基于监控量测的隧道围岩变形时空特性分析

隧道开挖导致围岩应力释放与调整,围岩变形具有明显的时空特性。通过分析围岩变形机理,结合大中山2号隧道现场监控量测数据,对隧道开挖后进口浅埋偏压段围岩变形的时空特性进行了分析。结果表明:隧道进口浅埋偏压段上覆围岩变形与开挖掌子面位置及移动过程具有明显的时空特性,隧道开挖后地表横向沉降呈U字型分布,且随埋深的增加累计沉降量逐渐减小。隧道围岩变形趋于稳定时,拱顶沉降和周边收敛时间特征曲线相似,并进行了回归分析。隧道围岩变形规律的分析研究对于防治工程灾害、确定支护最佳时机、调整施工工序有重要意义。     

指数平滑模型参数反分析及应用

边坡预测是基于现有的各种监测资料分析和预测未来边坡位移或应力发展趋势,从而分析判断边坡在未来某一时期内是否稳定,或者预测滑坡发生时间的过程.反分析方法已经成为目前解决复杂岩土工程的主要方法之一,在岩土工程中得到越来越多的应用.基于现场的监测时间序列,应用岩土工程反分析方法反演指数平滑预测模型的参数,建立其预测模型,并应用于高边坡的变形预测中.通过应用,论证了参数反演后的指数平滑预测模型的有效性和普适性,并发现该预测模型适合于中短期预测,建议应用该模型进行高边坡变形的短中期预测.     

医用血管支架非线性扩张过程快速预测方法的研究

建立了血管支架变形影响因子与其变形结果之间的具有高度非线性识别能力的神经网络模型，通过引入学习因子η和动量因子ψ,采用附加动量项的权值修正方法，优化了网络训练算法，从而提高了网络训练速度和系统鲁棒性．结合实例对网络进行训练，并对预测误差进行了统计假设检验，检验结果表明血管支架变形神经网络智能预测结果与非线性有限元分析结果误差均值低于0．03％，训练后的网络能够较好地对血管支架变形进行预测．在此基础上，基于Pro／Toolkit工具，融合血管支架扩张变形神经网络智能预测模型，建立了血管支架力学性能快速评价工具，该系统实用性强、效率高，能大幅缩短血管支架产品开发周期     

高应力软岩巷道对接式长锚杆支护可行性研究

以沈阳蒲河煤矿软岩巷道支护为工程背景,针对软岩巷道两帮收敛量大、顶底板移近量大、巷道变形严重的特点,通过采用新研发的具有高强度、高延伸率的对接式长锚杆进行支护代替老式的锚杆+锚索支护方式,实现了支护体与巷道围岩变形协调、作用力协调,解决了传统支护技术不适应软岩巷道围岩变形的难题,有效控制了巷道的变形,延长了巷道的服务周期。     

基于PCA-ELM的矿井瓦斯涌出量预测

为进一步提高矿井瓦斯涌出量的预测效率和精度,将主成分分析法(PCA)和极限学习机(ELM)神经网络相结合,建立基于PCA-ELM的矿井瓦斯涌出量预测模型。运用主成分分析法对矿井瓦斯涌出量影响因素样本进行主成分提取,去除各变量之间的线性相关,得到降维后的有效因子。再将这些有效因子作为ELM神经网络的输入层进行训练和预测,借助ELM神经网络不需较多参数调整、学习速度快、泛化性能好的特点,进行快速准确的预测。利用某典型矿井的实测数据进行实例分析,PCA-ELM方法预测的最大误差为0.2589,最小误差为0.0312,平均误差为0.1370,结果表明该预测模型预测速度快、精度高,能够用于矿井瓦斯涌出量预测。     

道路交通事故等间隔序列的灰色预测方法

在分析道路交通系统灰色属性的基础上，论文提出可以将道路交通事故作为道路交通系统行为特征量处理，运用灰色理论和方法来进行道路交通事故预测，在此基础上，建立了道路交通事故的等间隔序列灰色预测模型，并运用实例验证了模型的实用性。     

复杂环境下的浅埋暗挖洞室监测及数值计算研究

以深圳地铁3号线老街站东端站台扩建工程为例，通过对复杂环境下进行浅埋暗挖临时洞室开挖及长期使用过程中的现场监测，得到洞室围岩的位移以及地面沉降量的大小。基于FLAC3D有限差分软件中的Morh-Coulomb准则，对浅埋暗挖洞室引起的围岩变形进行数值计算。将数值计算结果与监测结果进行对比，证明了FLAC3D在此方面的可行性；为现场施工提供了合理的依据。     

