Summary:
UCLA researchers in the Department of Electrical and Computer Engineering have developed a novel structural health monitoring system that is highly accurate and cost effective, addressing limitations in current infrastructure and civil health monitoring and a rise in public safety concerns.
Background:
The need for structural health monitoring (SHM) has recently become more critical due to public safety concerns, driven by increased natural disasters and aging infrastructure. As these factors elevate the risk of structural failure, continuous monitoring of civil infrastructure has become essential. However, current SHM technologies, such as accelerometers and vibration sensors fall short due to spatial and temporal precision limitations. Additionally, the adoption of advanced laser-based sensors is hindered by high costs and the logistics associated with their complex systems. There is a pressing demand for cost-effective, precise, and highly responsive civil SHM systems to enhance public safety.
Innovation:
To address these limitations, UCLA researchers developed a cost-effective, highly accurate, and universally deployable comprehensive system for SHM. This novel sensor system integrates diffractive optical layers with embedded damage identification algorithms. By doing so, it reduces dependence on costly conventional sensors, data acquisition hardware, and complex post-processing methods, ultimately simplifying costs and logistics. The diffractive optical layers govern light propagation and diffraction, optimized by the integrated algorithms, enabling statistical inference. Statistical inference improves data analysis and allows for more reliable conclusions about structural health. Additionally, lower costs and simplified logistics improve SHM coverage for civil infrastructure, enhancing safety and infrastructure lifespan, all while leveraging the latest advancements in sensor technology and data analytics. This innovative system has the potential to transform structural health monitoring by providing real-time updates, helping to prevent structural failures and enhance public safety.
Potential Applications:
• Civil Infrastructure SHM
• Bridges, roads, dams
• Buildings, skyscrapers, historical monuments
• Railroad tracks, airport runways
• Wind turbines, power plants (cooling towers, reactor vessels, turbines)
• Offshore/Marine SHM
• Oil Platforms
• Offshore rigs
• Seismic Activity Monitoring
• Disaster Management
Advantages:
• Spatial and temporal precision
• Improved accuracy
• Affordability
• Suitable for large-scale projects
• Scalable and universally deployable
• Simplicity and reduced maintenance/downtime
Development-To-Date:
Successful experimental demonstrations and conducted cost-analysis
Related Papers:
[1] Structural Vibration Monitoring with Diffractive Optical Processors
https://arxiv.org/abs/2506.03317
[2] Lin X, Rivenson Y, Yardimci NT, Veli M, Luo Y, Jarrahi M, Ozcan A (2018) All-optical machine learning using diffractive deep neural networks. Science, 361(6406) https://doi.org/10.1126/science.aat8084
[3] Kulce O, Mengu D, Rivenson Y, Ozcan A (2021) All-optical information-processing capacity of diffractive surfaces. Light: Science and Applications, 10(1) https://doi.org/10.1038/s41377-020-00439-9
Reference:
UCLA Case No. 2025-201
Lead Inventor:
Aydogan Ozcan