Showing items 1 - 4 of 4

A comprehensive taxonomy for structure and material deficiencies, preventions and remedies of timber bridges

- Rashidi, Maria, Hoshyar, Azadeh, Smith, Liam, Bijan, Samali, Siddique, Rafat

  • Authors: Rashidi, Maria , Hoshyar, Azadeh , Smith, Liam , Bijan, Samali , Siddique, Rafat
  • Date: 2021
  • Type: Text , Journal article , Review
  • Relation: Journal of Building Engineering Vol. 34, no. (2021), p.
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  • Description: As timber bridges have become archaic, they are no longer able to effectively service their community. It is neither practical, nor possible, to replace all existing timber bridges, hence it is of paramount importance to maintain and extend the service life of those remaining timber bridges. The following discourse intends to provide an extensive and comprehensive review of the various deterioration mechanisms, the preventive actions and possible remedial options for management and maintenance of timber bridges. The classified information has been summarised in a tabular format and presented as a ready-reckoner taxonomy for quick reference. This taxonomy is purely a re-staetment of the information already covered in the paper, but when presented in the summary form, reference becomes highly convenient. © 2020

Algorithm development for the non-destructive testing of structural damage

- Noori Hoshyar, Azadeh, Rashidi, Maria, Liyanapathirana, Ranjith, Samali, Bijan

  • Authors: Noori Hoshyar, Azadeh , Rashidi, Maria , Liyanapathirana, Ranjith , Samali, Bijan
  • Date: 2019
  • Type: Text , Journal article
  • Relation: Applied sciences Vol. 9, no. 14 (2019), p. 2810
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  • Description: Monitoring of structures to identify types of damages that occur under loading is essential in practical applications of civil infrastructure. In this paper, we detect and visualize damage based on several non-destructive testing (NDT) methods. A machine learning (ML) approach based on the Support Vector Machine (SVM) method is developed to prevent misdirection of the event interpretation of what is happening in the material. The objective is to identify cracks in the early stages, to reduce the risk of failure in structures. Theoretical and experimental analyses are derived by computing the performance indicators on the smart aggregate (SA)-based sensor data for concrete and reinforced-concrete (RC) beams. Validity assessment of the proposed indices was addressed through a comparative analysis with traditional SVM. The developed ML algorithms are shown to recognize cracks with a higher accuracy than the traditional SVM. Additionally, we propose different algorithms for microwave- or millimeter-wave imaging of steel plates, composite materials, and metal plates, to identify and visualize cracks. The proposed algorithm for steel plates is based on the gradient magnitude in four directions of an image, and is followed by the edge detection technique. Three algorithms were proposed for each of composite materials and metal plates, and are based on 2D fast Fourier transform (FFT) and hybrid fuzzy c-mean techniques, respectively. The proposed algorithms were able to recognize and visualize the cracking incurred in the structure more efficiently than the traditional techniques. The reported results are expected to be beneficial for NDT-based applications, particularly in civil engineering.

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

- Yu, Yang, Hoshyar, Azadeh, Samali, Bijan, Zhang, Guang, Rashidi, Maria, Mohammadi, Masoud

  • 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

- Noori Hoshyar, Azadeh, Rashidi, Maria, Yu, Yang, Samali, Bijan

  • 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.
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