Advertisement

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

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.

Keywords

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

References

  1. 1.
    McEntire DA (2014) Disaster response and recovery: strategies and tactics for resilience. John Wiley & Sons, HobokenGoogle Scholar
  2. 2.
    Fayyadh MM, Abdul Razak H (2011) Stiffness reduction index for detection of damage location: analytical study. Int J Phys Sci 6(9):2194–2204Google Scholar
  3. 3.
    Jiang X, Adeli H (2007) Pseudospectra, MUSIC, and dynamic wavelet neural network for damage detection of highrise buildings. Int J Numer Meth Eng 71(5):606–629CrossRefzbMATHGoogle Scholar
  4. 4.
    Chen B, Liu W (2010) Mobile agent computing paradigm for building a flexible structural health monitoring sensor network. Comput Aided Civil Infrastruct Eng 25(7):504–516CrossRefGoogle Scholar
  5. 5.
    Stratman B, Mahadevan S, Li C, Biswas G (2011) Identification of critical inspection samples among railroad wheels by similarity-based agglomerative clustering. Integr Comput Aided Eng 18(3):203–219Google Scholar
  6. 6.
    Caglar N, Elmas M, Yaman ZD, Saribiyik M (2008) Neural networks in 3-dimensional dynamic analysis of reinforced concrete buildings. Constr Build Mater 22(5):788–800CrossRefGoogle Scholar
  7. 7.
    Han J, Kamber M (2005) Data mining: concepts and techniques, 2nd edn. Morgan and Kaufmann, San Francisco, pp 285–378Google Scholar
  8. 8.
    Pierce S, Worden K, Manson G (2006) A novel information-gap technique to assess reliability of neural network-based damage detection. J Sound Vib 293(1–2):96–111CrossRefGoogle Scholar
  9. 9.
    Chen J-F, Do QH, Hsieh H-N (2015) Training artificial neural networks by a hybrid PSO-CS algorithm. Algorithms 8:292–308MathSciNetCrossRefGoogle Scholar
  10. 10.
    Nanda SJ, Panda G (2014) A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evolut Comput 16:1–18CrossRefGoogle Scholar
  11. 11.
    Rahmanian B, Pakizeh M, Mansoori SAA, Esfandyari M, Jafari D, Maddah H, Maskooki A (2012) Prediction of MEUF process performance using artificial neural networks and ANFIS approaches. J Taiwan Inst Chem Eng 43(4):558–565CrossRefGoogle Scholar
  12. 12.
    Faruk DÖ (2010) A hybrid neural network and ARIMA model for water quality time series prediction. Eng Appl Artif Intell 23(4):586–594MathSciNetCrossRefGoogle Scholar
  13. 13.
    Hajela P, Berke L (1991) Neurobiological computational models in structural analysis and design. Comput Struct 41(4):657–667CrossRefzbMATHGoogle Scholar
  14. 14.
    Adeli H, Park HS (1995) A neural dynamics model for structural optimization—theory. Comput Struct 57(3):383–390MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Mukherjee A, Despande JM (1995) Modeling initial design process using artificial neural networks. J Comput Civ Eng 9(3):194–200CrossRefGoogle Scholar
  16. 16.
    Adeli H, Karim A (1997) Neural network model for optimization of cold-formed steel beams. J Struct Eng 123(11):1535–1543CrossRefGoogle Scholar
  17. 17.
    Park HS, Adeli H (1997) Distributed neural dynamics algorithms for optimization of large steel structures. J Struct Eng 123(7):880–888CrossRefGoogle Scholar
  18. 18.
    Elazouni AM, Nosair IA, Mohieldin YA, Mohamed AG (1997) Estimating resource requirements at conceptual stage using neural networks. J Comput Civ Eng 11(4):217–223CrossRefGoogle Scholar
  19. 19.
    Hadi MNS (2003) Neural network applications in concrete structures. Comput Struct 81(6):373–381CrossRefGoogle Scholar
  20. 20.
    Gupta R, Kewalramani M, Goel A (2006) Prediction of concrete strength using neural-expert system. J Mater Civ Eng 18(3):462–466CrossRefGoogle Scholar
  21. 21.