岩爆倾向性分析的CW-GT-TODIM预测模型及其应用

综合CW-GT法和TODIM法得到新组合预测模型,用于对岩爆倾向性进行预测。从应力、岩性与围岩方面选取了影响岩爆的8个主要评价指标,构建岩爆等级综合指标预测体系,再通过CW-GT法确定各评价指标的合理组合权重,进而结合TODIM法分别计算得到每个岩爆等级的总体感知优越度,从而预测得到相应岩爆等级。判断结果与AHP-TOPSIS和AHP-FUZZY综合评判模型的结果相吻合,实践表明:该模型适用于岩爆等级的预测,且预测的计算过程简单、清晰,为岩爆等级的预测问题提供了一条新的途径。     

顾及大坝位移残差序列混沌效应的GA-BP预测模型

针对大坝观测数据常规模型训练后的残差混沌效应及模型回归方法的拟合度等问题,文中融合遗传算法与神经网络的数据训练优势,通过构建的遗传神经网络(GA-BP)算法对大坝变形观测序列资料进行回归提取残差序列.基于位移回归残差序列的混沌特性,利用混沌理论对其残差序列进行数值分析,并将残差预测结果与GA-BP预测模型进行叠加.据此,提出了考虑大坝变形残差序列混沌效应的GA-BP监控预测模型.实例表明,文中建立的预测模型的计算精度及收敛速度均得到提高,且考虑残差影响的大坝监控模型的预测效果得到了有效的提升.该模型的建模方法亦可推广应用于边坡及其他水工建筑物的安全预警.     

营口软土地区桩网复合地基沉降规律研究

为研究滨海软土地区高铁地基的沉降问题，以哈大高铁营口东站附近软土地基处理工程为研究对象，对路基填筑过程和工后长期地基沉降进行观测。利用有限元数值分析软件ADINA建立相应的数值计算模型，研究了地基表面沉降规律。研究表明：地基沉降主要发生在路基填筑施工期内，填筑期的沉降量占总沉降量的70％以上；路基填筑施工过程中，地基沉降曲线呈倒钟形，刺入现象明显，在CFG桩周沉降曲线呈“∩”形。基于现场实测数据，建立了工后长期地基沉降预测模型。对比分析结果表明，路基填筑完工三个月后（大约100d）预测公式精度较高，最终预测沉降量与观测值误差在0．21％～0．56％之间，可以满足工后长期沉降预测要求。上述研究成果将为处理滨海地区高速软土路基工程，预测和控制工后沉降提供了合理的依据。     

基于概率积分法和数值模拟的采空塌陷区对高速公路影响的预测

利用采煤引起的开采沉陷研究理论和概率积分法,以及ADINA三维模型的建立,结合已有采空区沉陷理论及经验公式和数值模拟对其进行综合分析,并选取合理的计算参数和建立基本数学模型.结果表明,概率积分法和数值模拟的综合分析能够比较准确预计煤层采动覆岩的剩余变形量,对于未来进行灾害的预测和采空塌陷区上高速公路的建设具有重要的意义.     

顺层岩质边坡加陡开采的稳定性研究

为了研究确定顺层边坡陡帮开采的稳定性，以元宝山露天矿南帮边坡为研究对象，基于刚体极限平衡理论，采用RFPA中的细观本构模型，研究分析南帮边坡受采动影响应力和位移随时间的变化规律，分析滑坡机理、滑坡模式，并确定最终境界的合理边坡角。结果表明：采用细观模型得到的岩体变形随时间的变化规律与现场观测基本吻合，上部岩体沿圆弧滑面拉裂滑移，下部沿弱层组合滑面剪切滑移，且滑体上部均以下沉为主，向采空区发生水平位移，下部以水平位移为主。计算剖面边坡的稳定系数Fs〈1．2，应放缓边坡角使φ=18°以保证边坡稳定使露天矿安全生产并获得最大效益。     

Structural,VAR and BVAR models of exchange rate determination: A comparison of their forecasting performance

This paper compares the out-of-sample forecasting accuracy of a wide class of structural, BVAR and VAR models for major sterling exchange rates over different forecast horizons. As representative structural models we employ a portfolio balance model and a modified uncovered interest parity model, with the latter producing the more accurate forecasts. Proper attention to the long-run properties and the short-run dynamics of structural models can improve on the forecasting performance of the random walk model. The structural model shows substantial improvement in medium-term forecasting accuracy, whereas the BVAR model is the more accurate in the short term. BVAR and VAR models in levels strongly out predict these models formulated in difference form at all forecast horizons.     

