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首页> 外文期刊>Advances in Engineering Software >Modeling deflection basin using artificial neural networks with cross-validation technique in backcalculating flexible pavement layer moduli
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Modeling deflection basin using artificial neural networks with cross-validation technique in backcalculating flexible pavement layer moduli

机译：使用交叉验证技术的人工神经网络对挠度盆建模，反算柔性路面层模量

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摘要

Through the new technological developments, for highway maintenance engineering the structural capacity of pavement is to be determined using non-destructive techniques. Up to now various methodologies have been applied based on the surface deflection bowl obtained under either a known moving wheel load or devices such as falling weight deflectometer. Backcalculating pavement layer moduli are well-accepted procedures in the evaluation of the structural capacity of pavements. The ultimate aim of the backcalculation process from non-destructive testing (NDT) results is to estimate the pavement material properties. Using backcalculation analysis, in situ material properties can be backcalculated by the measured field data for appropriate analysis techniques. To backcalculate reliable moduli, the deflection basin must be modeled more realistically.Here, in this study, the deflection basins measured on the surface of the flexible pavements are modeled using artificial neural networks (ANN) with cross-validation technique. Distances between transducers can be varied with different producer companies. The distances between transducer are used for the form deflection basin. Layer thickness and distance to loading center are used as input in the present study. Limited experimental deflection data groups from NDT are used to show the capability of the neural network technique in modeling the deflection bowl. Since enough data are not available to construct a reliable neural network, a methodology based on the cross-validation technique can be used. The results show that the proposed methodology give the deflection bowl satisfied accuracy.
机译：通过新技术的发展，对于公路养护工程，人行道的结构能力将使用无损技术来确定。迄今为止，基于在已知的动轮载荷或诸如落锤重量计的装置下获得的表面偏转碗，已经应用了各种方法。在计算路面结构能力时，反算路面层模量是公认的程序。无损检测（NDT）结果的反算过程的最终目的是估算路面材料的性能。使用反算分析，可以通过实地数据为适当的分析技术对原位材料特性进行反算。为了反算可靠的模量，必须对变形盆进行更实际的建模。在此研究中，使用交叉验证技术的人工神经网络（ANN）对在柔性路面表面测量的变形盆进行建模。换能器之间的距离可以随不同的生产商公司而变化。换能器之间的距离用于形变盆。在本研究中，层厚度和到加载中心的距离用作输入。来自NDT的有限的实验偏转数据组用于显示神经网络技术对偏转碗进行建模的能力。由于没有足够的数据来构建可靠的神经网络，因此可以使用基于交叉验证技术的方法。结果表明，所提出的方法能够使偏转碗达到满意的精度。

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