    Graf W, Freitag S, Kaliske M, Sickert JU (2010) Recurrent neural networks for uncertain time-dependent structural behavior. Comput Aided Civ Infrastruct Eng 25(5):322–333CrossRefGoogle Scholar
  22. 22.
    Erdem H (2010) Prediction of moment capacity of reinforced concrete slabs in fire using artificial neural networks. Adv Eng Softw 41(2):270–276CrossRefzbMATHGoogle Scholar
  23. 23.
    Bagci M (2010) Neural network model for moment-curvature relationship of reinforced concrete sections. Math Comput Appl 15(1):66–78zbMATHGoogle Scholar
  24. 24.
    Jakubek M (2012) Neural network prediction of load capacity for eccentrically loaded reinforced concrete columns. Comput Assist Methods Eng Sci 19:339–349Google Scholar
  25. 25.
    Lagaros ND, Papadrakakis M (2012) Neural network based prediction schemes of the non-linear seismic response of 3D buildings. Adv Eng Softw 44(1):92–115CrossRefGoogle Scholar
  26. 26.
    Maizir H, Kassim KA (2013) Neural network application in prediction of axial bearing capacity of driven piles. Proc Int Multiconf Eng Comput Sci 2202(1):51–55Google Scholar
  27. 27.
    Uddi M, Jameel M, Abdul Razak H (2015) Application of artificial neural network in fixed offshore structures. Indian J Mar Sci 44:3Google Scholar
  28. 28.
    Joghataie A, Mojtaba F (2008) Dynamic analysis of nonlinear frames by Prandtl neural networks. J Eng Mech 134(11):961–969CrossRefGoogle Scholar
  29. 29.
    Plevris V, Papadrakakis M (2011) A hybrid particle swarm-gradient algorithm for global structural optimization. Comput Aided Civ Infrastruct Eng 26(1):48–68Google Scholar
  30. 30.
    Standard, Indian (2000) ‘IS-456. 2000’ Plain and Reinforced Concrete-Code of Practice. Bureau of Indian Standards Manak Bhavan. 9 Bahadur Shah Zafar Marg New Delhi 110002Google Scholar
  31. 31.
    Maren AJ, Harston CT, Pap RM (2014) Handbook of neural computing applications. Academic Press, San DiegozbMATHGoogle Scholar
  32. 32.
    Baughman DR, Liu YA (2014) Neural networks in bioprocessing and chemical engineering. Academic press, San DiegoGoogle Scholar
  33. 33.
    Rojas R (2013) Neural networks: a systematic introduction. Springer Science & Business Media, BerlinzbMATHGoogle Scholar
  34. 34.
    Dash RN, Subudhi B, Das S. (2010) A comparison between MLP NN and RBF NN techniques for the detection of stator inter-turn fault of an induction motor. In: 2010 International conference on industrial electronics, control and robotics (IECR), pp 251–256Google Scholar
  35. 35.
    CoelloCoello CA, Pulido GT (2005) Multiobjective structural optimization using a microgenetic algorithm. Struct Multidiscip Optim 30(5):388–403CrossRefGoogle Scholar
  36. 36.
    Kameli I, Miri M, Raji A (2011) Prediction of target displacement of reinforced concrete frames using artificial neural networks. Adv Mater Res 255:2345–2349CrossRefGoogle Scholar
  37. 37.
    Berardi VL, Kline DM (2005) Revisiting squared-error and cross-entropy functions for training neural network classifiers. Neural Comput Appl 14(4):310–318CrossRefGoogle Scholar
  38. 38.
    Zhang T (2009) On the consistency of feature selection using greedy least squares regression. J Mach Learn Res 10:555–568MathSciNetzbMATHGoogle Scholar
  39. 39.
    Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182zbMATHGoogle Scholar
  40. 40.
    Karayiannis N, Venetsanopoulos AN (2013) Artificial neural networks: learning algorithms, performance evaluation, and applications. Springer Science & Business Media, New YorkzbMATHGoogle Scholar
  41. 41.
    Socha K, Blum C (2007) An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training. Neural Comput Appl 16(3):235–247CrossRefGoogle Scholar
  42. 42.
    Nguyen NT, Lim CP, Jain LC, Balas VE (2009) Theoretical advances and applications of intelligent paradigms. J Intell Fuzzy Syst 20:1–2Google Scholar