岩质矿山边坡稳定性动态分析及研究

结合边坡岩体的变形监测信息，提出了矿山边坡稳定性动态分析技术。基于监测位移与计算位移之差最小为准则，根据矿山实际开采情况，建立对应于当前开采状况的数值分析模型，并进行等效边坡岩体参数的模式识别。     

Political manoeuvrings as sources of measurement errors in forecasts

We test the extent to which political manoeuvrings can be the sources of measurement errors in forecasts. Our objective is to examine the forecast error based on a simple model in which we attempt to explain deviations between the March budget forecast and the November forecast, and deviations between the outcome and the March budget forecast in the UK. The analysis is based on forecasts made by the general government. We use the forecasts of the variables as alternatives to the outcomes. We also test for political spins in the GDP forecast updates and the GDP forecast errors. We find evidence of partisan and electoral effects in forecast updates and forecast errors. Copyright © 2005 John Wiley & Sons, Ltd.     

Forecasting the 10‐year US Treasury rate

This study compares the performance of two forecasting models of the 10‐year Treasury rate: a random walk (RW) model and an augmented‐autoregressive (A‐A) model which utilizes the information in the expected inflation rate. For 1993–2008, the RW and A‐A forecasts (with different lead times and forecast horizons) are generally unbiased and accurately predict directional change under symmetric loss. However, the A‐A forecasts outperform the RW, suggesting that the expected inflation rate (as a leading indicator) helps improve forecast accuracy. This finding is important since bond market efficiency implies that the RW forecasts are optimal and cannot be improved. Copyright © 2009 John Wiley & Sons, Ltd.     

A Method of Retail Mortgage Stress Testing: Based on Time‐Frame and Magnitude Analysis

In this study, a non‐stationary Markov chain model and a vector autoregressive moving average with exogenous variables coupled with a logistic function (VARMAX‐L) are used to analyze and predict the stability of a retail mortgage portfolio, based on the stress test framework. The method introduced in this paper can be used to forecast the transition probabilities in a retail mortgage over pre‐specified states, given a shock with a certain magnitude. Hence this method provides a dynamic picture of the portfolio transition process through which one can assess its behavior over time. While the paper concentrates on retail mortgages, the methodology of this study can be adapted also to analyze other credit products in banks. Copyright © 2015 John Wiley & Sons, Ltd.     

Reducing uncertainty in short-term projections: Linkage of monthly and quarterly models

This paper shows how monthly data and forecasts can be used in a systematic way to improve the predictive accuracy of a quarterly macroeconometric model. The problem is formulated as a model pooling procedure (equivalent to non-recursive Kalman filtering) where a baseline quarterly model forecast is modified through ‘add-factors’ or ‘constant adjustments’. The procedure ‘automatically’ constructs these adjustments in a covariance-minimizing fashion to reflect the revised expectation of the quarterly model's forecast errors, conditional on the monthly information set. Results obtained using Federal Reserve Board models indicate the potential for significant reduction in forecast error variance through application of these procedures.     

Quarterly forecasts of the italian business cycle by means of monthly economic indicators

Recent years have witnessed a growing availability of high-frequency indicators which can be used to forecast future economic activity. This paper shows how some of the widely known monthly economic indicators at present available in Italy can be used in a systematic and coordinated manner to forecast the main variables of the National Accounts. In order to reduce as much as possible the amount of judgment in the analysis of the business cycle, a model-based approach is adopted. Thus, a pseudo macro-econometric model of the Italian economy is built, which can be used to produce forecasts one semester ahead of the last National Accounts data release. The model can be used autonomously as well as in combination with the Bank of Italy's quarterly econometric model.     

A comparison between linear and nonlinear forecasts for nonlinear AR models

In this paper the relative forecast performance of nonlinear models to linear models is assessed by the conditional probability that the absolute forecast error of the nonlinear forecast is smaller than that of the linear forecast. The comparison probability is explicitly expressed and is shown to be an increasing function of the distance between nonlinear and linear forecasts under certain conditions. This expression of the comparison probability may not only be useful in determining the predictor, which is either a more accurate or a simpler forecast, to be used but also provides a good explanation for an odd phenomenon discussed by Pemberton. The relative forecast performance of a nonlinear model to a linear model is demonstrated to be sensitive to its forecast origins. A new forecast is thus proposed to improve the relative forecast performance of nonlinear models based on forecast origins. © 1997 John Wiley & Sons, Ltd.