The growing demand for a more efficient maintenance of concrete bridges requires a model that tracks the deterioration of each bridge based on inspection data. Although it has been expected that machine learning could be applied to this problem, inspection data sparsely distributed over time are not suitable for machine learning in contrast to the continuous big data usually targeted. This study applies machine learning to a regression model of crack formation and propagation using inspection data to confirm the applicability. It includes the selection of the optimal algorithm, development of the model based on a novel methodology, and factor analysis using the model. Accordingly, the model was constructed by Gaussian process regression and it could appropriately extract the differences in the progress of crack damage due to multiple influential factors. The results demonstrate the excellent applicability of machine learning even to sparse data.

中文翻译：

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机器学习在混凝土桥梁裂缝模型中的适用性

对更有效地维护混凝土桥梁的需求不断增长，因此需要一种基于检验数据来跟踪每座桥梁的劣化的模型。尽管已经预期可以将机器学习应用于此问题，但是与通常目标连续的大数据相比，随着时间的推移稀疏分布的检查数据不适合机器学习。这项研究使用检查数据将机器学习应用于裂纹形成和扩展的回归模型，以确认其适用性。它包括最佳算法的选择，基于新方法的模型开发以及使用该模型的因子分析。因此，该模型是通过高斯过程回归法构建的，可以适当地提取由于多种影响因素导致的裂纹破坏进程的差异。结果表明，即使是稀疏数据，机器学习也具有出色的适用性。