An Efficient Data Mining Technique for Structural Strength Monitoring System

Rao, Moram Vishnu Vardhana; Chaparala, Aparna

doi:10.18280/isi.260211

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…RS-SVM is used to classify the structures, and RS-ANN is used to predict the damage levels. This method applied on earthquake-damaged datasets, got the accuracy of 90% [34].…”

Section: Volume 11 2023mentioning

confidence: 99%

Monitor the Strength Status of Buildings Using Hybrid Machine Learning Technique

Chaparala²,

Jain

et al. 2023

IEEE Access

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Standard inspections of buildings are not always possible because of human flaws in prediction. Hence, we need more stable, scalable, and efficient automated processes. Structure Health Monitoring (SHM) is one of the automation systems for forecasting potential losses in building structures. This article suggested how to monitor the strength status of buildings by using Hybrid Machine Learning Technique (HMLT). HMLT contains two-hybrid procedures. One for identifying the most significant features in Dataset using Hybrid Feature Selection Method (HFSM). HFSM uses the combined features of Mutual information (MI) and Rough Set Theory (RST) for feature selection. Another method is optimized classifiers such as Support Vector Machine (SVM) and Artificial Neural Networks (ANN) are used for the classification and predicting the accuracy i.e. predicting the strength status of buildings. Now the proposed method is applied on Earthquake Damage Dataset (Gorkha Earthquake in April 2015). Training and 10-fold crossvalidation procedure pragmatic to features. Then the performance of proposed method has been evaluated using the F1-score and accuracy metrics and get 91% and 92% respectively. Finally, the result analysis demonstrates the importance of the proposed approach in predicting the status of the building strength.

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“…RS-SVM is used to classify the structures, and RS-ANN is used to predict the damage levels. This method applied on earthquake-damaged datasets, got the accuracy of 90% [34].…”

Section: Volume 11 2023mentioning

confidence: 99%

Monitor the Strength Status of Buildings Using Hybrid Machine Learning Technique

Chaparala²,

Jain

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…As opposed to earlier numerical integrators, the combination’s accuracy was improved. In contrast to fuzzy set methods, the rough set analysis uses internal knowledge and does not assume any prior models [ 24 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Novel ANOVA-Statistic-Reduced Deep Fully Connected Neural Network for the Damage Grade Prediction of Post-Earthquake Buildings

Preethaa

Devi

Rajendran

et al. 2023

Sensors

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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.

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An Efficient Data Mining Technique for Structural Strength Monitoring System

Cited by 2 publications

References 15 publications

Monitor the Strength Status of Buildings Using Hybrid Machine Learning Technique

Monitor the Strength Status of Buildings Using Hybrid Machine Learning Technique

Novel ANOVA-Statistic-Reduced Deep Fully Connected Neural Network for the Damage Grade Prediction of Post-Earthquake Buildings

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