某连续箱梁桥静载试验分析

以某连续箱梁桥为研究对象,采用有限元计算软件midas/civil对结构进行计算。通过结构计算、现场检查、荷载试验等工作,评价该桥的工作性能和承载能力,对于今后同类型桥梁的试验测试方法和结构承载能力评价具有一定的参考价值。     

FMEA和FCE方法在动力定位控制系统中的应用

针对传统的FMEA(故障模式与影响分析)过于定性化、主观化以及不能进行量化分析的问题,本文提出了一种量化FMEA风险等级数的评定方法,并通过层次分析法(AHP)和模糊综合评判法(FCE)的理论分析,构建了动力定位控制系统的综合评判模型,将FMEA的结果进行了量化分析,从而能更好地预防故障的发生。以Arrow号近海支援船DP2动力定位(DP)控制系统为例,选择操作站操作面板为研究对象,对该系统的各个组成部分及其模块进行了故障模式与影响分析并做出评判,列出了可能发生的故障模式,确定了故障等级并提出了建议措施,其分析结果证明FMEA和模糊综合评判法是有效的。通过对动力定位控制系统进行故障模式与影响分析(FMEA),可以对动力定位控制系统和设备进行改进、维护和完善,以提高其可靠性,延长设备的使用寿命,提高动力定位船舶的安全水平。     

船舶消防系统失效模型的建立与分析

消防系统是保障船舶生命力的重要复杂系统。针对消防系统存在着失效不确定和失效路径模糊的问题,结合船舶的特点应用故障树建立了船舶消防系统失效模型。为了有效地避免故障树的计算量组合爆炸问题,采用二元决策图对该模型进行定性和定量分析。分析结果明确了系统失效路径,简便地得到了各失效因素的关键重要度,计算结果精确,并提出了相应措施来降低船舶消防系统失效的发生。     

Understanding the distribution characteristics of bus speed based on geocoded data

Data-driven traffic management and control has attracted much attention recently. This paper conducts a series of coherent analyses based on geocoded data to understand the distribution characteristics of bus operational speed and to explore the potential applications of speed distributions. First, an original bipartite model is adopted for capturing instantaneous speed where the suspended and moving states are considered separately and a two-component mixed Weibull distribution is used to model the speed distribution in moving states. The mixed Gaussian distribution with variable components is found to be capable of expressing the speed distribution patterns of different road sections. Second, elaborate analyses on the basis of speed distribution modelling are conducted: (i) regression analyses are conducted to explore the correlations between parameters of instantaneous speed distributions and traffic related factors; (ii) a powerful clustering method using Kullback-Leibler divergence as the distance measure is proposed to grade the road sections of a bus route. These results can be utilized in fields such as bus operations management, bus priority signal control and infrastructure transformation aiming to improve the efficiency of bus operations systems.     

Efficiency in Asian railways: a comparison between data envelopment analysis approaches

Performance analysis has become a vital part of the management practices in the logistics infrastructure. Although there are numerous applications using Data Envelopment Analysis (DEA) models to estimate efficiency in ports and airports, research on railway efficiency remains scarce. Most of the efficiency studies of railways assume that inputs and outputs are known with absolute precision. Here, we compare Stochastic-DEA and Fuzzy-DEA models to assess, respectively, how the underlying randomness and fuzziness impact efficiency levels in railway operations in six different Asian countries: Japan, Thailand, Vietnam, Malaysia, Myanmar, and Indonesia. Findings reveal that conclusions with respect to the ranking of these railways may vary substantially depending upon the type of model chosen, although efficiency scores are similar to some extent when compared within the ambits of Stochastic-DEA and Fuzzy-DEA models. Additionally, modeling choices on fuzziness, rather than randomness, appear to be the most critical source for variations in efficiency rankings.     

Anomaly detection via a Gaussian Mixture Model for flight operation and safety monitoring

Safety is key to civil aviation. To further improve its already respectable safety records, the airline industry is transitioning towards a proactive approach which anticipates and mitigates risks before incidents occur. This approach requires continuous monitoring and analysis of flight operations; however, modern aircraft systems have become increasingly complex to a degree that traditional analytical methods have reached their limits – the current methods in use can only detect ‘hazardous’ behaviors on a pre-defined list; they will miss important risks that are unlisted or unknown. This paper presents a novel approach to apply data mining in flight data analysis allowing airline safety experts to identify latent risks from daily operations without specifying what to look for in advance. In this approach, we apply a Gaussian Mixture Model (GMM) based clustering to digital flight data in order to detect flights with unusual data patterns. These flights may indicate an increased level of risks under the assumption that normal flights share common patterns, while anomalies do not. Safety experts can then review these flights in detail to identify risks, if any. Compared with other data-driven methods to monitor flight operations, this approach, referred to as ClusterAD-DataSample, can (1) better establish the norm by automatically recognizing multiple typical patterns of flight operations, and (2) pinpoint which part of a detected flight is abnormal. Evaluation of ClusterAD-DataSample was performed on two sets of A320 flight data of real-world airline operations; results showed that ClusterAD-DataSample was able to detect abnormal flights with elevated risks, which make it a promising tool for airline operators to identify early signs of safety degradation even if the criteria are unknown a priori.     

