Due to limited budgets and professional manpower, predicting possible damage in advance is essential for supporting on-site bridge inspections. This study aims to predict the severity of damage on bridge decks considering the effects of traffic and weather. First, the authors obtained identification, structural, environmental, and inspection data of pre-stressed concrete I-type bridges from the Korean Bridge Management System, and the final dataset of 16,728 tuples and 53 variables was prepared. Next, correlation analysis was performed to remove redundant variables, and random forest identified important factors that caused the more serious condition of damage to the deck. A total of 32 variables were finally used to develop Deep Neural Networks to predict different types of deck damage. The developed model successfully predicted the occurrences of seven different types of damage to bridge decks, that is, linear cracking, map cracking, scaling, breakage, leakage, efflorescence, and corrosion of exposed rebar, with the average weighted F1 score of 91%. Environmental effects on prediction were also determined; for example, traffic, temperature, and precipitation increased the F1 score of linear cracking by 4%. This research was a pioneering attempt to develop a model that enables specific damage-level prediction using both statistics and artificial intelligence techniques.

由于预算和专业人员的限制，提前预测可能的损坏对于支持现场桥梁检查至关重要。这项研究旨在考虑交通和天气的影响，预测桥面甲板的损坏严重程度。首先，作者从韩国桥梁管理系统获得了预应力混凝土I型桥梁的识别，结构，环境和检查数据，并准备了最终的数据集16,728个元组和53个变量。接下来，进行相关分析以去除多余的变量，随机森林确定了导致甲板损坏更为严重的条件的重要因素。最终总共使用了32个变量来开发深度神经网络，以预测甲板损坏的不同类型。所开发的模型成功地预测了桥面板的七种不同类型的损坏的发生，即线性裂缝，地图裂缝，结垢，结垢，破损，渗漏，风化和裸露钢筋的腐蚀，平均加权F1分数为91％。还确定了环境对预测的影响；例如，交通，温度和降水使线性裂纹的F1分数提高了4％。这项研究是开发模型的开创性尝试，该模型可以使用统计数据和人工智能技术实现特定的损伤程度预测。例如，交通，温度和降水使线性裂纹的F1分数提高了4％。这项研究是开发模型的开创性尝试，该模型可以使用统计数据和人工智能技术实现特定的损伤程度预测。例如，交通，温度和降水使线性裂纹的F1分数提高了4％。这项研究是开发模型的开创性尝试，该模型可以使用统计数据和人工智能技术实现特定的损伤程度预测。