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Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings

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Abstract

Faulty structural design may cause multistory reinforced concrete (RC) buildings to collapse suddenly. All attempts are directed to avoid structural failure as it leads to human life danger as well as wasting time and property. Using traditional methods for predicting structural failure of the RC buildings will be time-consuming and complex. Recent research proved the artificial neural network (ANN) potentiality in solving various real-life problems. The traditional learning algorithms suffer from being trapped into local optima with a premature convergence. Thus, it is a challenging task to achieve expected accuracy while using traditional learning algorithms to train ANN. To solve this problem, the present work proposed a particle swarm optimization-based approach to train the NN (NN-PSO). The PSO is employed to find a weight vector with minimum root-mean-square error (RMSE) for the NN. The proposed (NN-PSO) classifier is capable to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. A database of 150 multistoried buildings’ RC structures was employed in the experimental results. The PSO algorithm was involved to select the optimal weights for the NN classifier. Fifteen features have been extracted from the structural design, while nine features have been opted to perform the classification process. Moreover, the NN-PSO model was compared with NN and MLP-FFN (multilayer perceptron feed-forward network) classifier to find its ingenuity. The experimental results established the superiority of the proposed NN-PSO compared to the NN and MLP-FFN classifiers. The NN-PSO achieved 90 % accuracy with 90 % precision, 94.74 % recall and 92.31 % F-Measure.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Calcutta, Kolkata, India

    Sankhadeep Chatterjee

  2. Department of Civil Engineering, Hooghly Engineering and Technology College, Chinsurah, India

    Sarbartha Sarkar

  3. Department of Computer Science and Engineering, Hooghly Engineering and Technology College, Chinsurah, India

    Sirshendu Hore

  4. Department of Information Technology, Techno India College of Technology, Kolkata, India

    Nilanjan Dey

  5. Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt

    Amira S. Ashour

  6. Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina E. Balas

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  1. Sankhadeep Chatterjee
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  2. Sarbartha Sarkar
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  3. Sirshendu Hore
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  4. Nilanjan Dey
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  5. Amira S. Ashour
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  6. Valentina E. Balas
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Correspondence to Amira S. Ashour.

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Chatterjee, S., Sarkar, S., Hore, S. et al. Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Comput & Applic 28, 2005–2016 (2017). https://doi.org/10.1007/s00521-016-2190-2

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  • DOI: https://doi.org/10.1007/s00521-016-2190-2

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