高速公路路面不平度识别研究

摘要

In order to find the effect of vehicle dynamic load caused by road surface roughness on the pave-ment destruction on the highway,a 7 DOF vehicle vibration model was established,the vertical acceleration and pitching angular acceleration of vehicle body centroid were got,which were regarded as neural networks i-deal input sample,the corresponding road surface roughness was regarded as neural networks ideal output sam-ple.The level B and level C road surface roughness were identified.The maximum of relative error of level B and level C road surface was respectively 0.24% and 0.42%.The results show that the method has ideal iden-tification accuracy and better ability of anti-noise,the relative error on the level C road surface was greater than the level B road surface.The road surface roughness of the identification can provide a theoretical basis for analyzing the dynamic response of the freeway road surface.%为了发现高速公路上路面不平度产生的汽车动载对路面破坏影响，建立了7个自由度汽车振动模型，以得到车身质心垂直加速度和俯仰角加速度作为神经网络理想输入样本，路面不平度作为网络理想输出样本，识别了 B 级和 C 级路面不平度。识别的 B 级路面和 C 级路面相对误差的最大值分别是0．24％和0．42％。结果表明，该方法具有较理想的识别精度和较强的抗噪声能力，C 级路面不平度的相对误差比 B 级路面不平度的相对误差大。识别出来的路面不平度可为分析高速公路路面动力响应研究提供理论基础。