TY - JOUR
T1 - Reliability-based life-cycle cost design of asphalt pavement using artificial neural networks
AU - Xin, Jiyu
AU - Akiyama, Mitsuyoshi
AU - Frangopol, Dan M.
AU - Zhang, Mingyang
AU - Pei, Jianzhong
AU - Zhang, Jiupeng
N1 - Funding Information:
The support from the China Scholarship Council (CSC) is gratefully acknowledged during the research of the first author at Waseda University. The authors are grateful to the publicly accessible database provided by the LTPP program supported by FHWA.
Publisher Copyright:
© 2020 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2021
Y1 - 2021
N2 - It is widely recognized that reliability-based optimization methodologies are rational and promising tools to perform the life-cycle management (LCM) of civil engineering structures. In order to realize the optimal design and maintenance of asphalt pavement, a comprehensive reliability-based optimization methodology considering the life-cycle cost (LCC) is proposed in this paper. Considering the powerful ability of Artificial Neural Networks (ANNs) to solve complex and nonlinear problems, ANNs are implemented to predict the performance of asphalt pavement based on the training data (i.e. structural, traffic, climatic, and performance parameters) selected from the Long-Term Pavement Performance (LTPP) program. Monte Carlo simulation (MCS) with Importance Sampling (IS) is conducted based on the obtained ANNs model to calculate the life-cycle reliability of asphalt pavement. Finally, the expected LCC including the initial construction cost, the preventive maintenance cost, inspection cost, users cost, and salvage value is minimized while maintaining a prescribed life-cycle reliability level for asphalt pavement. Several applications are presented to investigate the effects of traffic and climatic parameters on reliability-based optimum cost solution.
AB - It is widely recognized that reliability-based optimization methodologies are rational and promising tools to perform the life-cycle management (LCM) of civil engineering structures. In order to realize the optimal design and maintenance of asphalt pavement, a comprehensive reliability-based optimization methodology considering the life-cycle cost (LCC) is proposed in this paper. Considering the powerful ability of Artificial Neural Networks (ANNs) to solve complex and nonlinear problems, ANNs are implemented to predict the performance of asphalt pavement based on the training data (i.e. structural, traffic, climatic, and performance parameters) selected from the Long-Term Pavement Performance (LTPP) program. Monte Carlo simulation (MCS) with Importance Sampling (IS) is conducted based on the obtained ANNs model to calculate the life-cycle reliability of asphalt pavement. Finally, the expected LCC including the initial construction cost, the preventive maintenance cost, inspection cost, users cost, and salvage value is minimized while maintaining a prescribed life-cycle reliability level for asphalt pavement. Several applications are presented to investigate the effects of traffic and climatic parameters on reliability-based optimum cost solution.
KW - Artificial neural networks
KW - Monte Carlo simulation
KW - asphalt pavement
KW - life-cycle cost
KW - long-term pavement performance program
KW - optimization
KW - reliability
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U2 - 10.1080/15732479.2020.1815807
DO - 10.1080/15732479.2020.1815807
M3 - Article
AN - SCOPUS:85089848515
VL - 17
SP - 872
EP - 886
JO - Structure and Infrastructure Engineering
JF - Structure and Infrastructure Engineering
SN - 1573-2479
IS - 6
ER -