Data dependent input control for origin–destination demand estimation using observability analysis

In this paper, we address the observability issue of static O–D estimation based on link counts. Unlike most classic observability analyses that relied only on network topological relationships, our analysis incorporates the actual values of input parameters, thus including network operational relations as well. We first analyze possible mathematical properties of an O–D estimation problem with different data input. We then propose a modeling approach based on mixed-integer program for selecting model input that ensures observability and estimation quality. Through establishing a stronger connection between observability analysis and the corresponding estimation problem, the proposed method aims to improve estimation quality while reducing reliance on erroneous data.     

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.     

Path-constrained traffic assignment: A trip chain analysis under range anxiety

This paper proposes and analyzes a distance-constrained traffic assignment problem with trip chains embedded in equilibrium network flows. The purpose of studying this problem is to develop an appropriate modeling tool for characterizing traffic flow patterns in emerging transportation networks that serve a massive adoption of plug-in electric vehicles. This need arises from the facts that electric vehicles suffer from the “range anxiety” issue caused by the unavailability or insufficiency of public electricity-charging infrastructures and the far-below-expectation battery capacity. It is suggested that if range anxiety makes any impact on travel behaviors, it more likely occurs on the trip chain level rather than the trip level, where a trip chain here is defined as a series of trips between two possible charging opportunities (Tamor et al., 2013). The focus of this paper is thus given to the development of the modeling and solution methods for the proposed traffic assignment problem. In this modeling paradigm, given that trip chains are the basic modeling unit for individual decision making, any traveler’s combined travel route and activity location choices under the distance limit results in a distance-constrained, node-sequenced shortest path problem. A cascading labeling algorithm is developed for this shortest path problem and embedded into a linear approximation framework for equilibrium network solutions. The numerical result derived from an illustrative example clearly shows the mechanism and magnitude of the distance limit and trip chain settings in reshaping network flows from the simple case characterized merely by user equilibrium.     

Robust weekly aircraft maintenance routing problem and the extension to the tail assignment problem

In this paper, we study two closely related airline planning problems: the robust weekly aircraft maintenance routing problem (RWAMRP) and the tail assignment problem (TAP). In real life operations, the RWAMRP solution is used in tactical planning whereas the TAP solution is implemented in operational planning. The main objective of these two problems is to minimize the total expected propagated delay (EPD) of the aircraft routes. To formulate the RWAMRP, we propose a novel weekly line-of-flights (LOF) network model that can handle complex and nonlinear cost functions of EPD. Because the number of LOFs grows exponentially with the number of flights to be scheduled, we propose a two-stage column generation approach to efficiently solve large-scale real-life RWAMRPs. Because the EPD of an LOF is highly nonlinear and can be very time-consuming to accurately compute, we propose three lower bounds on the EPD to solve the pricing subproblem of the column generation. Our approach is tested on eight real-life test instances. The computational results show that the proposed approach provides very tight LP relaxation (within 0.6% of optimal solutions) and solves the test case with more than 6000 flights per week in less than three hours. We also investigate the solutions obtained by our approach over 500 simulated realizations. The simulation results demonstrate that, in all eight test instances, our solutions result in less EPDs than those obtained from traditional methods. We then extend our model and solution approach to solve realistically simulated TAP instances.     

“s”型覆岩空间结构的冲击地压危险区预测

基于微地震监测和覆岩空间结构理论，以山东华丰矿1410工作面为例，分析了“s”型覆岩空间结构的形成，利用理论方法和微地震监测预测并圈定了华丰矿1410工作面发生冲击地压的危险区域，研究了“s”型覆岩空间结构的冲击地压危险区分布规律，为现场预测和控制冲击地压灾害提供了理论基础。     

轨道安全监测中传感器优化配置研究

为了研究轨道结构安全监测中传感器的合理配置方法,确保城市轨道交通安全运营,通过建立桥上交叉渡线道岔模型和钢轨应变传递误差最小的目标函数,提出传感器测点选择依据和监测方案评价标准,保证监测方案的经济性、合理性。结论表明:模型数据曲线可以指导监测系统的测点位置选择;随着传感器测点数量增加,监测误差逐渐减小,但在误差比率的"拐点"后增加单位个数传感器带来的收益降低;通过建立误差最小准则的目标函数,结合误差指标可以评价和优化传感器布设方案;钢轨位移、附加力等指标的监测方案也可以通过本文方法确定。     

新建隧道下穿多座隧道施工技术

以渝黔引入贵阳枢纽铁路龙宝冲隧道下穿6座已建及在建隧道工程为例,结合现场,详细分析、介绍了下穿隧道施工方案、原则、施工方法;提出了新建铁路隧道近距离下穿隧道交叉点附近施工爆破控制、防护方案,以及新建、既有隧道的监控量测、超前地质预报、安全性评估与评价等施工技术;解决了下穿隧道施工中既保证新建隧道施工安全,又保证既有隧道不受影响的问题。     

立交隧道相互作用三维数值模拟与实测对比研究

以温福铁路琯头岭隧道下穿同三高速公路隧道为例,研究新建下穿隧道与既有隧道工程之间相互影响的问题。采用ANSYS有限元软件,模拟上覆公路隧道和下穿铁路隧道的位移和应力变化情况,并与现场监控量测结果对比,得到以下结果:下穿隧道拱顶沉降量较小,水平收敛值较大,下穿隧道立交段拱腰和边墙是需要重点支护的对象;上覆隧道在立交段下沉值变大,变化幅度为仰拱边墙拱腰拱顶,隧道中部和下部水平收敛值变化较大;受下穿隧道开挖影响,立交隧道上覆地层受力产生不均匀变化,造成扭矩存在,地面水平位移整体呈现"8"字形,立交隧道的建设对地层产生了较大的转动扰动。     

极高地应力区隧道地质特征及围岩变形机制研究

以某在建高速铁路隧道作为工程实例,结合地质情况,从初始应力、岩体结构和地下水分析了影响围岩的因素。运用理论分析和现场监控量测反馈结果等综合方法,从监控量测数据时态曲线特征、方向性、累计变化量、变形速率、时效性、现场围岩变形情况、隧道施工影响、变形段变形差异性等方面系统分析了极高地应力区隧道围岩变形特征及机制,并得出如下主要结论:1)极高地应力区围岩应力释放有一定的过程,不同的围岩应力释放的速度可能不同,爆破对围岩产生扰动,将一定程度加速围岩应力的释放。2)围岩地质条件不同,变形规律会表现出一定的差异性。3)围岩的变形机制,层状围岩的变形破坏一般形成几个区域:破坏区,崩塌,滑动滑移,张裂、弯曲及折断。4)本隧道围岩变形特征主要是由极高地应力和岩体结构综合决定的,隧道初期支护变形情况一定程度上是隧道围岩变形特征的有效反映。