Multiple factors, regarding mechanical properties, load levels, and environmental conditions, affect the fatigue lifetime of reinforced concrete (RC) bridge decks. For the mechanical behavior, previous research predicts the fatigue lifetime of RC decks as a function of their punching shear capacity, where they give less attention to other modes of failure due to experimental limitations of fatigue loading and the restriction of girders spacing in the past design practice. Nowadays, multi-scale simulation can deal with fatigue loading problems, which secures examining complex situations that cannot be easily reproduced in the laboratories of fatigue tests. In this study, various RC decks with wide range of dimensions, material properties, reinforcement ratio, and load levels are analyzed by the validated multi-scale simulation. Then, artificial neural network (ANN) based model is also proposed based on wide-range of studied cases, which estimates the fatigue life of newly constructed RC decks, where it can be the basis of performance-based design. After that, the impact of deck’s properties on fatigue life is evaluated based on the built ANN model, which matches the conceptual design of RC decks. Finally, coupling of an empirical equation and ANN model is proposed, which may support conceptual decision-making.

中文翻译：

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新建钢筋混凝土公路桥面疲劳寿命预测

机械性能、载荷水平和环境条件等多种因素会影响钢筋混凝土 (RC) 桥面板的疲劳寿命。对于力学行为，先前的研究预测 RC 桥面板的疲劳寿命是其冲剪能力的函数，由于疲劳载荷的实验限制和过去设计中梁间距的限制，他们对其他失效模式的关注较少实践。如今，多尺度模拟可以处理疲劳载荷问题，这确保了在疲劳试验实验室中无法轻易重现的复杂情况的检查。在这项研究中，通过经过验证的多尺度模拟分析了具有广泛尺寸、材料特性、钢筋比和载荷水平的各种 RC 甲板。然后，还基于广泛的研究案例提出了基于人工神经网络 (ANN) 的模型，该模型估计了新建 RC 甲板的疲劳寿命，可以作为基于性能的设计的基础。之后，基于建立的ANN模型评估了甲板特性对疲劳寿命的影响，该模型与RC甲板的概念设计相匹配。最后，提出了经验方程和人工神经网络模型的耦合，这可以支持概念决策。