Lime stabilization is commonly used to improve weak natural subgrade in Illinois. ILLI-PAVEnonlinear finite element (FE) program was utilized in this study as an advanced pavement structural analysistool to quantify the improvement from the lime stabilized soil layer in conventional flexible pavements. UsingILLI-PAVE, many combinations of typical Illinois highway pavement layer thicknesses and material propertieswere analyzed to establish a database of surface deflections and critical pavement responses. Using this database,Artificial Neural Network (ANN) structural models were trained successfully as forward analysis tools, surrogateto ILLI-PAVE, to be used in the backcalculation of the Falling Weight Deflectometer data. The ANN modelpredictions were on the average within 1% of the ILLI-PAVE FE results. The developed ANN models weretherefore quite accurate for the rapid analyses of conventional flexible pavements built on lime stabilized soilsreplicating the ILLI-PAVE results.
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机译：石灰稳定通常用于改善伊利诺伊州弱自然路基。本研究中利用Illi-Pavenonlinear线性有限元（FE）程序作为先进的路面结构分析，从而量化常规柔性路面中石灰稳定的土壤层的改善。 umerceized ilderi-pave，典型的伊利诺伊州公路路面层厚度和材料性能的许多组合分析，建立了表面偏转和关键路面响应的数据库。使用此数据库，人工神经网络（ANN）结构模型被成功培训为前瞻性分析工具，SurrogateTo Illi-Paves，用于在下降重量偏转仪数据的后划分中。 Ann ModelPrictions在Illi-Pave Fe结果的1％内平均。开发的ANN模型无需快速分析石灰内置的常规柔性路面的快速分析稳定的土壤展示了Illi-Pale结果。
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