Abstract

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.

摘要

众所周知，基于可靠性的优化方法是执行土木工程结构生命周期管理（LCM）的合理且有前途的工具。为了实现沥青路面的优化设计和养护，本文提出了一种综合的基于寿命周期成本（LCC）的基于可靠性的优化方法。考虑到人工神经网络（ANN）能够解决复杂和非线性问题的强大功能，我们采用从人工神经网络（Long-Long）中选择的训练数据（即结构，交通，气候和性能参数）来预测沥青路面的性能。术语路面性能（LTPP）程序。基于获得的ANNs模型，进行了带有重要采样（IS）的蒙特卡洛模拟（MCS），以计算沥青路面的生命周期可靠性。最后，在保持规定的沥青路面生命周期可靠性水平的同时，将包括初始施工成本，预防性维护成本，检查成本，用户成本和残值的预期LCC降至最低。提出了几个应用程序来研究交通和气候参数对基于可靠性的最佳成本解决方案的影响。