冲压机械振动对高铁桥梁及行车动力影响分析

高速铁路桥梁的平顺性和稳定性对运营列车的平稳性和安全性有很大影响。为研究冲压机械产生的外部振动激励对高铁桥梁的影响,首先通过对此机械引起的地面振动进行实测,并结合有限元分析软件,确定最大冲击荷载作用下产生的地面振动及传播至桥墩处的振动;然后通过建立列车-轨道-桥梁耦合动力学模型,将桥墩处的地面振动作为激励输入,分析列车以不同速度通过时车辆、桥梁动力学响应。结果表明:地面冲击振动有限元模型计算结果与实测结果基本相符,验证了模型的可靠性;地面振动对桥梁响应会产生一定的影响,距振源50 m处地面振动对桥梁所产生的影响较距振源80 m处(桥墩处)的大,但对运行车辆的影响很小;随着车速由250 km/h至350 km/h,车辆及桥梁各结构的动态响应均有所增大,但都未超出安全限值。因此,冲压机械冲击作用导致的地面振动对列车-轨道-桥梁系统动态服役性能影响非常有限。     

Layout optimization of an inertial energy harvester for miniature underwater mooring platforms

Comprehensive analyses of the layout influence on power performance of a low-frequency miniature horizontal-pendulum-type inertial energy harvester for underwater mooring platforms are presented in this article. The mathematical models are obtained utilizing the Newton-Euler method. The power performance is evaluated by simulations over different parameters, such as damping, excitation frequency, and layout position. Simulation results indicate that the harvester can extract energy from low-frequency excitations over the range of 0.1 Hz–0.4 Hz, and maximum output power can reach to 0.7 W under excitation frequency f = 0.4 Hz. Different layout positions influence the energy harvesting performance of the harvester dramatically. Changing the layout position to positive value can enhance and broaden the power performance. The amplitude responses with different layout positions do not show resonant characteristics, even near the natural frequency of the harvester, due to large amplitude vibrations and strong nonlinear characteristic. As excitation frequency f = 0.4 Hz, there is a zero output power point appears at layout position d = −1.3 m. Moreover, the harnessed power has a quadratic relationship with the layout position value.     

Strain prediction for critical positions of FPSO under different loading of stored oil using GAIFOA-BP neural network

FPSO (floating, production, storage and offloading) units are widely used in the offshore oil and gas industry. Generally, FPSOs have excellent oil storage capacity owing to their huge oil cargo holds. The volume and distribution of stored oil in the cargo holds influence the strain level of hull girder, especially at critical positions of FPSO. However, strain prediction using structural analysis tools is computationally expensive and time consuming. In this study, a prediction tool based on back-propagation (BP) neural network called GAIFOA-BP is proposed to predict the strain values of concerned positions of an FPSO model under different oil storage conditions. The GAIFOA-BP combines BP model and GAIFOA which is a combination of genetic algorithm (GA) and an improved fruit fly optimization algorithm (IFOA). Results from three benchmark tests show that the GAIFOA-BP model has a remarkable performance. Subsequently, a total of 81 sets of training data and 25 sets of testing data are obtained from experiment using fiber Bragg grating (FBG) sensors installed on the surface of an FPSO model. The numerical results show that the GAIFOA-BP is capable of predicting the strain values with higher accuracy as compared with other BP models. Finally, the reserved GAIFOA-BP model is utilized to predict the strain values under the inputs of a 10-day time series of volume and distribution of stored oil. The predicted strain results are further used to calculate the fatigue consumption of measurement points.     

基于时延特性建模的多断面短时交通流预测

针对现有交通流预测方法未充分考虑多断面车流演变规律，提出基于时延特性建模的时空相关性计算方法. 该方法采用对不同断面、不同时刻交通流的分布相似性度量，对输入的车辆到达数据序列进行切割构建时空相似度矩阵，得到相邻断面之间的时延参数. 基于时延特性建模，将多断面之间的流量信息进行融合，使用长短时记忆(LSTM)网络进行流量预测. 通过对实际路段数据的预测和结果分析，验证所提方法的有效性和实用性.     

频繁爆破下隧道内振动传播的数值研究

为揭示频繁爆破下隧道内振动传播衰减规律,文章以蒙华铁路段家坪隧道为工程背景,利用LS-DY NA建立了三维隧道模型,模拟分析了多次爆破下隧道内近区和中远区的振动传播规律。结果表明,质点垂向峰值振速随距离和爆破次数的增大而减小,少数振速有增大的现象;近区振动衰减较快,中远区振动衰减比近区缓慢;中远区a值比近区至少小12.5%,但整体上二者的a值随爆破作用次数的增加呈缓慢上升趋势;系数K随爆破作用次数的增加而降低,近区K值降低速率普遍大于中远区,且近区K值比中远区至少大29.1%;频繁爆破下,中远区比近区较符合萨道夫斯基经验公式。     

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.