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首页> 外文期刊>Structural and multidisciplinary optimization >Principal component analysis-based predictive modeling and optimization of permanent deformation in asphalt pavement: elimination of correlated inputs and extrapolation in modeling
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Principal component analysis-based predictive modeling and optimization of permanent deformation in asphalt pavement: elimination of correlated inputs and extrapolation in modeling

机译：基于主成分分析的沥青路面永久性变形的预测性建模与优化：消除相关输入和建模外推的

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摘要

Permanent deformation in asphalt pavement is a function of material properties, loading, environmental conditions, and structural design (e.g., thickness of pavement layers). Because of the large number of effective variables and their nonlinear interrelationships, it is not easy to develop a predictive model for permanent deformation. In this study, a laboratory database containing accumulated strain values (output) and material properties (inputs) from several asphalt pavements has been used to develop a predictive model for permanent deformation. We first show that the inputs are highly correlated, then principal component analysis (PCA) is used to compute a set of orthogonal pseudo-inputs. Two predictive models based on the pseudo-inputs were developed using linear regression analysis and artificial neural networks (ANN) and are compared using statistical analysis. Extrapolation using empirical predictive models is highly risky and discouraged by experienced practitioners, so to guard against extrapolation, a method is developed to determine an input hyper-space. The above-developed model, along with an n-dimensional hyper-space, provides sufficient information for supporting an optimization algorithm for finding the minimum accumulated strain. An asphalt pavement design with accumulated strain value of 1772 micro-strain (0.02 in. in a 10-in.-thick asphalt pavement layer) is obtained by solving the optimization problem and the design parameters meet flexible pavement design specifications. The proposed framework is able to generate accurate predictive models when the original inputs are highly correlated and able to map to an optimal point in the fitted input space.
机译：沥青路面的永久变形是材料性质，装载，环境条件和结构设计的函数（例如，路面层的厚度）。由于大量有效变量及其非线性相互关系，因此开发永久变形的预测模型并不容易。在本研究中，已经使用来自几个沥青路面的累积应变值（输出）和材料特性（输入）的实验室数据库，用于开发用于永久变形的预测模型。首先表明输入是高度相关的，然后使用主成分分析（PCA）来计算一组正交伪输入。使用线性回归分析和人工神经网络（ANN）开发了基于伪输入的两个预测模型，并使用统计分析进行比较。使用经验预测模型的外推是高度危险的，经验从业者冒险，所以要开发一种方法来确定输入超空间。上述模型以及N维超空间提供足够的信息，用于支持用于找到最小累积应变的优化算法。通过解决优化问题，设计参数符合柔性路面设计规范，获得累积应变值1772微菌株（0.02英寸在10英寸 - 厚沥青路面层中）的沥青路面设计。当原始输入高度相关并能够映射到拟合的输入空间中的最佳点时，所提出的框架能够生成准确的预测模型。

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