Quantitative Deterioration Assessment of Road Bridge Decks Based on Site Inspected Cracks
Quantitative Deterioration Assessment of Road Bridge Decks Based on Site Inspected Cracks
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DOI：
10.3390/app8071197
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发表时间：
2018-07
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影响因子：
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通讯作者：
E. Fathalla;Yasushi Tanaka;K. Maekawa;A. Sakurai
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文献类型：
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作者：
E. Fathalla;Yasushi Tanaka;K. Maekawa;A. Sakurai
通过将多尺度模拟与虚拟开裂方法相结合，可以根据现场检测的裂缝模式估计在役钢筋混凝土（RC）桥面板的剩余疲劳寿命。但是，它仍然需要时间进行计算。为了实现一个快速的恶化程度评估的RC甲板上的裂缝模式的基础上，提出了两种评估方法。剩余疲劳寿命和裂纹密度（裂纹长度和宽度）之间的预测相关性作为一个快速判断。对于公平详细的判断，人工神经网络（ANN）模型也引入了机器学习的基础。由于现场真实的裂缝模式的多样性或多或少受到限制，这两种评估方法通常都是通过成千上万的人工随机裂缝模式来覆盖所有可能的范围。所建立的神经网络的性能进行检查，除了检查的真实的裂纹模式的桥梁钢筋混凝土桥面的预测精度的k折交叉验证。最后，危险地图的甲板的底面，以指示较高的风险开裂的位置，这来自于估计的权重的单个神经元在所建立的人工神经网络。
By integrating a multi-scale simulation with the pseudo-cracking method, the remaining fatigue life of in-service reinforced concrete (RC) bridge decks can be estimated based upon their site-inspected crack patterns. But, it still takes time for computation. In order to achieve a quick deterioration-magnitude assessment of RC decks based upon their crack patterns, two evaluation methods are proposed. A predictive correlation between the remaining fatigue life and the cracks density (both cracks length and width) is presented as a fast judgment. For fair-detailed judgment, an artificial neural network (ANN) model is also introduced which is the basis of the machine learning. Both assessment methods are built commonly by thousands of artificial random crack patterns to cover all possible ranges since the variety of the real crack patterns on site is more or less limited. The built ANN performances are examined by k-fold cross-validation besides checking the prediction accuracy of real crack patterns of bridge RC decks. Finally, the hazard map of the deck’s bottom surface is introduced to indicate the location of higher risk cracking, which derives from the estimated weight of individual neuron in the built artificial neural network.