A Review on Structural Failure of Bridges under Seismic Impact Employing Statistical Machine Learning Models

It can be concluded from previous discussions that it is mandatory to make a quick estimation and evaluation of the damages is necessary so as to immediately stop the cascading effect of the possible failure and casualties. This paper presents the basis of the employment of statistical and machine leaning based techniques for the estimation of bridge failure in the presence of seismic effects. The salient points pertaining to the contemporary literature in the domain has bene cited along with the brief description of the model being used. Finally the evaluation metrics for the techniques employed have been discussed.

1.                AC Neves, I Gonzalez, J Leander, “Structural health monitoring of bridges: a model-free ANN-based approach to damage detection”, Journal of Civil Structural Health Monitoring, Springer 2017, vol. 7, pp.689–702. 2.                J. Peng, S. Zhang, D. Peng and K. Liang, "Application of machine learning method in bridge health monitoring," 2017 Second International Conference on Reliability Systems Engineering (ICRSE), 2017, pp. 1-7. 3.                EK Chalouhi, I Gonzalez, C Gentile, R Karoumi -, “Damage detection in railway bridges using machine learning: application to a historic structure”, Procedia Engineering, Elsevier 2017, vol.199, pp. 1931-1936. 4.                M Alipour, DK Harris, LE Barnes, “Load-capacity rating of bridge populations through machine learning: Application of decision trees and random forests”, Journal of Bridge Engineering 2017, vol.22, issue.10. 5.                A Anaissi, NLD Khoa, S Mustapha, “Adaptive one-class support vector machine for damage detection in structural health monitoring”, Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017: Advances in Knowledge Discovery and Data Mining, Springer 2017, pp 42-57. 6.                A Diez, NLD Khoa, MM Alamdari, Y Wang, “A clustering approach for structural health monitoring on bridges”, Journal of Civil Structural Health Monitoring, Springer 2016, vol. 6, pp.429–445. 7.                M Malekzadeh, FN Catbas, “A machine learning framework for automated functionality monitoring of movable bridges”, Dynamics of Civil Structures, Springer 2016, vol. 2, pp 57-63. 8.                H Li, J Ou, “The state of the art in structural health monitoring of cable-stayed bridges”, Journal of Civil Structural Health Monitoring, Springer 2016, vol. 6, pp.43–67. 9.                R Ghiasi, P Torkzadeh, M Noori, “A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function”, Journal of Health Monitoring, SAGE publications, vol. 15, issue. 3, pp.302-316. 10.             D. Sun, V. C. S. Lee and Y. Lu, "An intelligent data fusion framework for structural health monitoring," 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), 2016, pp. 49-54.