当前位置:
X-MOL 学术
›
Autom. Constr.
›
论文详情
Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
AI-driven computer vision-based automated repair activity identification for seismically damaged RC columns
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-08 , DOI: 10.1016/j.autcon.2024.105959
Samira Azhari, Sara Jamshidian, Mohammadjavad Hamidia
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-08 , DOI: 10.1016/j.autcon.2024.105959
Samira Azhari, Sara Jamshidian, Mohammadjavad Hamidia
Manual visual inspection is the conventional method for post-earthquake damage assessment, which is unsafe, subjective, and prone to human error. This paper presents an automated rapid and non-contact seismic damage state prediction methodology for reinforced concrete columns using crack image analysis. For surface damage quantification, three features of crack texture complexity including percolation, heterogeneity, and Renyi entropy-based dimensions are measured. Various shallow- and deep-learning-rooted algorithms are trained using a large collected experimental database to develop FEMA P-58-compliant repair activity predictive models. Based on the structural parameters, geometric features, and image-extracted indices, 10 groups of input features are defined. For the overfitting assessment and generalizability evaluation of models, five-fold cross-validations are conducted. Among shallow learning-based algorithms, CatBoost algorithm performs best for the scenarios that rely on vision-derived intricacy indices. Using the deep learning-based multilayer perceptron model as a feedforward artificial neural network, 92 % accuracy is achieved for the testing dataset.
中文翻译:
基于 AI 驱动的基于计算机视觉的自动修复活动识别,用于地震损坏的 RC 柱
人工目视检查是震后损失评估的常规方法,不安全、主观且容易出现人为错误。本文提出了一种使用裂缝图像分析的钢筋混凝土柱自动快速、非接触式地震损伤状态预测方法。对于表面损伤量化,测量了裂纹织构复杂性的三个特征,包括渗流、异质性和基于仁义熵的维度。使用收集的大型实验数据库训练各种浅层和深层学习根的算法,以开发符合 FEMA P-58 标准的修复活动预测模型。根据结构参数、几何特征和图像提取的索引,定义了 10 组输入特征。对于模型的过拟合评估和泛化性评估,进行了五重交叉验证。在基于浅层学习的算法中,CatBoost 算法在依赖视觉衍生复杂性指数的场景下表现最佳。使用基于深度学习的多层感知器模型作为前馈人工神经网络,测试数据集的准确率达到 92%。
更新日期:2025-01-08
中文翻译:

基于 AI 驱动的基于计算机视觉的自动修复活动识别,用于地震损坏的 RC 柱
人工目视检查是震后损失评估的常规方法,不安全、主观且容易出现人为错误。本文提出了一种使用裂缝图像分析的钢筋混凝土柱自动快速、非接触式地震损伤状态预测方法。对于表面损伤量化,测量了裂纹织构复杂性的三个特征,包括渗流、异质性和基于仁义熵的维度。使用收集的大型实验数据库训练各种浅层和深层学习根的算法,以开发符合 FEMA P-58 标准的修复活动预测模型。根据结构参数、几何特征和图像提取的索引,定义了 10 组输入特征。对于模型的过拟合评估和泛化性评估,进行了五重交叉验证。在基于浅层学习的算法中,CatBoost 算法在依赖视觉衍生复杂性指数的场景下表现最佳。使用基于深度学习的多层感知器模型作为前馈人工神经网络,测试数据集的准确率达到 92%。