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Smart Structures and Systems Volume 32, Number 2, August 2023 , pages 111-121 DOI: https://doi.org/10.12989/sss.2023.32.2.111 |
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Assembly performance evaluation method for prefabricated steel structures using deep learning and k-nearest neighbors |
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Hyuntae Bang, Byeongjun Yu and Haemin Jeon
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| Abstract | ||
| This study proposes an automated assembly performance evaluation method for prefabricated steel structures (PSSs) using machine learning methods. Assembly component images were segmented using a modified version of the receptive field pyramid. By factorizing channel modulation and the receptive field exploration layers of the convolution pyramid, highly accurate segmentation results were obtained. After completing segmentation, the positions of the bolt holes were calculated using various image processing techniques, such as fuzzy-based edge detection, Hough's line detection, and image perspective transformation. By calculating the distance ratio between bolt holes, the assembly performance of the PSS was estimated using the k-nearest neighbors (kNN) algorithm. The effectiveness of the proposed framework was validated using a 3D PSS printing model and a field test. The results indicated that this approach could recognize assembly components with an intersection over union (IoU) of 95% and evaluate assembly performance with an error of less than 5%. | ||
| Key Words | ||
| assembly performance evaluation; k-nearest neighbors; machine learning; prefabricated steel structure; semantic segmentation; vision sensor | ||
| Address | ||
| "(1) Hyuntae Bang: Department of Autonomous Vehicle System Engineering, Chungnam National University, Yuseong-gu, Daejeon, 34134, Republic of Korea; (2) Byeongjun Yu: StradVision, Seoul, 06621, Republic of Korea; (3) Haemin Jeon: Department of Civil and Environmental Engineering, Hanbat National University, Yuseong-gu, Daejeon, 34158, Republic of Korea." | ||