On the imputation of missing data for road traffic forecasting: New insights and novel techniques

Vehicle flow forecasting is of crucial importance for the management of road traffic in complex urban networks, as well as a useful input for route planning algorithms. In general traffic predictive models rely on data gathered by different types of sensors placed on roads, which occasionally produce faulty readings due to several causes, such as malfunctioning hardware or transmission errors. Filling in those gaps is relevant for constructing accurate forecasting models, a task which is engaged by diverse strategies, from a simple null value imputation to complex spatio-temporal context imputation models. This work elaborates on two machine learning approaches to update missing data with no gap length restrictions: a spatial context sensing model based on the information provided by surrounding sensors, and an automated clustering analysis tool that seeks optimal pattern clusters in order to impute values. Their performance is assessed and compared to other common techniques and different missing data generation models over real data captured from the city of Madrid (Spain). The newly presented methods are found to be fairly superior when portions of missing data are large or very abundant, as occurs in most practical cases.     

Distinguishing the land use effects of road pricing based on the urban form attributes

This paper studies the effects of road pricing on land use under different development scenarios (business as usual scenario and transit oriented development scenario) by a quantitative method, which combines the integrated land use and transport interaction model (TRANUS model) with the scenario-planning techniques. Moreover, in order to further analyze the differences of the land use effects of road pricing on traffic analysis zones (TAZs) with different urban form attributes, a quantitative classification method combining factor analysis and cluster analysis is then used to quantitatively classify TAZs. The results demonstrate that the effects of road pricing on the land use of a specific region depend on the urban form attributes of the region. The higher the densities of employments and population, and better street design (high densities of street and intersections) and public transportation condition, the less the region is negatively affected by road pricing, and vice versa. More importantly, rail transit can alleviate the negative impact of road pricing on commercial development and population concentration of the region. Therefore, before introducing a road pricing policy, it is necessary to develop public transport system, especially rail transit.     

Assessment of offshore structures under extreme wave conditions by Modified Endurance Wave Analysis

Recently, various approaches have been introduced to estimate the response of offshore structures in different sea states by stepwisely intensifying records. In this article, a more practical approach entitled Modified Endurance Wave Analysis (MEWA) considering the random and probabilistic nature of wave loading and utilizing optimal time duration is introduced. Generation procedure of this approach is described based on two practical wave theories: random and constrained new-wave. In addition, assessment of a simplified model representing a typical fixed offshore platform under extreme wave conditions in the Persian Gulf is performed making use of MEWA. A comparative analysis has been also carried out to investigate the accuracy and computational costs of MEWA. The results indicate that MEWA can be a time-saving and also reliable method both in design and assessment of offshore platforms.     

Traffic consolidation in East Asian container ports: A network flow analysis

The proliferation of hub-and-spoke operations in maritime container transportation has resulted in the widespread consolidation of traffic flows. Utilising liner shipping network configurations, this paper assesses the impact of freight traffic consolidation in the container port industry by exploring the spatial pattern of traffic flow movements and identifying the variety of roles that container ports play within this context. On the basis of the network concept, the spatial inequality of freight traffic consolidation is determined by the density and direction of all meaningful connections (i.e. significant flows) identified by applying Multiple Linkage Analysis (MLA) to an initial traffic flow matrix.The effectiveness of the chosen methodology is tested empirically using a sample comprising the 18 major container ports in East Asia, together with another 21 important container ports located on the East–West trading route. Based on this sample network, the spatial structure of traffic flow consolidation reveals the nature and structure of hub-and-spoke operations within a port system, the relative hub-dependence of ports, the variety of roles which individual ports play within the overall structure of inter-port interactions and the hierarchical configuration of the port industry structure. The paper concludes that MLA offers new insights into the distributional inequality of traffic flows, the spatial and economic interactions between ports and the extent to which hinterlands overlap. Furthermore, the analysis clearly shows that inter-port relationships can no longer be evaluated as isolated phenomena; any change in a specific port’s competitiveness will directly impact upon the structure of the whole maritime transportation system. Port authorities and terminal operators will need, therefore, to carefully analyse and disentangle specific inter-port relationships in order to provide the most appropriate basis for their decision making.