基于多元胞模型的桥梁车流合成及荷载模拟

Traffic Flow Generation and Load Simulation of Bridges Based on Multicell Cellular Automaton

摘要:

随机车辆荷载模拟是桥梁荷载评定和性能评估的基础和关键，目前基于Monte Carlo的车流模拟较难呈现长加载跨径内车流的微观动态演化特性，而基于元胞自动机(CA)的单元胞模拟方法在加载精度方面仍需改进.单元胞模拟法中，将每个车辆用一个元胞模拟，难以体现轴重影响，限制了模拟精度的提高.在此基础上，提出了每个元胞模拟一个车轴，多个元胞模拟一辆车的多元胞自动机(MCA)模型，并利用实测车流及其荷载数据进行模型校核.研究表明：多元胞车轴模拟方法能够准确地还原车流量、车头时距、车速、车重等系列车流以及荷载参数.以1 000 m虚拟加载跨径的荷载集度、支点剪力、跨中弯矩为指标，统计模拟车流和实测数据的荷载响应特性.结果显示，MCA模型的模拟结果与实际车流误差均在5%以内，相较于传统单元胞模型17%的误差，精度显著提高.

Abstract:

The simulation of random traffic load is the foundation and crux of load assessment and performance evaluation of bridges. The current traffic simulation based on the Monte Carlo method is inappropriate for describing the dynamic evolution of traffic on microscopic scales within a relatively long span, while the singlecell method based on cellular automaton (CA) remains to be improved concerning precision. In the singlecell method, a car is simulated by a single cell, which could not consider the influence of axle load and exerts a limit on enhancement of simulation precision. In this paper, based on the current CA, a model is proposed to simulate an axle by a cell and to simulate a car by a number of consecutive cells, which is defined as multicell cellular automaton (MCA). It verifies the mathematical model with realistic traffic and its load data. It is demonstrated that the parameters of traffic flow and traffic load, such as traffic volume, time headway, velocity and weight of simulated traffic are reproduced accurately by the multicell method. With the distribution load, bearing shear and midspan moment in a simply supported girder with a length of 1 000 meters set as indexes respectively, the statistics analysis of the load response characteristics of simulated and realistic data is conducted. The result indicates that the relative errors of simulated data of the MCA model to realistic data are within 5%. When MCA is applied, the precision of load response can be increased from the correlated error of less than 17% in the traditional singlecell method to a relatively high precision.