussinesq fluid we derive the most simple rotation dependent expression for the stress divergence and demonstrate that it leads quantitatively to a similar helicity dependent force. In the B6nard problem it gives rise to an analogous division of the stability/instability regime as obtained for non-rotating fluids subject to the artificial helicity dependent force.     

ZrO_2/rGO多层复合薄膜的构筑及其摩擦学性能研究

将层层自组装(LBL)技术与原位还原过程相结合在单晶硅片表面成功构筑了与基底结合牢固、性能优异的氧化锆/还原氧化石墨烯(ZrO2/rGO)多层纳米复合薄膜.试验中,引入了具有强附着性和还原性的物质-多巴胺,使得氧化石墨烯(GO)片的表面锚固和原位还原可以一步完成.另外,ZrO2粒子的表面组装通过简单、温和的溶液沉积过程来实现.结果证明:复合薄膜中rGO和ZrO2的引入以及多层结构的构筑赋予复合薄膜许多优异的性能.在不进行热处理的情况下,所构筑的ZrO2/rGO多层薄膜表现出优异的力学性能(硬度:11.54 GPa)和摩擦学性能(摩擦系数:0.12~0.17),可作为减摩抗磨涂层被用于微/纳机电系统(MEMS/NEMS)的表面保护和其他领域.