  43. 43.
    Dehuri S, Cho SB (2010) A hybrid genetic based functional link artificial neural network with a statistical comparison of classifiers over multiple datasets. Neural Comput Appl 19(2):317–328CrossRefGoogle Scholar
  44. 44.
    MacIntyre J (2013) Applications of neural computing in the twenty-first century and 21 years of neural computing and applications. Neural Comput Appl 23(3–4):657–665CrossRefGoogle Scholar
  45. 45.
    Azar AT, El-Said SA, Balas VE, Olariu T (2013) Linguistic hedges fuzzy feature selection for differential diagnosis of Erythemato-Squamous diseases. Soft Comput Appl AISC 195:487–500CrossRefGoogle Scholar
  46. 46.
    Dey N, Samanta S, Yang X-S, Chaudhri SS, Das A (2013) Optimization of scaling factors in electrocardiogram signal watermarking using cuckoo search. Int J Bio Inspired Comput (IJBIC) 5(5):315–326CrossRefGoogle Scholar
  47. 47.
    Chakraborty S, Samanta S, Mukherjee A, Dey N, Chaudhuri SS (2013) Particle swarm optimization based parameter optimization technique in medical information hiding. In: 2013 IEEE international conference on computational intelligence and computing research (ICCIC), Madurai, 26–28 Dec 2013Google Scholar
  48. 48.
    Awan SM, Aslam M, Khan ZA, Saeed H (2014) An efficient model based on artificial bee colony optimization algorithm with neural networks for electric load forecasting. Neural Comput Appl 25(7–8):1967–1978CrossRefGoogle Scholar
  49. 49.
    Siddiquee MSA, Hossain MMA (2015) Development of a sequential artificial neural network for predicting river water levels based on Brahmaputra and Ganges water levels. Neural Comput Appl 26(8):1979–1990CrossRefGoogle Scholar
  50. 50.
    Cao Z, Cheng L, Zhou C, Gu N, Wang X, Tan M (2015) Spiking neural network-based target tracking control for autonomous mobile robots. Neural Comput Appl 26(8):1839–1847CrossRefGoogle Scholar
  51. 51.
    Gao S, Ning B, Dong H (2015) Adaptive neural control with intercepted adaptation for time-delay saturated nonlinear systems. Neural Comput Appl 26(8):1849–1857CrossRefGoogle Scholar
  52. 52.
    Kausar N, Palaniappan S, AlGhamdi BS, Samir BB, Dey N, Abdullah A (2015) Systematic analysis of applied data mining based optimization algorithms in clinical attribute extraction and classification for diagnosis of cardiac patients. Appl Intell Optim Biol Med Ser Intell Syst Ref Libr 96:217–231Google Scholar
  53. 53.
    Ciancio C, Ambrogio G, Gagliardi F, Musmanno R (2015) Heuristic techniques to optimize neural network architecture in manufacturing applications. Neural Comput Appl. doi: 10.1007/s00521-015-1994-9 Google Scholar
  54. 54.
    Mirjalili SZ, Saremi S, Mirjalili SM (2015) Designing evolutionary feedforward neural networks using social spider optimization algorithm. Neural Comput Appl 26(8):1919–1928CrossRefGoogle Scholar
  55. 55.
    Arslan MH (2010) An evaluation of effective design parameters on earthquake performance of RC buildings using neural networks. Eng Struct 32(7):1888–1898CrossRefGoogle Scholar
  56. 56.
    Arslan MH, Ceylan M, Koyuncu T (2012) An ANN approaches on estimating earthquake performances of existing RC buildings. Neural Netw World 22(5):443CrossRefGoogle Scholar
  57. 57.
    Kia A, Sensoy S (2014) Classification of earthquake-induced damage for R/C slab column frames using multiclass SVM and its combination with MLP neural network. Math Probl Eng 2014:1–14CrossRefGoogle Scholar
  58. 58.
    Arslan MH, Ceylan M, Koyuncu T (2015) Determining earthquake performances of existing reinforced concrete buildings by using ANN. World Acad Sci Eng Technol Int J Civ Environ Struct Constr Archit Eng 9(8):921–925Google Scholar

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

Personalised recommendations