新型深层螺旋板载荷试验装置研究及成果应用

常规螺旋板载荷试验过程中,随着测试深度增加,传力杆的弯曲变形逐渐累积,沉降测量的误差逐渐增大,测试精度逐渐降低,且该误差难以避免和修正,导致对地基土综合性状判别误差较大,不能满足设计要求.本文成功研制了集成位移测量和载荷测量的新型螺旋板载荷试验探头,利用内轴和套管相对位移原理,将传统的地表测量沉降位移方式改进为在地下的...     

俞氏子波整形反褶积在提高地震资料分辨率中的研究与应用

在浅层地震勘探中,由于地震资料受表层干扰波影响大,资料纵向分辨率低,信噪比差,层位识别困难。子波整形反褶积技术将子波提取和不同子波通过维纳滤波器结合起来,改变期望输出得到不同要求的整形反褶积因子,本文将余氏子波主峰窄、旁瓣小、分辨率高的优点结合到子波整形反褶积中,获取了一种余氏子波整形反褶积因子,它既能压缩子波提高纵向分辨率,又能通过调节期望输出的余氏子波峰值改善剖面输出的效果,并应用理论模型验证了该方法的有效性,最后在实际资料处理中得到不同分辨率的地震剖面。     

An Adaptive Conjugate Gradient Neural Network–Wavelet Model for Traffic Incident Detection

Artificial neural networks are known to be effective in solving problems involving pattern recognition and classification. The traffic incident-detection problem can be viewed as recognizing incident patterns from incident-free patterns. A neural network classifier has to be trained first using incident and incident-free traffic data. The dimensionality of the training input data is high, and the embedded incident characteristics are not easily detectable. In this article we present a computational model for automatic traffic incident detection using discrete wavelet transform, linear discriminant analysis, and neural networks. Wavelet transform and linear discriminant analysis are used for feature extraction, denoising, and effective preprocessing of data before an adaptive neural network model is used to make the traffic incident detection. Simulated as well as actual traffic data are used to test the model. For incidents with a duration of more than 5 minutes, the incident-detection model yields a detection rate of nearly 100 percent and a false-alarm rate of about 1 percent for two- or three-lane freeways.     

GM(1,1)模型在山区机场沉降预估中的应用

影响山区（高填方）机场沉降的因素很多,可以将高填方沉降过程看成一个灰色系统。本文利用灰色GM（1,1）模型对山区机场的形变监测数据进行建模、分析和预测,最后将其应用于某山区（高填方）机场沉降预测中。经与实测数据对比,表明预测精度良好。     

垃圾土的复合沉降模型研究

垃圾土的沉降包括荷载作用后发生的瞬时沉降、物理蠕变和生化降解3部分。但实际上,物理蠕变和生化降解在整个垃圾土的沉降过程中很难准确区分。因此,本文提出一种新的复合沉降模型,认为整个垃圾土的沉降可以分为2部分,第1部分为由于应力作用产生的瞬时沉降,第2部分综合考虑了垃圾土中的蠕变和生化降解,这里称为长期沉降。根据对自行设计的室内沉降试验装置中各观测点沉降值的非线性回归分析,得到复合沉降模型中的各参数值,并用各观测点参数的平均值得出模型计算值。结果表明,计算值与观测值吻合较好。     

光电编码器在深水离岸结构沉降测量中的应用

新型桶式结构防波堤是位于开敞式海域的深水离岸结构,对于该结构的沉降监测是原位试验中的一大难题。结合该结构的原位观测试验研究了离岸深水结构沉降的量测技术,利用光电编码器实现了沉降观测,并通过与同步实施的GNSS变形监测系统的结果进行对比,证明了将光电编码器用到深水离岸结构的沉降测量中是可行的,其测量精度受被测结构的倾角影响较大。开发了适用于开敞式离岸深水结构的自动化测试系统,利用该系统实现了沉降测量和数据传输。     

基坑开挖中地下水抽取对周围环境的影响分析

深基坑工程中,地下水的渗流将会使基坑周围形成降水漏斗.随着开挖的进行,地下水自由面不断下降,从而使坑外土体的有效应力增加,墙后土体将发生不均匀沉降.为分析基坑开挖过程中,周围土体的沉降及地下水位的变化,对基坑建立三维有限元分析模型,这是一个地下水渗流与地面沉降计算的一体化模型.利用该模型分析了在软土地区深基坑开挖引起地下水渗流场的变化.结果表明,地下水头计算值与工程现场的实测值吻合,此方法可以较好的模拟实际工程.还用该模型分析了基坑开挖地下水抽取引起的地面沉降问题.     

