基于Bezier插值方法的作大范围旋转运动锥形悬臂梁动力学研究

研究了作大范围旋转运动高度和宽度均沿着梁长度方向变化的锥形悬臂梁动力学问题. 采用 Bezier插值方法对柔性梁的变形场进行描述,考虑柔性梁的纵向拉伸变形和横向弯曲变形,计入由于横向弯曲变形引起的纵向缩短,即非线性耦合项. 运用第二类拉格朗日方程推导出作旋转运动锥形梁的动力学方程,并编制了动力学仿真软件,对作旋转运动锥形梁的频率和动力学响应进行研究. 结果表明: 不同锥形梁截面的动力学响应和系统频率将有明显差异,因此对实际系统合理建模,将能得到更为精确的结果.     

地铁车站开挖过程中的变形监测及仿真分析

为研究地铁车站开挖过程对地下连续墙、管线、墙顶和周边建筑的影响,采用ANSYS有限元方法建模计算,并与实测数据进行对比,验证了模型的可靠度,其最大误差为7.45%。利用该模型计算各部位的沉降,得出:随着开挖过程的进行,第一道支撑轴力从644.8 kN变化到3610 kN,增长460%;墙顶在第10天的沉降位移为0.82 mm;管线在第10天的沉降位移为0.35 mm;考虑地下水位变化时,周边5 m处的建筑会产生0.79 mm的沉降位移。根据力学平衡方程从理论上分析了地下连续墙的变形曲线。结合模拟结果,对不同因素引起的位移变化进行分析并提出解决方案,为今后绿色安全施工提供指导方法。     

高层建筑深基坑支护施工技术

随着我国经济的不断发展与建筑行业的快速发展,高层建筑发展迅猛,深基坑支护施工技术得到了广泛的应用。为满足使用功能及人防要求,目前开发的大型项目一般都带有较深的地下室,因此基础埋置深度也就越深,基坑的开挖深度也越来越深,建筑物的重要性和安全等级也就越来越高。因此,合理的基坑支护技术是保障建筑物安全施工的关键,为了确保建筑物的稳定性,建筑物基础必须要满足地下埋深嵌固的规范要求。本文主要对高层建筑深基坑支护施工技术进行了分析与探讨。     

埋入式基板中传输线间串扰问题研究

选取埋入式基板中的传输线宽度、传输线厚度、传输线耦合长度、耦合间距和基板介电常数5个参数作为关键因素,建立了五因素四水平16种参数水平的正交实验表,进行了极差分析。结果表明:传输线间耦合间距对串扰影响最大,其次是传输线耦合长度,而基板介电常数、传输线宽度和传输线厚度对串扰影响较小;最优参数组合是W4T4S4L1D1,即传输线宽度15 mil,传输线厚度70μm,传输线间耦合间距2 mm,耦合长度5 mm,介电常数4.3。     

偏振耦合测试在分布式应力传感中的理论分析

对高双折射保偏光纤内部的分布式偏振模式耦合在分布式应力传感中的应用进行了理论分析.根据光弹理论和弹性力学理论,建立了横向应力作用下保偏光纤的偏振耦合模型,推导出应力下的偏振耦合强度公式.偏振耦合强度对应力的大小,方向,作用长度以及保偏光纤的拍长都敏感.详细分析了每一个因素对耦合强度的影响.当横向应力的作用方向角为45°,作用长度为保偏光纤的一半时,这时保偏光纤耦合强度对应力传感最敏感.     

基于PSO-BP的磁致伸缩式静力水准系统温度补偿研究

静力水准系统（Hydrostatic Leveling System,HLS）被广泛应用于基坑、桥隧和建筑物结构等工程的沉降监测。针对环境温度变化引起HLS测量误差的问题,对温度、温度变化速率和温度梯度三种温度误差影响因素进行详细分析,并提出了一种基于粒子群优化（PSO）算法和反向传播神经网络（BPNN）的温度补偿模型。通过BP神经网络对钵体液位值建立修正模型,利用PSO算法优化神经网络参数来提高拟合准确度与模型泛化能力。试验结果表明,利用多个影响因素建立的PSO-BP模型对HLS温度补偿后,使钵体液位值的均方误差和最大误差相比于补偿前平均降低80%以上,从而有效减少计算沉降的误差,极大提高HLS测量精度。     

一种快速提取建筑物高度的方法研究

目前有多种利用单张高分辨率卫星影像计算建筑物高度的模型,但是其适用条件限制了建筑物高度提取的速度和精度。为了解决这一难题,提出利用建筑物投影同名点线段长度估算高度方法,并结合高分辨率影像数据和实测建筑物高度数据对估算结果进行分析。结果表明计算模型正确、精度较高(绝对误差在2.04 m以内)、使用参数较少、普适性强,在城市灾害评估与预测方面具有较大的实用潜质。     

