Natural hazards like earthquakes, high winds and floods are critical to the life and operability of the built structures. A multi-hazard resilient design framework is presented using smart engineering systems, IoT-based SHM and CNN-LSTM based hybrid deep learning model for detecting real-time damage and early warning. Finite element method simulation was conducted by developing G+10 RC building structure and subjecting it to the dynamic earthquake, wind and flood actions. The framework showed improved performance over conventional structure design: 31.69% reduction in earthquake induced displacement, 32.93% reduction in wind induced roof displacement and 41.63% reduction in foundation settlement. IoT based SHM exhibited a good sensor accuracy of 98.42%, data transmission rate accuracy of 99.13% and alert generation accuracy of 97.86%. The hybrid CNN- LSTM model produced a very good damage prediction accuracy of 97.84%, which indicates anomaly identification and quick decision making.
Multi-hazard resilient design, CNN-LSTM, SHM, IoT.
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