基于ABAQUS的水泥路面脱空形态神经网络识别方法
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摘要
为了能够准确识别板底脱空形态,并给出注浆工程量的计算依据,提出一种基于ABAQUS的神经网络方法。利用ABAQUS有限元软件模拟不同脱空形态路面板在荷载激励下的声学信号并提取声学特征值;通过BP神经网络构建多指标声学特征与脱空形态指标的关系模型;由实测声学信号提取脱空区域的多指标声学特征值,并进行路面板底脱空形态指标的预测。结果表明,建立的神经网络模型能够比较准确地识别板底脱空形态指标,可以为水泥混凝土路面板底脱空处治工程量的预测提供有效手段和依据。
In order to accurately identify the form of floor void and provide the basis for calculating grouting quantity,a neural network method based on ABAQUS was proposed.ABAQUS finite element software was used to simulate the acoustic signals of different void forms of pavement slabs under load excitation and extract the acoustic characteristic values;the relationship model between multi-index acoustic characteristics and void morphology index was constructed by neural network;the acoustic characteristic values of void areas were extracted from the measured acoustic signals,and the void morphology index of pavement slab bottom was predicted.The results show that the established neural network model can accurately identify the form index of floor void,and can provide effective means and basis for the prediction of the amount of treatment of floor void of cement concrete pavement.
In order to accurately identify the form of floor void and provide the basis for calculating grouting quantity,a neural network method based on ABAQUS was proposed.ABAQUS finite element software was used to simulate the acoustic signals of different void forms of pavement slabs under load excitation and extract the acoustic characteristic values;the relationship model between multi-index acoustic characteristics and void morphology index was constructed by neural network;the acoustic characteristic values of void areas were extracted from the measured acoustic signals,and the void morphology index of pavement slab bottom was predicted.The results show that the established neural network model can accurately identify the form index of floor void,and can provide effective means and basis for the prediction of the amount of treatment of floor void of cement concrete pavement.
引文
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[2]刘永江,王选仓,顾迎春,等.基于唧泥高度的水泥混凝土板底脱空评价方法研究[J].公路,2008(7):140-143.
[3]赵军,唐伯明,谈至明,等.基于弯沉指数的水泥混凝土路面板角脱空识别[J].同济大学学报,2006,34(3):335-339.
[4]葛如冰,孟凡强.探地雷达检测路面脱空大小的模型试验[J].物探与化探,2009,33(5):599-602.
[5]彭永恒,张肖宁,罗跃纲.基于频率下降率的刚性路面脱空自适应神经网络识别研究[J].公路,2004(2):50-53.
[6]彭永恒,陈静云,潘宝峰,等.声振法对刚性路面板脱空状况检测应用的研究[J].公路交通科技,2005,22(3):54-57.
[7]彭永恒,李想.基于板底脱空的水泥路面动力特性分析[J].黑龙江大学自然科学学报,2016,33(2):244-250.
[8]史春风.声振法在混凝土路面脱空检测中的应用[D].长沙:中南大学,2011.
[9]林小平,凌建明,官盛飞,等.水泥路面路基应力水平分析[J].同济大学学报,2010,38(4):545-551.
[10]朱武辉.高速公路路面结构连续性检测仪的研究[D].西安:长安大学,2013.
[11]陈祺.基于GA-BP神经网络的高校实验室安全评价研究[D].厦门:厦门大学,2014.
[12]尹豪,王志雄,王蓝英.基于BP神经网络的沥青混合料水稳定性预测模型[J].筑路机械与施工机械化,2017,34(10):63-67.
[13]王骑,韩西,易志坚.基于瞬态冲击响应的混凝土路面板脱空识别[J].西南交通大学学报,2010,45(5):718-724.
[14]陈元.基于BP神经网络的语音识别技术研究[D].昆明:云南大学,2015.
[15]曾胜,曾小军,许佳.基于弯沉比的水泥混凝土路面板底脱空识别方法[J].长沙理工大学学报,2008,5(2):14-19.
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