A Machine Learning-based Damage Prediction Techniques for Structural Health Monitoring

• Published in: Turkish journal of computer and mathematics education Vol. 12; no. 2; pp. 3392 - 3405
• Main Authors: Rao, M Vishnu Vardhana, Chaparala, Aparna
• Format: Journal Article
• Language: English
• Published: Trabzon Karadeniz Technical University Distance Education Research and Application Center 10.04.2021
• Subjects: Artificial Intelligence; Artificial neural networks; Buildings; Classification; Cyclones; Damage detection; Earthquake damage; Heuristic methods; Learning theory; Machine learning; Natural disasters; Neural networks; Sensors; Set theory; Structural health monitoring; Support vector machines; Vibration analysis
• ISSN: 1309-4653; 1309-4653
• DOI: 10.17762/turcomat.v12i2.2401

Nowadays, the Structural Building Health Damage Monitoring System (SBHDMS) is a crucial technology for predicting the civil building structures' health. SBHDMS contains abnormal changes in the buildings in terms of damage levels. Natural Disasters like Earthquakes, Floods, and cyclones affect the unusual changes in the buildings. If the building undergoes any natural disaster, the sensors capture the vibration data or change the buildings' structure. Due to the vibration data, these unusual changes can be analyzed. Here sensors or Machine Learning based Building Damage Prediction (MLBDP) are used for capturing and collecting the vibration data. This paper proposes a Novel Rough Set based Artificial Neural Network with Support Vector Machine (RAS) metaheuristic method. RAS method is used to predict the damaged building's vibration data levels captured by the sensors. For the feature reduction subset, we use one of the essential pre-processing method called the Rough set theory (RST) strategy. RAS has two contributions. The first one is the Support Vector Machine (SVM) classification method used for identifying the structures of the buildings. The artificial Neural Network (ANN) method used to predict the buildings' damage levels is the second contribution. The proposed method (RAS) is accurately predicting the conditions of the construction building structure and predicting the damage levels, without human intervention. Comparing the results states that the proposed method accuracy is better than SVM's classification methods, ANN. The prediction analysis depicts that the RAS method can effectively detect the damage levels.