Probabilistic fatigue analysis of marine structures using the univariate dimension-reduction method

Probabilistic fatigue analysis of offshore structures requires the numerical simulation of a huge number of loading cases to compute the long-term multi-dimensional integral associated to the fatigue damage assessment. This paper proposes the implementation of the univariate dimension-reduction method developed by Rahman and Xu [1] in order to compute the long-term fatigue damage more efficiently. This method is particularly attractive because it reduces significantly the number of simulations by decomposing the N-dimensional integral associated to expected long-term fatigue damage assessment into the sum of N one-dimensional integrals. In addition, this paper compares the univariate-dimension reduction method with the brute force direct integration methodology and other methods based on Taylor expansions, such as perturbation approach and asymptotic expansion method discussed by Low and Cheung [2]. Two comprehensive examples are included to show the effectiveness of the method. At first, the performance of the univariate dimension-reduction method is evaluated by assessing the fatigue damage of a theoretical structure represented by a single stress response amplitude operator (RAO). Then, in order to show a case of practical application, the fatigue damage is evaluated for a Steel Lazy Wave Riser (SLWR) connected to an FPSO in a water depth of 2200 m.     

Probabilistic multi-aircraft conflict detection approach for trajectory-based operation

Conflict detection (CD) is one of the key functions used to ensure air transport safety and efficiency. In trajectory-based operation (TBO), aircraft are provided with more flexibility in en route trajectory planning and more responsibility for self-separation. The high flexibility in trajectory planning enables random changes in pilot intent, thus increasing the uncertainty in trajectory prediction and CD. This study proposes a novel probabilistic CD approach for TBO in which the uncertainty of pilot intent is taken into account by quantifying the aircraft reachable domain constrained by the flight plan. First, a probabilistic model for aircraft trajectory prediction is developed using the truncated Brownian bridge method. Based on this model, a novel conflict probability estimation method is developed. Finally, the performance of the proposed probabilistic CD approach is demonstrated through an illustrative air traffic scenario.     

Efficient missing data imputing for traffic flow by considering temporal and spatial dependence

The missing data problem remains as a difficulty in a diverse variety of transportation applications, e.g. traffic flow prediction and traffic pattern recognition. To solve this problem, numerous algorithms had been proposed in the last decade to impute the missed data. However, few existing studies had fully used the traffic flow information of neighboring detecting points to improve imputing performance. In this paper, probabilistic principle component analysis (PPCA) based imputing method, which had been proven to be one of the most effective imputing methods without using temporal or spatial dependence, is extended to utilize the information of multiple points. We systematically examine the potential benefits of multi-point data fusion and study the possible influence of measurement time lags. Tests indicate that the hidden temporal–spatial dependence is nonlinear and could be better retrieved by kernel probabilistic principle component analysis (KPPCA) based method rather than PPCA method. Comparison proves that imputing errors can be notably reduced, if temporal–spatial dependence has been appropriately considered.     

Sensitivity analysis of the probabilistic damage stability regulations for RoPax vessels

In the light of the newly developed harmonised probabilistic damage stability regulations, set to come into force in 2009, this article presents a systematic and thorough analysis of the sensitivity of the Attained Subdivision Index with reference to a wide range of related design parameters. The sensitivity of the probabilistic regulations was investigated for a typical large RoPax vessel, with variation of parameters, such as the number, positioning and local optimisation of transverse bulkheads; the presence and position of longitudinal bulkheads below the main vehicle deck; the presence of side casings; and the height of the main deck and double bottom. The effects of water on deck and of operational parameters (draught, centre of gravity and trim) were also investigated. The results of the study, presented in graphical form, can provide valuable assistance to the designer when determining subdivision characteristics at the very early stage of the design process, resulting in optimal, efficient and safe ships.     