小波分解技术在深基坑监测数据处理中的应用

从小波分解技术的强大的去噪功能出发,提出了基于小波技术的监测数据前处理方法.结合润扬长江公路大桥南锚碇排桩冻结法深基坑的变形监测数据,利用小波分解与重构手段,对变形监测数据进行消噪滤波处理,结果表明,小波分解技术去噪合理有效,可以有效地从误差干扰的变形监测数据中提取数据所反应的原始特征,同时,也不需要监测数据的先验知识,为监测数据的误差消除的一种有效方法,具有一定的理论价值和应用价值.     

基于环境激励测试的开元寺塔动力性能分析

采用环境随机激励技术对定州开元寺塔的动力特性进行现场测试，提出一种新的空间测点布置方案——同向平行测试法，利用该方法可以对开元寺塔平动及扭转模态分量进行有效测量。基于现场实测数据，结合Welch自功率谱密度法和随机子空间法得到砖塔的自振频率、振型等参数，并反演推算出砖塔结构的层间刚度。研究表明砖塔东北侧新砌筑结构层间刚度普遍大于东南侧旧塔体结构，随着高度的增加砖塔的层间刚度逐渐减小。基于动力测试分析数据，建立并修正了ABAQUS有限元模型。有限元模型模态分析结果与实测结果对比误差较小，说明模型较为可靠，可以为古塔的后续动力响应分析提供数值模型。     

基于双曲线模型的软土路基沉降预测研究

某软土路基试验段采用真空-堆载联合预压法处理,以该试验段测得的竖向变形数据为基础,建立双曲线预报模型并进行沉降量预测。通过与实测资料进行比较分析表明,基于双曲线模型的推算值与实测值较为接近,沉降预测曲线与实际观测曲线吻合度较高,能较好的反映真空-堆载联合预压法加固软土路基土体沉降量随时间发展变化的全过程,从而说明该模型在该地区真空-堆载联合预压加固软土路基的沉降预测中是可行的。     

小波时间序列在广州地铁沉降监测数据处理中的应用

论文结合小波分析和时间序列分析的基本原理,提出了小波时间序列模型,并将该模型应用于广州市地铁建设工程沉降监测的预测工作,结果表明该模型具有粗差探测、剔除和抗差干扰的优点,同时比常规模型更具有预测的应用价值。     

用小波神经网络预测深基坑周围地表的沉降量

将小波神经网络应用于深基坑周围地表沉降的预测,提出了一种有效的预测方法,并构造了预测沉降的小波神经网络模型。预测结果表明,该方法收敛速度快、预测精度高,为预测深基坑周围地表沉降量的一种实用方法。     

A sensor-driven structural health prognosis procedure considering sensor performance degradation

This article presents a general framework for sensor-driven structural health prognosis and its application to probabilistic maintenance scheduling. Continuously collected sensor data is used to update the parameters of the stochastic structural degradation model. Uncertainty in sensor data (i.e. measurement error) is explicitly modelled as an evolving stochastic process. The proposed framework utilises Bayesian theorem and Markov Chain Monte Carlo (MCMC) sampling to calculate the posterior distributions of stochastic parameters of the structural degradation model. Bayesian updating allows the use of dynamic diagnostic information with prior knowledge for improved prognosis including risk analysis and remaining useful life (RUL) estimation. Although the proposed sensor-driven structural health prognosis procedure is illustrated with a fatigue-related example, it is applicable to more general applications such as corrosion and pavement cracking. A case study of the fatigue details found in a prototype steelgirder bridge has been conducted to demonstrate the proposed prognosis and maintenance scheduling procedure.     

