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Article
Publication date: 28 February 2023

Sandra Matarneh, Faris Elghaish, Amani Al-Ghraibah, Essam Abdellatef and David John Edwards

Incipient detection of pavement deterioration (such as crack identification) is critical to optimizing road maintenance because it enables preventative steps to be implemented to…

Abstract

Purpose

Incipient detection of pavement deterioration (such as crack identification) is critical to optimizing road maintenance because it enables preventative steps to be implemented to mitigate damage and possible failure. Traditional visual inspection has been largely superseded by semi-automatic/automatic procedures given significant advancements in image processing. Therefore, there is a need to develop automated tools to detect and classify cracks.

Design/methodology/approach

The literature review is employed to evaluate existing attempts to use Hough transform algorithm and highlight issues that should be improved. Then, developing a simple low-cost crack detection method based on the Hough transform algorithm for pavement crack detection and classification.

Findings

Analysis results reveal that model accuracy reaches 92.14% for vertical cracks, 93.03% for diagonal cracks and 95.61% for horizontal cracks. The time lapse for detecting the crack type for one image is circa 0.98 s for vertical cracks, 0.79 s for horizontal cracks and 0.83 s for diagonal cracks. Ensuing discourse serves to illustrate the inherent potential of a simple low-cost image processing method in automated pavement crack detection. Moreover, this method provides direct guidance for long-term pavement optimal maintenance decisions.

Research limitations/implications

The outcome of this research can help highway agencies to detect and classify cracks accurately for a very long highway without a need for manual inspection, which can significantly minimize cost.

Originality/value

Hough transform algorithm was tested in terms of detect and classify a large dataset of highway images, and the accuracy reaches 92.14%, which can be considered as a very accurate percentage regarding automated cracks and distresses classification.

Details

Smart and Sustainable Built Environment, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2046-6099

Keywords

Open Access
Article
Publication date: 13 October 2023

Maud van Merriënboer, Michiel Verver and Miruna Radu-Lefebvre

Drawing on an intersectional perspective on racial, migrant and entrepreneurial identities, this paper investigates the identity work of racial minority entrepreneurs with…

1099

Abstract

Purpose

Drawing on an intersectional perspective on racial, migrant and entrepreneurial identities, this paper investigates the identity work of racial minority entrepreneurs with native-born and migrant backgrounds, confronted to experiences of othering in a White entrepreneurial ecosystem.

Design/methodology/approach

The study takes a qualitative-interpretivist approach and builds on six cases of racial minority entrepreneurs in nascent stages of venture development within the Dutch technology sector. The dataset comprises 24 in-depth interviews conducted over the course of one and a half year, extensive case descriptions and online sources. The data is thematically and inductively analysed.

Findings

Despite strongly self-identifying as entrepreneurs, the research participants feel marginalised and excluded from the entrepreneurial ecosystem, which results in ongoing threats to their existential authenticity as they build a legitimate entrepreneurial identity. Minority entrepreneurs navigate these threats by either downplaying or embracing their marginalised racial and/or migrant identities.

Originality/value

The study contributes to the literature on the identity work of minority entrepreneurs. The paper reveals that, rather than “strategising away” the discrimination and exclusion resulting from othering, racial minority entrepreneurs seek to preserve their sense of existential authenticity and self-worth, irrespective of entrepreneurial outcomes. In so doing, the study challenges the dominant perspective of entrepreneurial identity work among minority entrepreneurs as overly instrumental and market-driven. Moreover, the study also contributes to the literature on authenticity in entrepreneurship by highlighting how racial minority entrepreneurs navigate authenticity threats while building legitimacy in a White ecosystem.

Details

International Journal of Entrepreneurial Behavior & Research, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1355-2554

Keywords

Article
Publication date: 15 January 2024

Faris Elghaish, Sandra Matarneh, Essam Abdellatef, Farzad Rahimian, M. Reza Hosseini and Ahmed Farouk Kineber

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly…

Abstract

Purpose

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

Design/methodology/approach

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

Findings

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

Practical implications

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

Originality/value

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.

Details

Smart and Sustainable Built Environment, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2046-6099

Keywords

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