Corrosion and coating defect assessment of Coal Handling and Preparation Plants (CHPP) using an ensemble of deep convolutional neural networks and decision-level data fusion
- Authors: Yu, Yang , Hoshyar, Azadeh , Samali, Bijan , Zhang, Guang , Rashidi, Maria , Mohammadi, Masoud
- Date: 2023
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 35, no. 25 (2023), p. 18697-18718
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- Description: In view of the problems of ineffective feature extraction and low detection accuracy in existing detection system, this study presents a novel machine vision-based approach composed of an ensemble of deep convolutional neural networks (CNNs) and improved Dempster-Shafer (D-S) theory-based data fusion to evaluate corrosion and coating defect of coal handling and preparation plants. To start with, the structural surface image is sent to each transferred CNN for initial defect identification. Then, an improved D-S fusion algorithm is proposed to combine the identification results from different CNNs, which are vectors consisting of statistical indicators of all the potential damage severity categories. The decision-level fusion of different CNNs can effectively improve image classification. To validate the performance of the proposed method, a dataset made of 3593 surface images with different defect severities captured from mining infrastructure in field is established together with data augmentation. The validation result demonstrates that the proposed method is able to effectively improve the recognition accuracy of defect severity and reduce the wrong recognition rate. Finally, the robustness of the proposed approach is also appraised by polluting the images with different types and intensities of noise, with satisfactory results. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Proposed machine learning techniques for bridge structural health monitoring : a laboratory study
- Authors: Noori Hoshyar, Azadeh , Rashidi, Maria , Yu, Yang , Samali, Bijan
- Date: 2023
- Type: Text , Journal article
- Relation: Remote Sensing Vol. 15, no. 8 (2023), p.
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- Description: Structural health monitoring for bridges is a crucial concern in engineering due to the degradation risks caused by defects, which can become worse over time. In this respect, enhancement of various models that can discriminate between healthy and non-healthy states of structures have received extensive attention. These models are concerned with implementation algorithms, which operate on the feature sets to quantify the bridge’s structural health. The functional correlation between the feature set and the health state of the bridge structure is usually difficult to define. Therefore, the models are derived from machine learning techniques. The use of machine learning approaches provides the possibility of automating the SHM procedure and intelligent damage detection. In this study, we propose four classification algorithms to SHM, which uses the concepts of support vector machine (SVM) algorithm. The laboratory experiment, which intended to validate the results, was performed at Western Sydney University (WSU). The results were compared with the basic SVM to evaluate the performance of proposed algorithms. © 2023 by the authors.