Inspection data of actual concrete structures should be analyzed to elucidate the deterioration mechanism and construct a regression model. Although machine learning can be applied to this problem, inspection data are not suitable because machine learning targets big data with a uniform density and a balanced distribution. This study applies machine learning to a regression model of the crack damage grade in concrete bridges, using imbalanced inspection data. The model performance is improved by analyzing the influence of undersampling. Undersampling is conducted step-wise, and the models are constructed by learning all the undersampled data. The cross-validation of these models yielded the regression errors on each crack damage grade to evaluate the model performance considering the bias of data imbalance. Based on the results, the effect of undersampling on the model performance is analyzed, and the appropriate model is selected. Additionally, the influence of the model difference on the evaluation is investigated via historical change or factor analysis to confirm the effect of undersampling. This article not only presents a case study of a regression task for crack damage grades in concrete bridges, but also describes a strategy to maximize the use of imbalanced data for regression problems.

应分析实际混凝土结构的检验数据，以阐明其劣化机理并构建回归模型。尽管可以将机器学习应用于此问题，但是检查数据不适合，因为机器学习的目标是密度均匀且分布均衡的大数据。这项研究使用不平衡的检查数据将机器学习应用于混凝土桥梁裂缝破坏等级的回归模型。通过分析欠采样的影响，可以提高模型性能。欠采样是逐步进行的，通过学习所有欠采样数据来构建模型。这些模型的交叉验证得出每个裂纹损伤等级的回归误差，以考虑数据不平衡的偏差来评估模型性能。根据结果​​，分析了欠采样对模型性能的影响，并选择了合适的模型。此外，还可以通过历史变化或因素分析研究模型差异对评估的影响，以确认欠采样的影响。本文不仅介绍了混凝土桥梁裂缝破坏等级的回归任务的案例研究，而且还介绍了一种最大限度地利用不平衡数据解决回归问题的策略。