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What is High-Accuracy Damage Assessment in MSCSS about?

This study presents a stacking ensemble-based multi-channel CNN strategy for damage assessment in Mega-Sub Controlled Structures (MSCSS) to improve accuracy during seismic events. It develops a finite element model to analyze damage mechanisms and utilizes a convolutional neural network framework to classify damage features from structural responses, achieving over 98% accuracy even with imbalanced datasets. The research highlights the importance of effective structural health monitoring and proposes method

Author

Salisu Borodo

Language

EN