Smart Structures and Systems

CONTENTS

Volume 29, Number 1, 2022

• Preface: Special issue of the 1st International Project Competition of Structural Health Monitoring (IPC-SHM 2020)

  Dr. Yuequan Bao, Dr. Jian Li, Dr. Tomonori Nagayama, Dr. Hui Li, Dr. Billie F. Spencer, Jr.

  Abstract | Full Text (0.11 MB)

  pages i-i.

• A modified U-net for crack segmentation by Self-Attention-Self-Adaption neuron and random elastic deformation

  Jin Zhao, Fangqiao Hu, Weidong Qiao, Weida Zhai, Yang Xu, Yuequan Bao and Hui Li

  Abstract | Full Text (3.10 MB)

  pages 1-16.

  DOI: 10.12989/sss.2022.29.1.001

• Damaged cable detection with statistical analysis, clustering, and deep learning models

  Hyesook Son, Chanyoung Yoon, Yejin Kim, Yun Jang, Linh Viet Tran, Seung-Eock Kim, Dong Joo Kim and Jongwoong Park

  Abstract | Full Text (3.69 MB)

  pages 17-28.

  DOI: 10.12989/sss.2022.29.1.017

• A three-stage deep-learning-based method for crack detection of high-resolution steel box girder image

  Shiqiao Meng, Zhiyuan Gao, Ying Zhou, Bin He and Qingzhao Kong

  Abstract | Full Text (3.51 MB)

  pages 29-39.

  DOI: 10.12989/sss.2022.29.1.029

• Unsupervised one-class classification for condition assessment of bridge cables using Bayesian factor analysis

  Xiaoyou Wang, Lingfang Li, Wei Tian, Yao Du, Rongrong Hou and Yong Xia

  Abstract | Full Text (2.66 MB)

  pages 41-51.

  DOI: 10.12989/sss.2022.29.1.041

• Data anomaly detection for structural health monitoring using a combination network of GANomaly and CNN

  Gaoyang Liu, Yanbo Niu, Weijian Zhao, Yuanfeng Duan and Jiangpeng Shu

  Abstract | Full Text (3.01 MB)

  pages 53-62.

  DOI: 10.12989/sss.2022.29.1.053

• Convolutional neural network-based data anomaly detection considering class imbalance with limited data

  Yao Du, Ling-fang Li, Rong-rong Hou, Xiao-you Wang, Wei Tian and Yong Xia

  Abstract | Full Text (2.03 MB)

  pages 63-75.

  DOI: 10.12989/sss.2022.29.1.063

• SHM data anomaly classification using machine learning strategies: A comparative study

  Jau-Yu Chou, Yuguang Fu, Shieh-Kung Huang and Chia-Ming Chang

  Abstract | Full Text (3.53 MB)

  pages 77-91.

  DOI: 10.12989/sss.2022.29.1.077

• Data anomaly detection for structural health monitoring of bridges using shapelet transform

  Monica Arul and Ahsan Kareem

  Abstract | Full Text (2.70 MB)

  pages 93-103.

  DOI: 10.12989/sss.2022.29.1.093

• Condition assessment of stay cables through enhanced time series classification using a deep learning approach

  Zhiming Zhang, Jin Yan, Liangding Li, Hong Pan and Chuanzhi Dong

  Abstract | Full Text (4.47 MB)

  pages 105-116.

  DOI: 10.12989/sss.2022.29.1.105

• Data abnormal detection using bidirectional long-short neural network combined with artificial experience

  Kang Yang, Huachen Jiang, Youliang Ding, Manya Wang and Chunfeng Wan

  Abstract | Full Text (2.81 MB)

  pages 117-127.

  DOI: 10.12989/sss.2022.29.1.117

• Detection of multi-type data anomaly for structural health monitoring using pattern recognition neural network

  Ke Gao, Zhi-Dan Chen, Shun Weng, Hong-Ping Zhu and Li-Ying Wu

  Abstract | Full Text (2.51 MB)

  pages 129-140.

  DOI: 10.12989/sss.2022.29.1.129

• Smartphone-based structural crack detection using pruned fully convolutional networks and edge computing

  X.W. Ye, Z.X. Li and T. Jin

  Abstract | Full Text (2.92 MB)

  pages 141-151.

  DOI: 10.12989/sss.2022.29.1.141

• One-step deep learning-based method for pixel-level detection of fine cracks in steel girder images

  Zhihang Li, Mengqi Huang, Pengxuan Ji, Huamei Zhu and Qianbing Zhang

  Abstract | Full Text (2.97 MB)

  pages 153-166.

  DOI: 10.12989/sss.2022.29.1.153

• Long-term condition monitoring of cables for in-service cable-stayed bridges using matched vehicle-induced cable tension ratios

  Zhen Peng, Jun Li and Hong Hao

  Abstract | Full Text (3.21 MB)

  pages 167-179.

  DOI: 10.12989/sss.2022.29.1.167

• CNN based data anomaly detection using multi-channel imagery for structural health monitoring

  Shaik Althaf V. Shajihan, Shuo Wang, Guanghao Zhai and Billie F. Spencer Jr.

  Abstract | Full Text (3.77 MB)

  pages 181-193.

  DOI: 10.12989/sss.2022.29.1.181

• An active learning method with difficulty learning mechanism for crack detection

  Jiangpeng Shu, Jun Li, Jiawei Zhang, Weijian Zhao, Yuanfeng Duan and Zhicheng Zhang

  Abstract | Full Text (2.88 MB)

  pages 195-206.

  DOI: 10.12989/sss.2022.29.1.195

• Impacts of label quality on performance of steel fatigue crack recognition using deep learning-based image segmentation

  Shun-Hsiang Hsu, Ting-Wei Chang and Chia-Ming Chang

  Abstract | Full Text (3.07 MB)

  pages 207-220.

  DOI: 10.12989/sss.2022.29.1.207

• Crack segmentation in high-resolution images using cascaded deep convolutional neural networks and Bayesian data fusion

  Wen Tang, Rih-Teng Wu and Mohammad R. Jahanshahi

  Abstract | Full Text (5.09 MB)

  pages 221-235.

  DOI: 10.12989/sss.2022.29.1.221

• Synthetic data augmentation for pixel-wise steel fatigue crack identification using fully convolutional networks

  Guanghao Zhai, Yasutaka Narazaki, Shuo Wang, Shaik Althaf V. Shajihan and Billie F. Spencer Jr.

  Abstract | Full Text (2.60 MB)

  pages 237-250.

  DOI: 10.12989/sss.2022.29.1.237

• A semi-supervised interpretable machine learning framework for sensor fault detection

  Panagiotis Martakis, Artur Movsessian, Yves Reuland, Sai G.S. Pai, Said Quqa, David Garcıa Cava, Dmitri Tcherniak and Eleni Chatzi

  Abstract | Full Text (3.65 MB)

  pages 251-266.

  DOI: 10.12989/sss.2022.29.1.251