Short-Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition

Abstract:   This article investigates the application of Kalman filter with discrete wavelet analysis in short-term traffic volume forecasting. Short-term traffic volume data are often corrupted by local noises, which may significantly affect the prediction accuracy of short-term traffic volumes. Discrete wavelet decomposition analysis is used to divide the original data into several approximate and detailed data such that the Kalman filter model can then be applied to the denoised data and the prediction accuracy can be improved. Two types of wavelet Kalman filter models based on Daubechies 4 and Haar mother wavelets are investigated. Traffic volume data collected from four different locations are used for comparison in this study. The test results show that both proposed wavelet Kalman filter models outperform the direct Kalman filter model in terms of mean absolute percentage error and root mean square error.     

扁铲侧胀试验计算地基变形及原型试验研究

直接依据扁铲侧胀原位测试成果,提出地基变形计算方法,并结合某国际机场特大面积堆载预压工程原型试验,在长期沉降、深层土体水平位移、孔隙水压力、土压力、分层沉降等监测资料的基础上,验证方法的合理性,为扁铲侧胀试验计算地基变形提供参考依据。     

小波多分辨分析在钢结构应力监测的应用研究

在钢结构应力监测过程中, 温度对应力监测结果影响明显, 为了得到钢结构真实的载荷应力, 采用小波多分辨分析方法对实时测量数据进行分解和重构, 首先对信号进行去噪, 然后利用温度数据与各分解层下应力数据的相关关系, 剔除出温度应力部分, 研究结果表明, 采用小波多分辨分析方法处理后的应力数据更能准确的反映钢结构真实的载荷应力响应。     

基于小波变换对GPS信号去噪的分析

小波分解能够精细地把信号划分到不同的频带范围内,因此可对含噪信号在不同频带范围内的特征进行信噪分离.本文从GPS精密测量、导航领域的信号去噪角度,探讨了基于小波分析的信噪分离方法,并结合具体实例,说明小波分析对GPS信号消噪处理的实用性及有效性.     

交通荷载下路面振动响应信号的时频特征分析

本文在简单分析了Fourier变换、短时Fourier变换和小波变换的基础上,以实测的交通荷载路面振动信号为模拟信号,采用小波包对模拟信号进行时频特征分析,并结合Fourier变换,给出了小波包各分解重构信号随时间的衰减规律及其在各子频带的频谱图,因此,结合Fourier变换对信号进行小波包分析是一种有效的信号处理途径.研究结果对今后交通荷载作用下路面路基动力响应计算模型的初始边界条件的确定提供依据,为路面路基动力响应的计算奠定基础.     

改进残差修正GM(1,1)模型在基础沉降预测中的应用

普通的灰色残差修正GM(1,1)模型利用差分代替微分,并用原始数据第一个点的值作为时间响应函数的初始值C0,从而给预测带来了一定的误差。本文用多项式逼近法对残差修正GM(1,1)模型进行了改进,并增加了一个初始值参数;还通过一个基础沉降预测的工程实例,对此模型与普通残差修正GM(1,1)模型进行对比。沉降预测结果表明,改进后的模型明显提高了精度,更加适合于基础沉降的预测,具有较好的准确性和工程应用价值。     

Prediction of shield tunneling-induced ground settlement using machine learning techniques

Predicting the tunneling-induced maximum ground surface settlement is a complex problem since the settlement depends on plenty of intrinsic and extrinsic factors. This study investigates the efficiency and feasibility of six machine learning (ML) algorithms, namely, back-propagation neural network, wavelet neural network, general regression neural network (GRNN), extreme learning machine, support vector machine and random forest (RF), to predict tunneling-induced settlement. Field data sets including geological conditions, shield operational parameters, and tunnel geometry collected from four sections of tunnel with a total of 3.93 km are used to build models. Three indicators, mean absolute error, root mean absolute error, and coefficient of determination the (R²) are used to demonstrate the performance of each computational model. The results indicated that ML algorithms have great potential to predict tunneling-induced settlement, compared with the traditional multivariate linear regression method. GRNN and RF algorithms show the best performance among six ML algorithms, which accurately recognize the evolution of tunneling-induced settlement. The correlation between the input variables and settlement is also investigated by Pearson correlation coefficient.