遥感影像空间分辨率对城市建筑物高度估算精度的影响

选用WorldView-2、高分二号、高分一号遥感数据,以厦门软件园为研究区,运用面向对象方法及形态学的思想,并结合光谱、阴影和形状约束,构建了适用于不同影像的建筑物及阴影提取流程;运用阴影长度估算法,估算了0.5、1、2m这3种不同空间分辨率建筑物高度;并定量评估影像空间分辨率对建筑物提取精度、建筑物高度估算精度的影响。结果表明:(1)改进的建筑物及阴影提取流程对不同分辨率影像均取得了较高的提取精度,但建筑物提取精度随着遥感影像空间分辨率的降低而略有降低;(2)随着遥感影像空间分辨率的降低,建筑物高度估算精度逐渐降低,但不呈现出线性关系,分辨率从1m上升至0.5 m阶段建筑物高度估算精度提高的速度要高于分辨率从2m上升至1m阶段;(3)GF-1比较适用于高层建筑物的高度估算,GF-2适用于中高层到高层建筑物的高度估算,而WorldView-2对不同高度范围内建筑物的高度均有较高的估算精度。     

基于Ansys的悬臂式簧片振动模态仿真和分析

运用Ansys软件对悬臂式簧片振动模态进行了有限元分析,得到簧片十阶之内的振动频率和振型变化,并将仿真结果与Matlab计算结果进行比较,同时考察了长度、宽度和厚度变化对簧片模态的影响规律,并采用响应面分析法得到长度、宽度和厚度与簧片基频之间的回归方程及各因素对基频的影响显著性。结果表明,簧片的振动频率随着阶次的增加而增大;Ansys仿真得到的频率和振型变化与Matlab计算结果基本一致,误差较小;振动频率随长度和宽度的增加而减小、随厚度的增加而增大;簧片长度和厚度对基频的有显著影响,而宽度对基频的影响不大,影响显著性顺序为:长度厚度宽度。本研究可为悬臂式簧片的结构设计及动态特性分析提供理论依据。     

基于灰色理论的车型自动分类识别

在灰色理论基础上提出了一种车型自动分类识别技术.该技术分为二大部分:第一部分是测量车辆的三维信息,即完成从二维信息到三维信息的转换,获得车辆的长度、宽度和高度特征;第二部分是利用灰色关联分析对车辆进行分类识别.实验表明,与传统的识别方法相比明显提高识别速度与识别精度,且具有很好的实用性.     

基于PSO-LSSVM模型的基坑周边建筑倾斜预测

针对基坑周边建筑倾斜变形影响因素的复杂性,以及监测数据的小样本和非线性特征,提出了一种基于PSO-LSSVM模型的基坑周边建筑倾斜的时间序列预测方法。采用相空间重构对基坑前期施工工况下的周边建筑沉降差时间序列进行重构,构建沉降差预测的学习样本输入到最小二乘支持向量机（LSSVM）中训练。利用粒子群算法（PSO）对LSSVM参数进行优化,获得最优预测模型,对后期工况施工期间的沉降差进行滚动预测,并代入公式计算得到未来倾斜变形值。将该方法用于昆明某基坑工程的周边建筑倾斜预测分析,取得了令人满意的预测结果。     

一种基坑施工地面沉降预测方法

针对深基坑地面沉降难以有效预测的问题,结合灰色理论和基因表达式编程方法,提出一种新的基坑周边地面沉降预测方法——灰色基因表达式编程算法,并将其应用到上海地铁11号线曹杨路地铁车站基坑施工的数据验证中。实验结果表明,根据该方法获得的地面沉降预测值具有很好的预测精度,其与实测值之间吻合度高于传统方法,同时该方法具有根据实测数据进行自我学习的能力。     

基于能量最小化的星载SAR图像建筑物分割方法

针对星载合成孔径雷达 (Synthetic aperture radar, SAR) 图像信噪比低、建筑物目标几何变形大以及周围背景复杂的特点, 本文提出了一种基于能量最小化的星载SAR图像建筑物分割方法.基于星载SAR图像数据构造条件概率能量项, 推动变形曲线向建筑物目标边界演化; 在能量泛函模型中定义长度能量项以保证变形曲线的平滑; 在水平集方法获取的SAR图像初始分割结果的基础上, 以高分辨率光学遥感影像中建筑物目标的轮廓作为先验信息, 构造先验形状能量项约束曲线在第二阶段的演化, 最终实现SAR图像建筑物的分割.实验结果表明, 该方法显著提高了建筑物目标轮廓的分割精度.     

