Neural Computing and Applications

, Volume 28, Issue 8, pp 2005–2016 | Cite as

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

  • Sankhadeep Chatterjee
  • Sarbartha Sarkar
  • Sirshendu Hore
  • Nilanjan Dey
  • Amira S. Ashour
  • Valentina E. Balas
Original Article


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.


Reinforced concrete structures Structural failure Artificial neural network Particle swarm optimization Multilayer perceptron feed-forward network Scaled conjugate gradient algorithm Cross-entropy 


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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Sankhadeep Chatterjee
    • 1
  • Sarbartha Sarkar
    • 2
  • Sirshendu Hore
    • 3
  • Nilanjan Dey
    • 4
  • Amira S. Ashour
    • 5
  • Valentina E. Balas
    • 6
  1. 1.Department of Computer Science and EngineeringUniversity of CalcuttaKolkataIndia
  2. 2.Department of Civil EngineeringHooghly Engineering and Technology CollegeChinsurahIndia
  3. 3.Department of Computer Science and EngineeringHooghly Engineering and Technology CollegeChinsurahIndia
  4. 4.Department of Information TechnologyTechno India College of TechnologyKolkataIndia
  5. 5.Department of Electronics and Electrical Communications Engineering, Faculty of EngineeringTanta UniversityTantaEgypt
  6. 6.Faculty of EngineeringAurel Vlaicu University of AradAradRomania

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