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Efficient Prediction of Bridge Conditions Using Modified Convolutional Neural Network

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Abstract

Artificial Intelligence (AI) technology has proved itself as a proficient substitute for classical techniques of modeling. AI is a branch of computer science with the help of which machines and software with intelligence similar to humans can be developed. Many problems related to structural as well as civil engineering are exaggerated with uncertainties that are difficult to be solved using traditional techniques. AI proves advantageous in solving these complex problems. Presently, a comprehensive model based on the convolutional neural network technique of artificial intelligence is developed. This model is advantageous in accurately predicting the structure of a bridge without the need for actual testing. The firefly algorithm is used as a technique for accurate feature selection. The database is taken from national bridge inventory (NBI) using internet sources. Different performance measures like accuracy, recall, precision, and F1 score are considered for accurate prediction of the bridge structure and also provide advantages in actual monitoring and controlling of bridges. The proposed CNN model is used to measure these parameters and to provide a comparison with the standard CNN model. The proposed model provides a considerable amount of accuracy (97.49%) as compared to accuracy value (85%) using the standard CNN model.

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Authors and Affiliations

  1. Department of Civil Engineering, RIMT University, Punjab, India

    Amit Kumar & Sandeep Singla

  2. Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, India

    Ajay Kumar, Aarti Bansal & Avneet Kaur

Authors
  1. Amit Kumar
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  2. Sandeep Singla
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  3. Ajay Kumar
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  4. Aarti Bansal
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  5. Avneet Kaur
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Contributions

Amit Kumar and Sandeep Singla designed the model and the computational framework and analysed the data. Amit Kumar and Ajay Kumar carried out the implementation and wrote the manuscript with input from all authors. Aarti Bansal and Avneet Kaur helped in analysis and implementation. Sandeep Singla validated all the results in the manuscript. All authors discussed the results and contributed to the final manuscript.

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Correspondence to Sandeep Singla.

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Kumar, A., Singla, S., Kumar, A. et al. Efficient Prediction of Bridge Conditions Using Modified Convolutional Neural Network. Wireless Pers Commun 125, 29–43 (2022). https://doi.org/10.1007/s11277-022-09539-8

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  • DOI: https://doi.org/10.1007/s11277-022-09539-8

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