Unleashing the Potential of Quality NDT Data to Safeguard Our Built Environment
Just like humans, built structures require health monitoring too. And just like humans, prevention is the best cure. Early identification and preventative efforts in structural health can minimize time and costs, and most importantly, save lives.
However, gaining clear insights into a structure's condition takes more than just collecting data; it requires a combination of approaches and technologies, including advanced Non-Destructive Testing (NDT) and Artificial Intelligence (AI).
Data is the starting point for monitoring structural health, increasing safety, and boosting the value of assets. But not all data is equal. Some NDT data can be heavy to process, complicated to analyze, or simply too poor in quality to use, on the other hand, the right data is extremely valuable…
The Power of Quality NDT Data
High-quality NDT data is a precious asset that can save trillions of dollars by eliminating waste, saving time, enabling faster and clearer decision-making, and facilitating preventive maintenance. This data can provide valuable insights into the durability of post-tensioned and reinforced concrete and its capability to withstand challenges like seismic activity. This is now more crucial than ever as many structures around the world require assessment.
If we look at bridge structures in the U.S alone, a 2021 report by the American Road & Transportation Builders Association (ARTBA), found that over 220,000 bridges (or 36% of all bridges in the country) need major repair work or replacement, which would require an investment of nearly $42 billion.
Using data from multiple sources, it’s possible to prioritize maintenance plans in importance of what needs doing right away, and what can wait. This helps to avoid small issues turning into unmanageable problems and increases the longevity of structures. NDT can be used at every stage of the structure’s lifecycle to create reliable data-driven models and achieve predictive structural health monitoring (SHM). This is where we are starting to see a huge transformation in the way data is used to protect the built world.
Embracing AI and Big Data for Structural Health
Machine learning and big data are already being used to predict future defects or optimize maintenance processes in industrial machinery, aerospace, mining equipment, and many other sectors. However, the adoption of AI and big data analysis for structural health has been slower.
Companies like Screening Eagle Technologies are striving to close this global technological gap by providing advanced NDT solutions that enable early defect detection and diagnosis through visual checks, vital sign assessment, and imaging.
AI-Enhanced Visual Inspection
In the past, surface conditions and defects would be checked with an expert human eye and a clipboard for taking notes. Now, it can be completed with AI-assisted software to further enhance what the human eye can see with automatic crack detection and 360-degree photo capture. Instead of paper and pen, an iPad can be used so that all structural data is instantly geolocated to the map for future reference and fast reporting.
Comprehensive Vital Sign Assessment
Checking the vital signs of structures involves various methods of NDT to capture clear data on strength, uniformity, thickness, defects, permeability, and corrosion. NDT technologies like ultrasonics, rebound hammers, corrosion sensors, and electrical resistivity testers can capture crucial data on strength, uniformity, thickness, defects, permeability, and corrosion, informing critical maintenance decisions. The data from these vital sign assessments can be the difference between a functional valuable asset and structural failure.
Advanced Structural Imaging
Just like when you have an X-ray or ultrasound test to visualize what is happening inside your body, similar imaging tests need to be done for structures. Ground-penetrating radar (GPR), ultrasound, and Eddy current technologies can detect objects, rebars, and defects within structures and assess the concrete thickness, rebar cover, and diameter. Recent advances have made these devices portable, ergonomic, and easy to use and in the more advanced cases, with 3D visualization and augmented reality capabilities for in-depth analysis.
Incorporating these three components into the SHM strategy eliminates any element of guesswork with clear diagnostic data. For predictive healthcare to work for all structures, it’s imperative to not only have an accurate view of its current condition but also to centralize all data consistently. Creating a digital twin is fast becoming the most popular way to do this, by connecting all asset data with a real-time representation of the structure.
The Rise of Digital Twins
Digital twins are real-time representations of built structures that can offer valuable insights for asset owners and engineers. By using intelligent asset management software, engineers can create a 3D replica of the structure and populate it with deep data sets from all non-destructive testing (NDT) and evaluations. This allows for the centralization of data and enables predictive maintenance, potentially providing millions of data points over the entire lifespan of the structure. Digital twins have the potential to revolutionize asset management and could be extremely valuable for the future of cities if all buildings and infrastructure had them.
Beyond Predictive SHM: The Next Frontier
As we continue to integrate advanced NDT, AI, and digital twins into our predictive SHM strategies, we can explore even more innovative ways to ensure the safety and longevity of our built environment.
1. Integrating IoT for Real-Time Monitoring
The Internet of Things (IoT) presents an opportunity to further enhance SHM by embedding sensors within structures to monitor key parameters continuously. IoT-enabled sensors can provide real-time information on stress, temperature, humidity, and other factors, allowing engineers to act promptly in response to changing conditions.
2. Expanding the Use of Drones and Robotics
Drones and robotics can play a crucial role in structural health assessment, particularly in hard-to-reach or hazardous areas. Equipped with advanced NDT tools and AI-driven data analysis, drones and robots can improve inspection efficiency while reducing the risk for human inspectors.
3. Developing Smarter, Self-Healing Materials
The development of smart materials, such as self-healing concrete and shape-memory alloys, has the potential to revolutionize the way we approach structural health. These innovative materials can detect and repair damage automatically, significantly reducing maintenance costs and enhancing the resilience of our built environment.
4. Fostering Cross-Disciplinary Collaboration
Encouraging collaboration between civil engineers, NDT professionals, data scientists, and other experts is essential for driving innovation in SHM. By combining diverse perspectives and expertise, we can develop more effective solutions and further advance the field of structural health monitoring.
Conclusion
Structural health matters and affects us all. Quality data that keep structures safe and healthy play a significant contribution to the well-being of our environment and the planet. With advanced NDT, AI, digital twins, and emerging technologies they are paving the way for safer, more resilient, and sustainable cities.
