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Article
Publication date: 20 December 2024

Ahmad Honarjoo, Ehsan Darvishan, Hassan Rezazadeh and Amir Homayoon Kosarieh

This article addresses the need for a comprehensive model for structural crack detection in the context of structural health monitoring. The main innovation of this research is…

19

Abstract

Purpose

This article addresses the need for a comprehensive model for structural crack detection in the context of structural health monitoring. The main innovation of this research is the introduction of a dynamic attention-based transformer model that significantly enhances the accuracy and efficiency of detecting and localizing cracks in structures. This study seeks to overcome previous limitations and contribute to advancements in structural health monitoring practices.

Design/methodology/approach

The research focuses on three primary computer vision tasks: classification, object detection and semantic segmentation applied to crack detection in concrete, brick and asphalt structures. The proposed approach employs transformer encoders with dynamic attention mechanisms to assess the severity and extent of damage accurately.

Findings

In this study, we propose a dynamic attention-based transformer model for structural crack detection, achieving a remarkable accuracy of 99.38% and an impressive F1 score. Our method demonstrates superior performance compared to existing techniques, such as the fusion features-based broad learning system and deep convolutional neural networks, while also significantly reducing execution time, highlighting its efficiency and potential for practical applications in structural health monitoring.

Originality/value

This research introduces a novel framework for crack detection, leveraging recent advancements in deep learning technology, with significant implications for the field of civil engineering and maintenance.

Details

International Journal of Building Pathology and Adaptation, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2398-4708

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Article
Publication date: 12 April 2024

Ahmad Honarjoo and Ehsan Darvishan

This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of…

57

Abstract

Purpose

This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.

Design/methodology/approach

This paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.

Findings

Results show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.

Originality/value

This paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.

Details

International Journal of Structural Integrity, vol. 15 no. 3
Type: Research Article
ISSN: 1757-9864

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Article
Publication date: 13 September 2024

Ahmad Honarjoo, Ehsan Darvishan, Hassan Rezazadeh and Amir Homayoon Kosarieh

This article introduces SigBERT, a novel approach that fine-tunes bidirectional encoder representations from transformers (BERT) for the purpose of distinguishing between intact…

67

Abstract

Purpose

This article introduces SigBERT, a novel approach that fine-tunes bidirectional encoder representations from transformers (BERT) for the purpose of distinguishing between intact and impaired structures by analyzing vibration signals. Structural health monitoring (SHM) systems are crucial for identifying and locating damage in civil engineering structures. The proposed method aims to improve upon existing methods in terms of cost-effectiveness, accuracy and operational reliability.

Design/methodology/approach

SigBERT employs a fine-tuning process on the BERT model, leveraging its capabilities to effectively analyze time-series data from vibration signals to detect structural damage. This study compares SigBERT's performance with baseline models to demonstrate its superior accuracy and efficiency.

Findings

The experimental results, obtained through the Qatar University grandstand simulator, show that SigBERT outperforms existing models in terms of damage detection accuracy. The method is capable of handling environmental fluctuations and offers high reliability for non-destructive monitoring of structural health. The study mentions the quantifiable results of the study, such as achieving a 99% accuracy rate and an F-1 score of 0.99, to underline the effectiveness of the proposed model.

Originality/value

SigBERT presents a significant advancement in SHM by integrating deep learning with a robust transformer model. The method offers improved performance in both computational efficiency and diagnostic accuracy, making it suitable for real-world operational environments.

Details

International Journal of Structural Integrity, vol. 15 no. 5
Type: Research Article
ISSN: 1757-9864

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Article
Publication date: 12 October 2021

Xiaobo Dong and Ely Salwana

One of the significant dimensions in organizations is the human resource discussion and its related procedures. Human beings have a special place and particular importance in…

426

Abstract

Purpose

One of the significant dimensions in organizations is the human resource discussion and its related procedures. Human beings have a special place and particular importance in modern management to focus on organizational behavior and human resource management. The cloud-based human resource (CBHR) can be converted into human capital and impressive contributions to health, education and moral valence. Also, one of the most robust operational paradigms is the cloud-based supply chain management (CBSCM) for developing the competitive pros of service and manufacturing organizations to give the same attention to those who manage the supply chain or those operating in it. The present investigation's target is to check out whether CBHR and CBSCM enhance the performance of multinational organizations.

Design/methodology/approach

Multinational organizations consist of numerous integrated applications, including manufacturing, logistics, marketing, accounting, distribution, human resources, finance, supply chain, etc. This paper proposes using CBHR and CBSCM to improve financial, marketing and collaborative performances. We focus on the applications in multinational organizations. This study has utilized the SEM to estimate the measurement model's validity and reliability and assess the causal model. The offered model and the questionnaires were analyzed using SPSS and LISREL.

Findings

The research results showed that CBSCM influences the companies' performance. Additionally, the outcomes showed that CBHRM affects the performance of companies. The results support the proposition that CBSCM and CBHRM are both necessary and good for financial performance, marketing performance and collaborative performance.

Research limitations/implications

Using a questionnaire to mentally measure some of the variable dimensions of a firm's financial performance and market performance for which objective data are available can be helpful. However, since such information is considered confidential in companies, it is not possible to access it.

Originality/value

Our innovation is the primary attempt of applying CBHR and CBSCM to elevate performance in multinational organizations.

Details

Kybernetes, vol. 51 no. 6
Type: Research Article
ISSN: 0368-492X

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