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Deep Learning for Structural Health Monitoring
Original price was: USD $99.00.USD $59.00Current price is: USD $59.00.
International Workshop on AI-Driven Condition Assessment and Failure Prediction in Civil & Mechanical Structures
Course Overview
Deep Learning for Structural Health Monitoring is an international, cutting-edge course designed to explore how AI, especially deep learning techniques like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers, can be applied to monitor the health and integrity of physical structures.
Throughout this course, participants will learn to work with sensor data, vibration signals, thermal imagery, and inspection footage to build deep learning models for detecting cracks, localizing damage, predicting fatigue, and classifying structural conditions. This course bridges the gap between structural engineering, machine learning, and IoT, offering practical tools and deep insights for researchers and professionals in the field.
Course Objective
The aim is to provide participants with both theoretical and practical expertise in applying deep learning techniques to Structural Health Monitoring (SHM), empowering them to carry out early fault detection, predictive maintenance, and safety assurance in civil infrastructure, aerospace, mechanical systems, and smart cities.
Learning Outcomes
- Bridge the knowledge gap between AI and structural/mechanical engineering.
- Learn deep learning architectures tailored to SHM datasets.
- Foster the development of innovative, automated, and scalable monitoring tools.
- Prototype real-world solutions for infrastructure safety and maintenance.
- Promote AI integration in regulatory, public safety, and industrial maintenance practices.
Course Structure
📅 Module 1: Deep Learning Foundations for Structural Materials
- Topics Covered:
- Fundamentals of AI 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:
- Build deep learning models for structural monitoring data
- Use Convolutional Neural Networks (CNNs) for crack detection and classification from image datasets
- Tools & Platforms: Google Colab, Python, PyTorch, Keras, TensorFlow
📅 Module 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:
- Use LSTM networks for time-series prediction and vibration analysis in structural data
- Tools & Platforms: Google Colab, Python, PyTorch, Keras, TensorFlow
📅 Module 3: Generative AI & IoT Integration for Smart Infrastructure
- Topics Covered:
- Introduction to Generative AI: Concepts and Use Cases
- Overview of IoT in Smart Infrastructure Monitoring Systems
- Generative AI Model Development for Sensor Nodes and IoT Devices
- Training, Testing, and Validation of Generative AI Models on Time Series Data
- Practical Implementation:
- Deploy generative AI models on IoT sensor nodes for predictive infrastructure monitoring
- Tools & Platforms: Google Colab, Hugging Face Transformers, Python Programming Environment
Who Should Enrol?
- Civil, mechanical, aerospace, and materials engineers
- AI/ML developers interested in applying deep learning to engineering problems
- Researchers in smart infrastructure, IoT, and NDE (Non-Destructive Evaluation)
- Urban safety and infrastructure monitoring teams
- PhD/MS students in engineering, data science, or related fields
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