scholarly journals Study on the Non-contact Acoustic Inspection Method for Concrete Structures by using Strong Ultrasonic Sound source

2015 ◽  
Vol 70 ◽  
pp. 398-401 ◽  
Author(s):  
Tsuneyoshi Sugimoto ◽  
Itsuki Uechi ◽  
Kazuko Sugimoto ◽  
Noriyuki Utagawa ◽  
Kageyoshi Katakura
Keyword(s):  
Sound Source ◽  
Concrete Structures ◽  
Inspection Method ◽  
Ultrasonic Sound ◽  
Acoustic Inspection

Study on the measurement speed and signal to noise ratio of multi tone burst wave for high speed non-contact acoustic inspection method

2016 ◽  
Vol 140 (4) ◽  
pp. 3212-3212
Author(s):  
Nobuaki Kosuge ◽  
Tsuneyosi Sugimoto ◽  
Kazuko Sugimoto ◽  
Chitose Kuroda ◽  
Noriyuki Utagawa
Keyword(s):  
High Speed ◽  
Signal To Noise Ratio ◽  
Tone Burst ◽  
Signal To Noise ◽  
Inspection Method ◽  
Noise Ratio ◽  
Acoustic Inspection

Study on detectable size and depth of defects in noncontact acoustic inspection method

2014 ◽  
Vol 53 (7S) ◽  
pp. 07KC15 ◽  
Author(s):  
Kageyoshi Katakura ◽  
Ryo Akamatsu ◽  
Tsuneyoshi Sugimoto ◽  
Noriyuki Utagawa
Keyword(s):  
Inspection Method ◽  
Acoustic Inspection

Vision-based automated crack detection using convolutional neural networks for condition assessment of infrastructure

2020 ◽  
pp. 147592172096544
Author(s):  
Aravinda S Rao ◽  
Tuan Nguyen ◽  
Marimuthu Palaniswami ◽  
Tuan Ngo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crack Detection ◽  
Concrete Structures ◽  
Area Under The Curve ◽  
Network Models ◽  
Condition Assessment ◽  
Neural Network Models ◽  
Data Set ◽  
Inspection Method

With the growing number of aging infrastructure across the world, there is a high demand for a more effective inspection method to assess its conditions. Routine assessment of structural conditions is a necessity to ensure the safety and operation of critical infrastructure. However, the current practice to detect structural damages, such as cracks, depends on human visual observation methods, which are prone to efficiency, cost, and safety concerns. In this article, we present an automated detection method, which is based on convolutional neural network models and a non-overlapping window-based approach, to detect crack/non-crack conditions of concrete structures from images. To this end, we construct a data set of crack/non-crack concrete structures, comprising 32,704 training patches, 2074 validation patches, and 6032 test patches. We evaluate the performance of our approach using 15 state-of-the-art convolutional neural network models in terms of number of parameters required to train the models, area under the curve, and inference time. Our approach provides over 95% accuracy and over 87% precision in detecting the cracks for most of the convolutional neural network models. We also show that our approach outperforms existing models in literature in terms of accuracy and inference time. The best performance in terms of area under the curve was achieved by visual geometry group-16 model (area under the curve = 0.9805) and best inference time was provided by AlexNet (0.32 s per image in size of 256 × 256 × 3). Our evaluation shows that deeper convolutional neural network models have higher detection accuracies; however, they also require more parameters and have higher inference time. We believe that this study would act as a benchmark for real-time, automated crack detection for condition assessment of infrastructure.

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