以位移为基础的钢筋混凝土连续梁桥抗震设计方法

利用等效线性化方法将钢筋混凝土(RC)连续梁桥结构简化为多自由度线弹性体系,采用振型反应谱的概念研究了结构在横向地震作用下考虑多阶模态效应的直接基于位移的抗震设计方法。探讨了连续梁桥的上部结构(主梁)及桥墩(台)刚度的变化对结构横向振动模态质量与模态周期的影响,给出了多阶模态设计方法的具体设计过程。对对称与非对称连续梁桥采用相同的设计步骤进行基于位移的抗震设计,并对设计算例用非线性时程分析验证了设计结果的合理性。     

大断面地铁车站隧道软弱围岩渐进破坏规律模型试验研究

以贵阳市地铁2号线阳明祠车站为背景,采用室内模型试验模拟大断面地铁车站施工过程中隧道塌方破坏过程,明确施工期间大断面隧道塌方破坏过程机制,对比分析围岩和路面的变形。结果表明:通过围岩重力作用模拟隧道施工过程中塌方过程,与实际塌方过程基本吻合,弥补了常规加载破坏的不足;围岩渐进破坏过程表现为裂隙出现-裂隙发展-裂隙贯通-围岩塌方,支护渐进破坏过程表现为变形缓慢增加-变形快速增加-裂缝快速发展-支护破坏;围岩渐进破坏与支护渐进破坏相互作用,共同发展;在实际施工过程中,当支护变形大幅增加时,应增加支护强度,同时还应及时注浆、打设长锚杆,以减缓围岩裂隙发展,阻断围岩渐进破坏过程。     

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。     

软弱富水地层浅埋暗挖隧道地表沉降和支护结构受力的模型试验

以莞惠城际轨道交通工程项目GZH-5标段中DK32+300~DK32+927.303段为背景,通过2组模型试验,其中一组无超前注浆,另一组进行超前注浆,研究软弱富水地层浅埋暗挖隧道地表沉降和支护结构受力规律及超前注浆对其受力变形造成的影响,最后,针对软弱富水地层浅埋暗挖隧道施工,提出几点隧道工程设计和施工的相关建议。     

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.     

Driving cycle prediction model based on bus route features

Bus fuel economy is deeply influenced by the driving cycles, which vary for different route conditions. Buses optimized for a standard driving cycle are not necessarily suitable for actual driving conditions, and, therefore, it is critical to predict the driving cycles based on the route conditions. To conveniently predict representative driving cycles of special bus routes, this paper proposed a prediction model based on bus route features, which supports bus optimization. The relations between 27 inter-station characteristics and bus fuel economy were analyzed. According to the analysis, five inter-station route characteristics were abstracted to represent the bus route features, and four inter-station driving characteristics were abstracted to represent the driving cycle features between bus stations. Inter-station driving characteristic equations were established based on the multiple linear regression, reflecting the linear relationships between the five inter-station route characteristics and the four inter-station driving characteristics. Using kinematic segment classification, a basic driving cycle database was established, including 4704 different transmission matrices. Based on the inter-station driving characteristic equations and the basic driving cycle database, the driving cycle prediction model was developed, generating drive cycles by the iterative Markov chain for the assigned bus lines. The model was finally validated by more than 2 years of acquired data. The experimental results show that the predicted driving cycle is consistent with the historical average velocity profile, and the prediction similarity is 78.69%. The proposed model can be an effective way for the driving cycle prediction of bus routes.     

A hybrid deep learning based traffic flow prediction method and its understanding

Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic flow with big data. While existing DNN models can provide better performance than shallow models, it is still an open issue of making full use of spatial-temporal characteristics of the traffic flow to improve their performance. In addition, our understanding of them on traffic data remains limited. This paper proposes a DNN based traffic flow prediction model (DNN-BTF) to improve the prediction accuracy. The DNN-BTF model makes full use of weekly/daily periodicity and spatial-temporal characteristics of traffic flow. Inspired by recent work in machine learning, an attention based model was introduced that automatically learns to determine the importance of past traffic flow. The convolutional neural network was also used to mine the spatial features and the recurrent neural network to mine the temporal features of traffic flow. We also showed through visualization how DNN-BTF model understands traffic flow data and presents a challenge to conventional thinking about neural networks in the transportation field that neural networks is purely a “black-box” model. Data from open-access database PeMS was used to validate the proposed DNN-BTF model on a long-term horizon prediction task. Experimental results demonstrated that our method outperforms the state-of-the-art approaches.     

浅谈综合工费对铁路隧道机械化施工经济性影响

为分析研究综合工费对大型机械钻爆法施工概算费用的影响,以3条新建双线铁路隧道项目数据为依托,核算出不同施工模式下的经济指标及工程费用;建立基期综合工费单价理论计算模型,计算出不同施工模式、不同围岩等级条件下新建铁路双线隧道临界综合工费单价。通过与普通钻爆法对比,大型机械钻爆法施工建造费用增长约10%;当基期综合工费高于226. 54元/工日时,大型机械钻爆法施工的经济性得以体现。