基于卫星遥感平台的浅层地下水推断模型

采用遥感—数学—模型相融合的方法建立浅层地下水推断模型。该模型克服以往勘察方法费用高、效率差、周期长的不足,从未知出发,由面到点,层次分明、衔接有序。经实地考察验证,模型精度较高,效率较快,具有一定实用性,不仅可以宏观、快速、高效地确定浅层地下水的分布和范围,而且可以降低费用,缩短周期,为进一步找水工程布置提供依据。     

基于Asaoka法的软基处理动态设计

在软基处理工程中，对实测沉降数据的分析通常仅局限于推算最终沉降和固结度。以Asaoka法为基础，可以利用实测沉降数据建立软基处理动态设计的完整流程。总结了采用Asaoka法推算预压荷载下的最终沉降和固结度的方法，提出进行水平向固结系数、压缩指数等土体参数反演分析和预压工期预测的实用方法，并通过工程实例介绍了其应用。该流程可以充分利用监测数据，实现软基处理的动态化设计, 对设计和施工具有较强的指导意义。     

基于网联数据的电动车快速充电影响因素分析

基于北京市私家电动汽车网联数据，按照充电行为类型提取车辆行程，并对行程中影响快速充电行为的潜在因素进行细致分析；基于Logistic回归模型进行显著性影响因素识别，结果表明，电动汽车续航里程、出行距离、出行时间等因素显著影响电动汽车的快速充电行为；最后，基于显著影响因素建立模型，对私家电动汽车快速充电行为进行预测，预测结果表明，预测模型具有较好的预测效果和可靠度.本文研究成果将有助于优化私家电动汽车的充电行为，提高充电效率.     

Forecasting journey time distribution with consideration to abnormal traffic conditions

Travel time is an important index for managers to evaluate the performance of transportation systems and an intuitive measure for travelers to choose routes and departure times. An important part of the literature focuses on predicting instantaneous travel time under recurrent traffic conditions to disseminate traffic information. However, accurate travel time prediction is important for assessing the effects of abnormal traffic conditions and helping travelers make reliable travel decisions under such conditions. This study proposes an online travel time prediction model with emphasis on capturing the effects of anomalies. The model divides a path into short links. A Functional Principal Component Analysis (FPCA) framework is adopted to forecast link travel times based on historical data and real-time measurements. Furthermore, a probabilistic nested delay operator is used to calculate path travel time distributions. To ensure that the algorithm is fast enough for online applications, parallel computation architecture is introduced to overcome the computational burden of the FPCA. Finally, a rolling horizon structure is applied to online travel time prediction. Empirical results for Guangzhou Airport Expressway indicate that the proposed method can capture an abrupt change in traffic state and provide a promising and reliable travel time prediction at both the link and path levels. In the case where the original FPCA is modified for parallelization, accuracy and computational effort are evaluated and compared with those of the sequential algorithm. The proposed algorithm is found to require only a piece rather than a large set of traffic incident records.     

Competing risk mixture model and text analysis for sequential incident duration prediction

Predicting the duration of traffic incidents sequentially during the incident clearance period is helpful in deploying efficient measures and minimizing traffic congestion related to such incidents. This study proposes a competing risk mixture hazard-based model to analyze the effect of various factors on traffic incident duration and predict the duration sequentially. First, topic modeling, a text analysis technique, is used to process the textual features of the traffic incident to extract time-dependent topics. Given four specific clearance methods and the uncertainty of these methods when used during traffic incidents, the proposed mixture model uses the multinomial logistic model and parametric hazard-based model to assess the influence of covariates on the probability of clearance methods and on the duration of the incident. Subsequently, the performance of estimated mixture model in sequentially predicting the incident duration is compared with that of the non-mixture model. The prediction results show that the presented mixture model outperforms the non-mixture model.     

Semi-submersible wind turbine hull shape design for a favorable system response behavior

Floating offshore wind turbines are a novel technology, which has reached, with the first wind farm in operation, an advanced state of development. The question of how floating wind systems can be optimized to operate smoothly in harsh wind and wave conditions is the subject of the present work. An integrated optimization was conducted, where the hull shape of a semi-submersible, as well as the wind turbine controller were varied with the goal of finding a cost-efficient design, which does not respond to wind and wave excitations, resulting in small structural fatigue and extreme loads.The optimum design was found to have a remarkably low tower-base fatigue load response and small rotor fore-aft amplitudes. Further investigations showed that the reason for the good dynamic behavior is a particularly favorable response to first-order wave loads: The floating wind turbine rotates in pitch-direction about a point close to the rotor hub and the rotor fore-aft motion is almost unaffected by the wave excitation. As a result, the power production and the blade loads are not influenced by the waves. A comparable effect was so far known for Tension Leg Platforms but not for semi-submersible wind turbines. The methodology builds on a low-order simulation model, coupled to a parametric panel code model, a detailed viscous drag model and an individually tuned blade pitch controller. The results are confirmed by the higher-fidelity model FAST. A new indicator to express the optimal behavior through a single design criterion has been developed.     

BP模型在闽江岸线变形预测中的应用

河道岸线变形问题是一个复杂的非线性动力系统问题,文中利用神经网络处理非线性问题的优势,以闽江竹歧至侯官河段为研究对象,建立预测河道岸线变形的BP神经网络模型。通过对河道岸线变形影响因子的分析,确立了2种输入、输出因子模式。结果表明,模型输入、输出因子的选择对模型预测结果影响很大,选用合适的输入、输出因子,会得到比较好的预测效果。     

自密实混凝土梁长期变形的灰色动态拓广模型预测

采用灰色系统模型以及灰色动态拓广模型考虑荷载持续时间对混凝土徐变的影响对6根自密实混凝土梁的长期变形进行预测研究。结果表明:采用灰色动态拓广模型所得结果与试验结果较吻合,该法具有计算简单、预测精度高且不需要大量的试验数据等特点,能方便地用于实际。     

大风浪中船舶航行安全性评价

从船舶在波浪中运动的机理出发，将模糊数学中的层次分析法理论用到大风浪中船舶航行安全性评估上来，开发了相应的计算程序。寻找到了船舶在波浪中航行时的安全航态范围和在某一航速(或航向)时的最佳航向(或航速)，形成了一套“理论航法”，使得驾驶员在操纵船舶时能够得到有效的指导。     

基于Rough Set理论的铁路货运量预测

利用Rough Set理论通过对数据进行分析和推理发现隐含知识的优点，在结合该理论与铁路货运量预测要求的基础上，提出一个基于Rough Set理论的铁路货运量预测流程；合理选择统计指标并将相关原始数据代人预测流程涉及的各步骤后，得出预测我国铁路货运量发展水平的规则集；利用该规则集预测了“十五”期间我国铁路货运量的发展水平；该规则集有望在我国“十一五”规划的制定中发挥一定的参考作用。