INTELLIGENT STRUCTURAL SYSTEMS & HEALTH MONITORING LABORATORY

Welcome to ISSHM LAB

Overview: The ISSHM Lab at the University of Louisiana at Lafayette is dedicated to advancing the frontiers of structural engineering through the integration of artificial intelligence, machine learning, and advanced materials. Our research focuses on creating intelligent systems for real-time health monitoring and the design of resilient, future-proof infrastructures.

Mission: Our mission is to revolutionize the way we approach structural health monitoring and resilient design. By developing innovative AI-driven solutions and high-performance materials, we aim to improve infrastructure safety, efficiency, and sustainability on a global scale.

Call-to-Action: Are you interested in collaborating or learning more about our cutting-edge research? Explore our projects, publications, and team, or get in touch with us today.

ONGOING RESEARCH PROJECTS

Framework Development of Digital Twin for real time monitoring: A Case Study OF STOKES HALL AT UL Lafayette.

This project aims to create a comprehensive digital twin of Stokes Hall at the University of Louisiana at Lafayette. By integrating advanced data collection, modeling, and simulation techniques, we will develop a virtual replica of the facility. This digital twin will serve as a framework for studying and implementing digital twin methodologies, providing insights into building performance, predictive maintenance, and operational optimization. The case study will contribute to best practices in digital twin applications within educational institutions.

Automated Bridge Damage Detection Using Machine Learning and AI

This project focuses on developing an automated system for detecting structural deficiencies in bridges by leveraging machine learning and artificial intelligence. By analyzing structural properties and employing advanced algorithms, the system aims to identify and predict potential damages, enhancing maintenance efficiency and ensuring bridge safety. The approach integrates data-driven techniques to monitor and assess bridge health in real-time.

Development of Smart Aggregate Materials with Embedded Wireless Sensors for Real-Time Structural Health Monitoring

This project focuses on the creation of innovative smart aggregate materials integrated with wireless sensors capable of real-time data transmission. These advanced materials aim to enhance structural health monitoring by providing continuous insights into the integrity and performance of concrete structures. By embedding sensors within the aggregate, the system facilitates early detection of potential issues, thereby improving maintenance strategies and extending the lifespan of infrastructure. The project encompasses the design, fabrication, and testing of these smart aggregates to ensure their effectiveness in various construction applications.

Development of Sustainable Sandwich Panels for Lightweight and Structurally Enhanced Bridge Decks

This project focuses on the design and fabrication of innovative sandwich panels utilizing sustainable materials to create bridge decks that are both lightweight and structurally robust. By integrating eco-friendly core substances with durable outer layers, the aim is to reduce the overall weight of bridge decks while maintaining or enhancing their load-bearing capacities. This approach not only contributes to environmental sustainability but also facilitates accelerated construction processes and extends the lifespan of bridge structures. The project encompasses material selection, structural design, and performance evaluation to ensure the developed panels meet the rigorous demands of modern bridge engineering

Developing an AI-powered Decision Support System for Bridge Maintenance Optimization

This research project aims to develop an advanced AI-powered Decision Support System (DSS) for optimizing bridge maintenance. The proposed system will revolutionize bridge management by integrating cutting-edge artificial intelligence technologies. It will utilize GPT models to efficiently process and analyze unstructured data from inspection reports, employ Artificial Neural Networks (ANNs) to predict bridge deterioration and optimize maintenance schedules, and incorporate real-time sensor data from IoT devices for continuous monitoring of bridge conditions. Additionally, the system will feature a genetic algorithm to optimize repair strategies and resource allocation while adhering to budget constraints. This comprehensive approach promises to enhance decision-making in bridge maintenance, leading to improved infrastructure longevity, cost-effectiveness, and public safety.

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