Evaluation of accelerated deterioration in NAPTF flexible test pavements

Abstract：Previous research studies have successfully demonstrated the use of artificial neural network(ANN)models for predicting critical structural responses and layer moduli of highway flexible pavements.The primary objective of this study was to develop an ANN-based approach for backcalculation of pavement moduli based on heavy weight deflectometer(HWD)test data,especially in the analysis of airport flexible pavements subjected to new generation aircraft(NGA).Two medium-strength subgrade flexible test sections,at the National Airport Pavement Test Facility(NAPTF),were modeled using a finite element(FE) based pavement analysis program,which can consider the non-linear stress-dependent behavior of pavement geomaterials.A multi-layer,feed-forward network which uses an error-backpropagation algorithm was trained to approximate the HWD backcalculation function using the FE program generated synthetic database.At the NAPTF,test sections were subjected to Boeing 777 (B777)trafficking on one lane and Boeing 747(B747)trafficking on the other lane using a test machine.To monitor the effect of traffic and climatic variations on pavement structural responses.HWD tests were conducted on the traffieked lanes and on the untraffieked centerline of test sections as trafficking progressed.The trained ANN models were successfully applied on the actual HWD test data acquired at the NAPTF to predict the asphalt concrete moduli and non-1inear subgrade moduli of the medium-strength subgrade flexible test sections. 作者： Author： Kasthurirangan GOPALAKRISHNAN 作者單位： Dempartment of Civil,Construction and Environmental Engineering,Iowa State University,IA 50011,USA 期 刊： 浙大學(xué)報(bào)（英文版）（A輯：應(yīng)用物理和工程）   ISTICEISCI Journal： JOURNAL OF ZHEJIANG UNIVERSITY SCIENCE A 年，卷(期)： 2008, 9(9) 分類號(hào)： V21 Keywords： Airport flexible,pavements,Heavy weig htdefleetometer(HWD)    Artificial neural networks(ANN)    Elastic moduli    New generation aircraft(NGA)    機(jī)標(biāo)分類號(hào)： P53 TU6 機(jī)標(biāo)關(guān)鍵詞： artificial neural network    climatic variations    test data    asphalt concrete    finite element    test machine 基金項(xiàng)目：

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