Sri Preethaa K R, Munisamy Shyamala Devi, Rajendran Aruna, Muthuramalingam Akila, Natarajan Yuvaraj, Yusuf Ali Ahmed Abdi
Department of Robot and Smart System Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea.
Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, India.
Sensors (Basel). 2023 Jul 16;23(14):6439. doi: 10.3390/s23146439.
Earthquakes are cataclysmic events that can harm structures and human existence. The estimation of seismic damage to buildings remains a challenging task due to several environmental uncertainties. The damage grade categorization of a building takes a significant amount of time and work. The early analysis of the damage rate of concrete building structures is essential for addressing the need to repair and avoid accidents. With this motivation, an ANOVA-Statistic-Reduced Deep Fully Connected Neural Network (ASR-DFCNN) model is proposed that can grade damages accurately by considering significant damage features. A dataset containing 26 attributes from 762,106 damaged buildings was used for the model building. This work focused on analyzing the importance of feature selection and enhancing the accuracy of damage grade categorization. Initially, a dataset without primary feature selection was utilized for damage grade categorization using various machine learning (ML) classifiers, and the performance was recorded. Secondly, ANOVA was applied to the original dataset to eliminate the insignificant attributes for determining the damage grade. The selected features were subjected to 10-component principal component analysis (PCA) to scrutinize the top-ten-ranked significant features that contributed to grading the building damage. The 10-component ANOVA PCA-reduced (ASR) dataset was applied to the classifiers for damage grade prediction. The results showed that the Bagging classifier with the reduced dataset produced the greatest accuracy of 83% among all the classifiers considering an 80:20 ratio of data for the training and testing phases. To enhance the performance of prediction, a deep fully connected convolutional neural network (DFCNN) was implemented with a reduced dataset (ASR). The proposed ASR-DFCNN model was designed with the sequential keras model with four dense layers, with the first three dense layers fitted with the ReLU activation function and the final dense layer fitted with a tanh activation function with a dropout of 0.2. The ASR-DFCNN model was compiled with a NADAM optimizer with the weight decay of L2 regularization. The damage grade categorization performance of the ASR-DFCNN model was compared with that of other ML classifiers using precision, recall, F-Scores, and accuracy values. From the results, it is evident that the ASR-DFCNN model performance was better, with 98% accuracy.
地震是具有灾难性的事件，会对建筑物和人类生命造成损害。由于存在若干环境不确定性因素，对建筑物地震损害的评估仍然是一项具有挑战性的任务。建筑物的损害等级分类需要耗费大量的时间和精力。对混凝土建筑结构的损害率进行早期分析对于满足修复需求和避免事故至关重要。出于这一动机，提出了一种方差分析 - 统计量 - 降维深度全连接神经网络（ASR - DFCNN）模型，该模型可以通过考虑显著的损害特征来准确地对损害进行分级。一个包含来自762,106座受损建筑物的26个属性的数据集被用于模型构建。这项工作着重于分析特征选择的重要性并提高损害等级分类的准确性。最初，使用一个未进行主要特征选择的数据集，通过各种机器学习（ML）分类器进行损害等级分类，并记录其性能。其次，对方差分析应用于原始数据集，以消除对于确定损害等级而言不重要的属性。对所选特征进行10分量主成分分析（PCA），以审查对建筑物损害分级有贡献的排名前十的显著特征。将10分量方差分析PCA降维（ASR）数据集应用于分类器进行损害等级预测。结果表明，在所有分类器中，对于训练和测试阶段采用80:20的数据比例，使用降维数据集的Bagging分类器产生了最高的准确率，为83%。为了提高预测性能，使用降维数据集（ASR）实现了一个深度全连接卷积神经网络（DFCNN）。所提出的ASR - DFCNN模型采用顺序Keras模型设计，具有四个全连接层，前三个全连接层配备ReLU激活函数，最后一个全连接层配备tanh激活函数，且有0.2的随机失活率。ASR - DFCNN模型使用带有L2正则化权重衰减的NADAM优化器进行编译。使用精确率、召回率、F值和准确率值，将ASR - DFCNN模型的损害等级分类性能与其他ML分类器的性能进行了比较。从结果可以明显看出，ASR - DFCNN模型的性能更好，准确率为98%。
