尼铁现代化项目中矩形空心桥台的优化设计

当前常用的时速200 km的铁路矩形空心桥台参考图中的桥台截面尺寸偏厚,安全富余量很大。为节约混凝土圬工量,希望在保证安全的前提下减少桥台截面尺寸。首先提出专门针对铁路矩形空心桥台台身截面计算的荷载图示,然后利用这一新的图示对拉伊铁路项目中使用的常规矩形空心桥台进行有限元分析,经过不断试算提出最优的截面尺寸。在钢筋用量基本不变的情况下,新尺寸桥台较原参考图中桥台的混凝土用量减少10%以上。对于项目所在地尼日利亚国水泥价格高昂的情况而言,桥台尺寸的这一优化将产生较大的经济效益。     

Fitness-for-purpose assessment of cracked offshore wind turbine monopile

In this paper, the procedure for flaw acceptability assessment is examined through a case study of a semi-elliptical surface crack in an offshore monopile as it grows till it forms a through thickness crack. Using the procedure prescribed in an industrial standard (BS 7910), the fracture ratio, K_r is shown to increase monotonically with increasing crack depth. The load ratio, L_r, is initially insensitive to the crack depth. However, there is a rapid increase in L_r when the crack depth to thickness ratio exceeds 80%. L_r values obtained from detailed 3D FE limit analysis using elastic-perfectly-plastic material behaviour do not exhibit the asymptotic behaviour predicted by BS 7910 as the flaw transitions from deep crack to through-thickness crack. Furthermore, K_r predicted by BS 7910 is shown to be an over-estimation for the typical dimensions of offshore monopiles. The findings suggest that a structure with a deep flaw may be identified as unacceptable based on BS 7910 when it may still possess a non-trivial amount of structural residual life. This is a concern for monopiles where crack growth as a large flaw forms a significant part of the total life.     

挂篮底模纵梁受力简化计算方法及承重分配系数

以一桥梁施工工程实例为依托,采用有限元软件ANSYS对桥梁悬臂施工中使用的菱形挂篮进行仿真分析.提出3种简化模式计算底模纵梁的承重分配,对比发现将底模纵梁承受的外荷载简化为集中荷载,并分配至挂篮下前后横梁的方法是最优简化计算方法.在此基础上对挂篮底模纵梁承重分配系数进行优化,从而简化了繁琐的挂篮底模纵梁受力分析计算,保证了挂篮结构设计和施工的安全.     

STX1(2)型驮背多功能运输车超偏载检测标准

分析了STX₁₍₂)型驮背多功能运输车对既有超偏载检测方法和标准的适应性,提出了该车的超偏载检测方案,明确了公路货车或半挂车超偏载的检测方法及评判标准。     

Tuning of transfer functions for analysis of wave–ship interactions

Transfer functions are often used together with a wave spectrum for analysis of wave–ship interactions, where one application addresses the prediction of wave-induced motions or other types of global responses. This paper presents a simple and practical method which can be used to tune the transfer function of such responses to facilitate improved prediction capability. The input to the method consists of a measured response, i.e. time series sequences from a given sensor, the 2D wave spectrum characterising the seaway in which the measurements are taken, and an initial estimate of the transfer function for the response in study. The paper presents results obtained using data from an in-service container ship. The 2D wave spectra are taken from the ERA5 database, while the transfer function is computed by a simple closed-form expression. The paper shows that the application of the tuned transfer function leads to predictions which are significantly improved compared to using the transfer function without tuning.     

基于ANN的船舶撞击高桩码头群桩损伤位置预测

本文采用有限元软件ABAQUS建立了船舶撞击高桩码头群桩的空间有限元模型。通过计算评估了撞击力、桩体刚度、撞击位置和撞击角度下对群桩结构损伤位置的影响。基于人工神经网络(ANN)方法,对不同参数组合下的群桩结构损伤位置进行了预测,并对ANN方法的可行性进行了评估。     

内压载荷下X80管道裂纹应力分布规律研究

为了研究内压作用下管道裂纹应力场分布规律,以含有表面裂纹的X80管道为研究对象,对不同形状、不同方向、不同内压、不同尺寸的含裂纹管道进行仿真分析和实验验证。结果表明:裂纹尖端处应力远大于裂纹中心应力。裂纹形状对应力影响作用较小,随着裂纹方向与管道轴向夹角增大,裂纹尖端应力先增大后减小,随着管道内压、裂纹深度、裂纹长度的增大,裂纹尖端处应力随之线性增大。其中,裂纹长度对裂纹尖端应力的影响小于管道内压和裂纹深度。     

An improved cellular automaton with axis information for microscopic traffic simulation

Cellular Automaton (CA), an efficient dynamic modeling method that is widely used in traffic engineering, is newly introduced for traffic load modeling. This modeling method significantly addresses the modest traffic loads for long-span bridges. It does, however, require improvement to calculate precise load effects. This paper proposed an improved cellular automaton with axis information, defined as the Multi-axle Single-cell Cellular Automaton (MSCA), for the precise micro-simulation of random traffic loads on bridges. Four main ingredients of lattice, cells’ states, neighborhoods and transition rules are redefined in MSCA to generate microscopic vehicle sequences with detailed vehicle axle positions, user-defined cell sizes and time steps. The simulation methodology of MSCA is then proposed. Finally, MSCA is carefully calibrated and validated using site-specific WIM data. The results indicate: (1) the relative errors (REs) for the traffic parameters, such as volumes, speeds, weights, and headways, from MSCA are basically no more than ±10% of those of WIM data; (2) the load effects of three typical influence lines (ILs) with varied lengths of 50, 200 and 1000 m are also confidently comparable, both of which validate the rationality and precision of MSCA. Furthermore, the accurate vehicle parameters and gaps generated from MSCA can be applied not only for precise traffic loading on infrastructures but also for the accurate estimation of vehicle dynamics and safety. Hence, wide application of MSCA can potentially be expected.     

Forecasting current and next trip purpose with social media data and Google Places

Trip purpose is crucial to travel behavior modeling and travel demand estimation for transportation planning and investment decisions. However, the spatial-temporal complexity of human activities makes the prediction of trip purpose a challenging problem. This research, an extension of work by Ermagun et al. (2017) and Meng et al. (2017), addresses the problem of predicting both current and next trip purposes with both Google Places and social media data. First, this paper implements a new approach to match points of interest (POIs) from the Google Places API with historical Twitter data. Therefore, the popularity of each POI can be obtained. Additionally, a Bayesian neural network (BNN) is employed to model the trip dependence on each individual’s daily trip chain and infer the trip purpose. Compared with traditional models, it is found that Google Places and Twitter information can greatly improve the overall accuracy of prediction for certain activities, including “EatOut”, “Personal”, “Recreation” and “Shopping”, but not for “Education” and “Transportation”. In addition, trip duration is found to be an important factor in inferring activity/trip purposes. Further, to address the computational challenge in the BNN, an elastic net is implemented for feature selection before the classification task. Our research can lead to three types of possible applications: activity-based travel demand modeling, survey labeling assistance, and online recommendations.