基于模糊综合评价法的城市综合管廊结构健康评价方法研究

为解决城市综合管廊结构健康状况评估结果无法量化和指导运维的问题，通过分析模糊综合评价理论在交通隧道结构健康评价方法研究中的应用，依照类比思想，利用该理论建立城市综合管廊结构健康状况评价体系。首先，依据有关养护规定和调研结果，基于不同需求分别建立管廊结构的本体完好状况和本体结构状况评价指标体系； 其次，以交通隧道和综合管廊养护经验为基础，划分各指标的分级判定标准； 然后，采用层次分析法，计算评价指标的权重分配； 最后，采用柯西分布型隶属函数和矩阵型隶属函数确定各指标的隶属度，建立一级和二级模糊综合评价模型，赋予评价向量中各评语相应分值，将评价向量单值化，判定管廊结构健康状况。采用所提出的评价方法应用于实际工程，所得评价结果与经验评判得到的结果相同。     

武汉港阳逻港区多式联运发展思路

多式联运能够充分发挥各种运输方式的综合优势,对于推动物流业降本增效、完善现代综合交通运输体系具有积极意义。武汉港阳逻港区具备发展多式联运的各项要素,但缺乏对多式联运发展的系统性规划,港区多式联运发展相对滞后,综合交通优势尚未有效发挥。通过总结典型案例的发展经验,结合港区内外部综合因素,思考港区多式联运发展思路,探讨港区多式联运的布局规划以及综合服务的提升,并提出相关建议。     

提高TSP探测精度的途径

TSP作为目前最先进的隧道地质超前预报探测仪器，得到了广泛的应用。但是由于在现实中存在各种问题，从而导致该仪器的预测精度受到了极大限制，主要阐述如何提高其预测精度，更好地为隧道的建设服务。     

A methodology for evaluating the performance of model-based traffic prediction systems

Model-based traffic prediction systems (mbTPS) are a central component of the decision support and ICM (integrated corridor management) systems currently used in several large urban traffic management centers. These models are intended to generate real-time predictions of the system’s response to candidate operational interventions. They must therefore be kept calibrated and trustworthy. The methodologies currently available for tracking the validity of a mbTPS have been adapted from approaches originally designed for off-line operational planning models. These approaches are insensitive to the complexity of the network and to the amount and quality of the data available. They also require significant human intervention and are therefore not suitable for real-time monitoring. This paper outlines a set of criteria for designing tests that are appropriate for the mbTPS task. It also proposes a test that meets the criteria. The test compares the predictions of the mbTPS in question to those of a model-less alternative. A t-test is used to determine whether the predictions of the mbTPS are superior to those of the model-less predictor. The approach is applied to two different systems using data from the I-210 freeway in Southern California.     

Real-time trip purpose prediction using online location-based search and discovery services

The use of smartphone technology is increasingly considered a state-of-the-art practice in travel data collection. Researchers have investigated various methods to automatically predict trip characteristics based upon locational and other smartphone sensing data. Of the trip characteristics being studied, trip purpose prediction has received relatively less attention. This research develops trip purpose prediction models based upon online location-based search and discovery services (specifically, Google Places API) and a limited set of trip data that are usually available upon the completion of the trip. The models have the potential to be integrated with smartphone technology to produce real-time trip purpose prediction. We use a recent, large-scale travel behavior survey that is augmented by downloaded Google Places information on each trip destination to develop and validate the models. Two statistical and machine learning prediction approaches are used, including nested logit and random forest methods. Both sets of models show that Google Places information is a useful predictor of trip purpose in situations where activity- and person-related information is uncollectable, missing, or unreliable. Even when activity- and person-related information is available, incorporating Google Places information provides incremental improvements in trip purpose prediction.     

Dynamic demand estimation and prediction for traffic urban networks adopting new data sources

Nowadays, new mobility information can be derived from advanced traffic surveillance systems that collect updated traffic measurements, both in fixed locations and over specific corridors or paths. Such recent technological developments point to challenging and promising opportunities that academics and practitioners have only partially explored so far.The paper looks at some of these opportunities within the Dynamic Demand Estimation problem (DDEP). At first, data heterogeneity, accounting for different sets of data providing a wide spatial coverage, has been investigated for the benefit of off-line demand estimation. In an attempt to mimic the current urban networks monitoring, examples of complex real case applications are being reported where route travel times and route choice probabilities from probe vehicles are exploited together with common link traffic measurements.Subsequently, on-line detection of non-recurrent conditions is being recorded, adopting a sequential approach based on an extension of the Kalman Filter theory called Local Ensemble Transformed Kalman Filter (LETKF).Both the off-line and the on-line investigations adopt a simulation approach capable of capturing the highly nonlinear dependence between the travel demand and the traffic measurements through the use of dynamic traffic assignment models. Consequently, the possibility of using collected traffic information is enhanced, thus overcoming most of the limitations of current DDEP approaches found in the literature.     

半断面法超前周边注浆在煤层采空区治理中的应用

以煤层采空区治理工程为依托,详细介绍了超前周边注浆参数的选取原则,讨论了全断面注浆和半断面注浆作业的优缺点和适用范围.介绍了周边注浆施工材料、设备及施工工艺,采用分析法和检查孔法对注浆效果进行了对比分析.结果表明,采用超前周边注浆加固,能有效控制涌水量,加固松散岩体.     

隧道工程建设地质预报及信息化技术的主要进展及发展方向

复杂的不良地质条件是制约隧道安全高效建设的主要因素,要实现隧道工程的安全高效建设,首先要提高地质预测预报技术水平及其信息化程度。1)介绍我国复杂不良地质隧道超前预报的方法进展及其应用,包括突水突泥灾害源超前探测方法与设备、断层破碎带超前预报、城市地铁溶洞和孤石等探测的进展及应用等;2)介绍我国隧道岩爆监测预警方法及其应用,预报清楚之后就要加强安全风险过程监控;3)介绍基于BIM技术的建筑物(隧道工程)安全风险监控最新进展,包括安全风险实时感知系统和实时预警系统;4)指出隧道工程建设信息化技术的发展方向,包括开展基于大数据技术的TBM/盾构施工的分析与控制研究以及数字隧道向智慧隧道(建设和运营维护)的发展。     

多元评价体系组合模型在铁路隧道变形预测中的应用

为克服传统预测模型结构单一、预测精度及稳定性不足等缺陷,提出多元体系组合预测模型的建模思路。首先,基于支持向量机、BP神经网络及ARMA模型3种单一预测模型,构建铁路隧道变形预测体系;再以均方根误差、误差平方和及平均绝对误差等为评价准则或指标,构建各预测结果的误差评价体系,求解各单项预测模型的权值贡献指数,得到最优组合权值;然后利用后验差检验、残差检验和关联度检验构建预测精度校验体系,对组合预测结果进行检验,评价预测模型的有效性;最后,结合工程实例,对多元体系组合预测模型在特大断面隧道中的变形预测效果进行检验。结果表明:多元评价体系组合模型预测相对误差值均小于2%,具有较高的预测精度,且较单一预测模型具有更高的预测精度,也一致通过相关检验,验证了多元体系组合预测模型的有效性。