RBF neural networks for the prediction of building interference effects

Wind loads on tall buildings can be quite different from those on an isolated building due to neighboring building effects. With the increase of number of tall buildings in large cities, there is a growing attention to the interference effects among adjacent buildings under wind action. While wind tunnel tests are of importance in the understanding of the physical process, the general quantitative predictions of interference effects are difficult to reach owing to many variables involved. In the present paper, a radial basis function (RBF) neural network is proposed for its strong ability in nonlinear mapping and its higher training speed. Thus the RBF neural network is applied to evaluate the interference effects (expressed by interference factor, IF) by using experimental data obtained from many sources as training patterns. The results indicate that a very good agreement is found between the predicted IF values and the experimental counterparts.     

Mode coupling in the vibration response of asymmetric buildings

Coupling of the lateral and torsional modes in the vibration response of asymmetric buildings is treated using simple three-dimensional computer models. This study will be useful in predicting the torsional coupling in the earthquake response of the building. The computer models are based on energy methods and assume that each floor is a rigid diaphragm with three degrees of freedom (two lateral and one rotational) and can analyse buildings which are unsymmetrical in plan and/or elevation. To treat various types of asymmetry, buildings which are square, rectangular and L-shaped in plan are considered. In these buildings, the position of the core is varied to obtain different degrees of asymmetry and the consequent coupling of the modes of vibration. Numerical examples are presented where the natural periods of vibration and associated mode shapes are obtained for different degrees of asymmetry. The mode shapes demonstrate the torsional coupling inherent in asymmetric buildings. The relationship between the coupled mode shapes obtained in the free vibration analysis and the anticipated dynamic response is discussed.     

传感器在监测隧道开挖对上方铁塔影响的应用

煤矿开采通常会导致铁塔基础发生沉降,塔体受力发生改变,对铁塔基础的沉降和铁塔的受力情况进行了监测.对铁塔受力的监测采用应变计与配套测试仪,并给出了应变计和沉降监测点的布设方案.监测结果表明:铁塔基础与地表的沉降都不大,铁塔的应力变化也不大.这说明隧道采用三台阶式开挖,对此铁塔的影响较小,可以保证铁塔的安全.     

Topological mapping and assessment of multiple settlement time series in deep excavation: A complex network perspective

This study proposed a novel methodology that integrates complex network theory and multiple time series to enhance the systematic understanding of the daily settlement behavior in deep excavation. The original time series of ground surface, surrounding buildings, and structure settlement instrumentation data over an excavation time period were measured into a similarity matrix with correlation coefficients. A threshold was then determined and binarized into adjacent matrix to identify the optimal topology and structure of the complex network. The reconstructed settlement network has nodes corresponding to multiple settlement time series individually and edges regarded as nonlinear relationships between them. A deep excavation case study of the metro station project in the Wuhan Metro network, China, was applied to validate the feasibility and potential value of the proposed approach. Results of the topological analysis corroborate a small-world phenomenon with highly compacted interactions and provide the assessment of the significance among multiple settlement time series. This approach, which provides a new way to assess the safety monitoring data in underground construction, can be implemented as a tool for extracting macro- and micro-level decision information from multiple settlement time series in deep excavation from complex system perspectives.     

Neural simulation of dynamic response of prefabricated buildings subjected to paraseismic excitations

On the basis of measurements on a group of 5-storey flat buildings experimental data were collected. Back-propagation neural networks (BPNNs) are formulated as replicators for data compression. The compressed values of the discretized displacement response spectrum are associated with paraseismic excitations caused by explosions in nearby quarries. The compressed excitations are completed by buildings parameters and used in the master BPNN for simulation of compressed outputs which correspond to the building displacement response on the 4th floor of buildings. The BPNN replicator is also formulated for the compression of the building displacement records in time domain into the target vectors of the master BPNN. After the training the compressed outputs of the master BPNN are decompressed by means of the BPNN decompressor into the building displacement records. In the paper it was proved that the discussed application of BPNNs gives satisfactory neural simulation of displacement records in time domain for vibrations with large amplitudes without analysis of the building motion equations.     

基于一阶剪切板理论的FGM板刚柔耦合动力学建模与仿真

对作大范围运动功能梯度材料(functionally graded materials,FGM)厚板的刚柔耦合动力学问题进行了研究,基于一阶剪切变形理论,从连续介质理论出发,计及了变形位移场中的二次耦合变形量,利用Lagrange方法推导了FGM厚板的刚柔耦合动力学方程,该方程适用于普通均质板和FGM板的动力学分析.采用20自由度矩形单元对变形场进行离散,对不同转速下的悬臂板进行动力学仿真,比较了本文建立的基于一阶剪切理论的模型和基于经典薄板理论的模型,验证了本文模型的正确性以及经典薄板理论的一些不足.研究了不同功能梯度指数下,FGM厚板的横向变形、速度响应频率和固有频率.结果表明,随着转速增大,剪切项对结构动力学行为影响变大;考虑横向剪切项的情况下,计算结果更偏柔性.