Deep Learning for Structural Health Monitoring

Deep Learning for Structural Health Monitoring is a cutting-edge international workshop that explores how AI—particularly convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers—can be applied to monitor the health and integrity of physical structures.

Participants will learn to work with sensor data, vibration signals, thermal imagery, and inspection footage to build deep learning models for crack detection, damage localization, fatigue prediction, and condition classification. The workshop bridges the fields of structural engineering, machine learning, and IoT, offering rich insights and practical tools for researchers and industry professionals alike.

• Bridge the knowledge gap between AI and structural/mechanical engineering

• Teach deep learning architectures tailored to SHM datasets

• Foster innovation in safe, automated, and scalable monitoring tools

• Enable participants to prototype real-world solutions for infrastructure safety

• Promote AI integration in regulatory, public safety, and industrial maintenance practices

📅 Day 1: Deep Learning Foundations for Structural Materials

Topics Covered

• 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

Tools & Platforms

• Google Colab

• Python Programming Environment

Capstone Project

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

📅 Day 2: Time Series Modeling with LSTM in Structural Applications

Topics Covered

• 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

Tools & Platforms

• Google Colab

• Python Programming Environment

Capstone Project

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

📅 Day 3: Generative AI & IoT Integration for Smart Infrastructure

Topics Covered

• 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

Tools & Platforms

• Google Colab

• Hugging Face Transformers

• Python Programming Environment

Capstone Project

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

• Civil, mechanical, aerospace, and materials engineers

• AI/ML developers interested in engineering applications

• Researchers in smart infrastructure, IoT, and NDE (non-destructive evaluation)

• Urban safety and infrastructure monitoring teams

• PhD/MS students in engineering or data science