Deep Learning for Structural Health Monitoring

• Fundamentals of Artificial Intelligence and Deep Learning

• Applications of Deep Learning in Structural and Building Materials

• Model Training, Testing, and Validation Workflows

• Dataset Preparation, Processing, and Visualization Techniques

• Hands-on Training: Deep Learning Models for Structural Monitoring Data

• Frameworks and Libraries: Python, PyTorch, Keras, TensorFlow

• Google Colab

• Python Programming Environment

Convolutional Neural Networks (CNNs):
Detection and classification of structural cracks using image datasets.

• Introduction to Long Short-Term Memory (LSTM) Networks

• LSTM Architecture and Use Cases in Structural Engineering

• End-to-End Workflow: Training, Testing, and Validating LSTM Models

• Time Series Dataset Preparation and Visualization

• Practical Implementation on Real-World Structural Monitoring Data

• Frameworks and Libraries: Python, PyTorch, Keras, TensorFlow

• Google Colab

• Python Programming Environment

LSTM for Structural Dynamics:
Time-series prediction and vibration analysis using LSTM networks.

• Introduction to Generative AI: Concepts and Use Cases

• Overview of Internet of Things (IoT) in Smart Monitoring Systems

• Generative AI Model Development for Sensor Nodes and IoT Devices

• Training, Testing, and Validation of Generative AI on Time Series Data

• Dataset Handling and Visualization

• Frameworks and Libraries: Python, PyTorch, Keras, TensorFlow

• Google Colab

• Hugging Face Transformers

• Python Programming Environment

Generative AI for Edge Deployment:
Deploying generative AI models on IoT sensor nodes for predictive infrastructure monitoring.