Use of a probabilistic particle tracking model to simulate the Prestige oil spill

A probabilistic particle tracking model is used to simulate the oil dispersion after the Prestige wreckage. This oil spill constitutes a suitable benchmark to analyze the capabilities of a probabilistic model, since the time elapsed from wreckage to oil landing (12 days) is much longer than the reliability time associated with forecast winds, usually on the order of 3–4 days. The particle model can be run in two different modes: real time mode (when existing reliable wind fields for the event under scope) and in probabilistic mode (in absence of reliable wind fields but with historical fields corresponding to a similar period). The validity of the particle model is first evaluated in a hindcast way, running the Prestige case with the wind fields corresponding to the period November 19 to November 30, 2002, which were not available at the moment of the wreckage. Calculations show the accuracy of the model to provide the right impact point and timing. The probabilistic model is then used to simulate the same event by means of historical data. The region where the oil landed is shown to be the area with the highest probability to be impacted.     

Extending Time to Collision for probabilistic reasoning in general traffic scenarios

Vehicle-to-vehicle communication systems allow vehicles to share state information with one another to improve safety and efficiency of transportation networks. One of the key applications of such a system is in the prediction and avoidance of collisions between vehicles. If a method to do this is to succeed it must be robust to measurement uncertainty and to loss of communication links. The method should also be general enough that it does not rely on constraints on vehicle motion for the accuracy of its predictions. It should work for all interactions between vehicles and not just a select subset. This paper presents a method to calculate Time to Collision for unconstrained vehicle motion. This metric is gated using a novel technique based on relative vehicle motion that we call “looming”. Finally, these ideas are integrated into a probabilistic framework that accounts for uncertainty in vehicle state and loss of vehicle-to-vehicle communication. Together this work represents a new way of considering vehicle collision estimation. These algorithms are validated on data collected from real world vehicle trials.     

Reliability-based sensitivity analysis of vehicle components with non-normal distribution parameters

Techniques from the perturbation method, fourth moment method, reliability-based design theory, and sensitivity analysis approach are employed to present a practical and efficient method for testing the reliability sensitivity of vehicle components with non-normal distribution parameters. With the condition that the first four moments of original random variables are known, the reliability sensitivity theory and cases are researched using the presented numerical method. The variation regularities of reliability sensitivity are obtained and the effects of design parameters on reliability of the vehicle components are studied. The sophisticated formulation provided in this paper is easily amenable to computational procedures. The respective program can be used to obtain the reliability sensitivity of vehicle components with non-normal distribution parameters accurately and quickly. The results obtained are perfect and the solutions compared very well with those from Monte Carlo simulation. The method presents a theoretic basis for the reliability design of the vehicle components.     

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.     

基于人工神经网络的混合梁斜拉桥智能诊断方法研究

研究目的:本文以天津市河北大街混合梁斜拉桥为工程背景,基于人工神经网络模型,提出适用于混合梁斜拉桥的分步识别方法,分别采用概率和径向基函数神经网络对子结构和钢主梁子结构局部构件进行损伤识别.此外还提出适用于钢主梁局部构件识别的动-静组合损伤指标,并建立相应的径向基函数网络模型,分别针对单损伤、双损伤和三损伤的不同损伤情况进行数值模拟.研究结论:识别结果表明:(1)本文所提出的分步识别方法具有较高的识别精度,网络识别速度快,适用于大型混合梁斜拉桥的智能诊断过程;(2)所提出的动-静组合损伤指标对混合梁斜拉桥的局部损伤识别也较为敏感;(3)单处损伤测试工况中,识别精度几乎高达100％;(4)在两处和三处损伤测试工况中,位置识别正确率分别达到82.61％和78.3％.     

Application of advanced sampling for efficient probabilistic traffic modelling

In probabilistic traffic models, consideration of stochasticity in the dynamics of traffic gives a closer representation of a traffic system in comparison to that of a deterministic approach. Monte Carlo simulation is a broadly accepted method to consider variations in traffic within modelling. In this contribution, the possibility of increasing the efficiency of probabilistic traffic flow models using Monte Carlo simulation is analysed using variance reduction techniques and sequencing, for varied capacity and traffic demand values. The techniques of Importance Sampling, Latin Hypercube Sampling and Quasi-Random Sequencing are compared in a dynamic macroscopic traffic model to demonstrate the effectiveness of these techniques for reduction of the computational load when considering multiple input variations. Demonstration of their efficiency in traffic modelling is expected to lead to a wider application of the methods in practice.