Structural Health Monitoring
Structural health monitoring (SHM) is a technology to prevent possible structural failure, which would cause disasters as shown in the figures, by monitoring responses from the structure. The structural health monitoring is composed of three steps: (1) monitoring of responses from the structure, (2) information analysis from the measured responses, and (3) identification of condition of the structure. Accurate identification helps to take actions by authorities for the maintenance of the structure.
SSaS Lab seeks to develop various approaches for advanced SHM of our infrastructure. Innovative response measurement method or system, algorithms to read structural condition in high fidelity, and practical SHM approaches for on-service structures are being explored by the members. The applications of the developed approaches are to be expanded to the other engineering domains, such as mechanical, aerospace, and ocean engineering.
SSaS Lab seeks to develop various approaches for advanced SHM of our infrastructure. Innovative response measurement method or system, algorithms to read structural condition in high fidelity, and practical SHM approaches for on-service structures are being explored by the members. The applications of the developed approaches are to be expanded to the other engineering domains, such as mechanical, aerospace, and ocean engineering.
Sungsoo Bridge collapse
(Korea, 1994) |
I35W Mississippi River Bridge collapse
(US, 2007) |
Digital Image Processing and Computer Vision
By rapid development of digital imaging devices, such as cameras, the digital image processing (DIP) and computer vision (CV) techniques are becoming popular in the field of civil engineering. DIP and CV alter the ocular sensing by human, and even provide faster and more accurate recognition of target objects. Thus, visual changes of a structure can be easily captured by the DIP and CV systems, and be put into the database for systematic maintenance.
SSaS Lab seeks to develop a real-time displacement measurement system using a computer vision technique, a drone-based concrete crack assessment system, and a hyperspectral camera system for concrete efflorescence monitoring. The systems based on the state-of-the-art DIP and CV techniques are to be applied for the monitoring and assessment of our infrastructure.
SSaS Lab seeks to develop a real-time displacement measurement system using a computer vision technique, a drone-based concrete crack assessment system, and a hyperspectral camera system for concrete efflorescence monitoring. The systems based on the state-of-the-art DIP and CV techniques are to be applied for the monitoring and assessment of our infrastructure.
Real-time displacement measurement system
using computer vision |
Hyperspectral camera system
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Development of Deep Learning Model for Civil Structures
As an emerging technique these days, deep learning is investigated to resolve the limitations of current structural health monitoring techniques. Deep learning that uses a deep architecture of artificial neural networks can significantly enhance the automation, simulation, and interpretation of images and datas obtained from civil structures. Along with the tuned application of pre-developed models, the development of deep learning models is a difficult task due to the unique characteristics of civil structures.
SSaS Lab seeks to develop a deep learning models for rapid inspection of structures, vehicle location and weight estimation on the bridges, and structural condition change using the measured vibration data. For the development of accurate and practical models, the collection and interpretation of big data obtained at the structures and the structure networks are another key issues to be solved by our group.
SSaS Lab seeks to develop a deep learning models for rapid inspection of structures, vehicle location and weight estimation on the bridges, and structural condition change using the measured vibration data. For the development of accurate and practical models, the collection and interpretation of big data obtained at the structures and the structure networks are another key issues to be solved by our group.
Overall framework of automated concrete damage inspection
using deep learning |
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Internet of Things (IoT) / Wireless Smart Sensor
Advancement of small computing platforms embodies the wireless smart sensor as a promising alternative to the traditional tethered sensor systems. Due to the large scale of civil infrastructure, the tethering of sensors to the data repository takes a majority of the installation cost. The wireless smart sensors are typically small, inexpensive, and multi-functional. Based on the wireless communication and onboard computation, the wireless smart sensor is considered as a means of cost-effective monitoring of our infrastructure. Now the wireless sensor system is expanding to IoT-based structural sensing system. The IoT uses internet to upload/download, database, and manage data as an advanced monitoring scheme.
SSaS Lab seeks to develop an IoT-based structural monitoring system and wireless smart sensor system. The IoT system is based on mini computing platforms, such as Arduino and Raspberry Pi, while the wireless sensor system is on commercial wireless sensor platforms, such as Imote2 and Xnode. They will enable a flexible system to efficiently monitor civil structures using internet and wireless communication.
SSaS Lab seeks to develop an IoT-based structural monitoring system and wireless smart sensor system. The IoT system is based on mini computing platforms, such as Arduino and Raspberry Pi, while the wireless sensor system is on commercial wireless sensor platforms, such as Imote2 and Xnode. They will enable a flexible system to efficiently monitor civil structures using internet and wireless communication.
Imote2-based wireless smart sensor
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Arduino: microcontroller-based small computer kit
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Damage Scan of Concrete Structure Using Drone
Though concrete is one of the major materials used for civil structures, the assessment is mostly carried out based on the visual inspection of apparent damage on the concrete. The typical damage are concrete crack, efflorescene, spalling, delamination, etc. They may not be perfectly prevented, but after occurance, their growths to dangerous level needs to be monitored continuously using an automated tools.
SSaS Lab seeks to develop a flying drone system to scan concrete crack, efflorescene, and spalling using a combined vision system on the drone. The vision system includes a 2D/3D camera, a hyperspectral camera, and an IR camera. This system will leverage the monitoring of concrete structures in the automated manner during operation or after extreme events such as earthquakes and typhoons.
SSaS Lab seeks to develop a flying drone system to scan concrete crack, efflorescene, and spalling using a combined vision system on the drone. The vision system includes a 2D/3D camera, a hyperspectral camera, and an IR camera. This system will leverage the monitoring of concrete structures in the automated manner during operation or after extreme events such as earthquakes and typhoons.
Flying drone for civil structure monitoring
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Concept of drone system for concrete damage scan
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Risk Assessment of Civil Structures
To be updated.