Multi-layers deep learning model with feature selection for automated detection and classification of highway pavement cracks

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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.

Original languageEnglish
JournalSmart and Sustainable Built Environment
Early online date15 Jan 2024
DOIs
Publication statusEarly online date - 15 Jan 2024

Keywords

  • CNN
  • Feature selection
  • Highway surface cracks
  • Optimisation algorithms
  • Particle swarm optimisation (PSO)

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Architecture
  • Cultural Studies
  • Renewable Energy, Sustainability and the Environment
  • Building and Construction
  • Urban Studies

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