How to cite this paper
Maurya, M., Sadarang, J & Panigrahi, I. (2020). Detection of crack in structure using dynamic analysis and artificial neural network.Engineering Solid Mechanics, 8(3), 285-300.
Refrences
Abd-Elhady, A. (2013). Mixed mode I/II stress intensity factors through the thickness of disc type specimens. Engineering Solid Mechanics, 1(4), 119-128.
Akbardoost, J. (2014). Size and crack length effects on fracture toughness of polycrystalline graphite. Engineering Solid Mechanics, 2(3), 183-192.
Akbardoost, J., Ayatollahi, M. R., Aliha, M. R. M., Pavier, M. J., & Smith, D. J. (2014). Size-dependent fracture behavior of Guiting limestone under mixed mode loading. International Journal of Rock Mechanics and Mining Sciences, 71, 369-380.
Aliha, M. R. M., & Gharehbaghi, H. (2017). The effect of combined mechanical load/welding residual stress on mixed mode fracture parameters of a thin aluminum cracked cylinder. Engineering Fracture Mechanics, 180, 213-228.
Aliha, M. R. M., Berto, F., Bahmani, A., Akhondi, S., & Barnoush, A. (2016). Fracture assessment of polymethyl methacrylate using sharp notched disc bend specimens under mixed mode I+ III loading. Physical Mesomechanics, 19(4), 355-364.
Aliha, M. R. M., Heidari-Rarani, M., Shokrieh, M. M., & Ayatollahi, M. R. (2012). Experimental determination of tensile strength and K (IC) of polymer concretes using semi-circular bend(SCB) specimens. Structural Engineering and Mechanics, 43(6), 823-833.
Aliha, M. R. M., Mahdavi, E., & Ayatollahi, M. R. (2017b). The influence of specimen type on tensile fracture toughness of rock materials. Pure and Applied Geophysics, 174(3), 1237-1253.
Aliha, M. R. M., Razmi, A., & Mansourian, A. (2017a). The influence of natural and synthetic fibers on low temperature mixed mode I+ II fracture behavior of warm mix asphalt (WMA) materials. Engineering Fracture Mechanics, 182, 322-336.
Ayatollahi, M. R., & Aliha, M. R. M. (2011). On the use of an anti‐symmetric four‐point bend specimen for mode II fracture experiments. Fatigue & Fracture of Engineering Materials & Structures, 34(11), 898-907.
Carpinteri, A., & Ingraffea, A. R. (Eds.). (2012). Fracture mechanics of concrete: Material characterization and testing: Material Characterization and Testing (Vol. 3). Springer Science & Business Media.
Dimarogonas, A. D. (1996). Vibration of cracked structures: a state of the art review. Engineering fracture mechanics, 55(5), 831-857.
Fayed, A. (2018). Numerical evaluation of mode I/II SIF of quasi-brittle materials using cracked semi-circular bend specimen. Engineering Solid Mechanics, 6(2), 175-186.
Frommherz, M., Scholz, A., Oechsner, M., Bakan, E., & Vaßen, R. (2016). Gadolinium zirconate/YSZ thermal barrier coatings: Mixed-mode interfacial fracture toughness and sintering behavior. Surface and coatings technology, 286, 119-128.
Ince, R. (2004). Prediction of fracture parameters of concrete by artificial neural networks. Engineering Fracture Mechanics, 71(15), 2143-2159.
Li, H., He, C., Ji, J., Wang, H., & Hao, C. (2005). Crack damage detection in beam-like structures using RBF neural networks with experimental validation. International Journal of Innovative Computing Information and Control, 1(4), 625-634.
Mahdavi, E., Obara, Y., & Ayatollahi, M. (2015). Numerical investigation of stress intensity factor for semi-circular bend specimen with chevron notch. Engineering Solid Mechanics, 3(4), 235-244.
Mirsayar, M. M., Razmi, A., Aliha, M. R. M., & Berto, F. (2018). EMTSN criterion for evaluating mixed mode I/II crack propagation in rock materials. Engineering Fracture Mechanics, 190, 186-197.
Mirsayar, M., Shi, X., & Zollinger, D. (2017). Evaluation of interfacial bond strength between Portland cement concrete and asphalt concrete layers using bi-material SCB test specimen. Engineering Solid Mechanics, 5(4), 293-306.
Nasiri, S., Khosravani, M. R., & Weinberg, K. (2017). Fracture mechanics and mechanical fault detection by artificial intelligence methods: A review. Engineering Failure Analysis, 81, 270-293.
Pan, D. G., Lei, S. S., & Wu, S. C. (2010, October). Two-stage damage detection method using the artificial neural networks and genetic algorithms. In International Conference on Information Computing and Applications (pp. 325-332). Springer, Berlin, Heidelberg.
Park, J. H., Kim, J. T., Hong, D. S., Ho, D. D., & Yi, J. H. (2009). Sequential damage detection approaches for beams using time-modal features and artificial neural networks. Journal of Sound and Vibration, 323(1-2), 451-474.
Prabhakar, M. S. (2009). Vibration analysis of cracked beam (Doctoral dissertation, Master Thesis, National Institute of Technology Rourkela, Rourkela).
Rosales, M. B., Filipich, C. P., & Buezas, F. S. (2009). Crack detection in beam-like structures. Engineering Structures, 31(10), 2257-2264.
Rossi, P., & Le Maou, F. (1989). New method for detecting cracks in concrete using fibre optics. Materials and structures, 22(6), 437-442.
Sahin, M., & Shenoi, R. A. (2003). Quantification and localisation of damage in beam-like structures by using artificial neural networks with experimental validation. Engineering Structures, 25(14), 1785-1802.
Satpute, D., Baviskar, P., Gandhi, P., Chavanke, M., & Aher, T. (2017). Crack detection in cantilever shaft beam using natural frequency. Materials Today: Proceedings, 4(2), 1366-1374.
Scholey, J. J., Wilcox, P. D., Wisnom, M. R., Friswell, M. I., Pavier, M., & Aliha, M. R. (2009). A GENERIC TECHNIQUE FOR ACOUSTIC EMISSION SOURCE LOCATION. Journal of Acoustic Emission, 27.
Suresh, S., Omkar, S. N., Ganguli, R., & Mani, V. (2004). Identification of crack location and depth in a cantilever beam using a modular neural network approach. Smart Materials and Structures, 13(4), 907.
Sutar, M. K., Pattnaik, S., & Rana, J. (2015). Neural Based Controller for Smart Detection of Crack in Cracked Cantilever Beam. Materials Today: Proceedings, 2(4-5), 2648-2653.
Taheri-Behrooz, F., Aliha, M. R., Maroofi, M., & Hadizadeh, V. (2018). Residual stresses measurement in the butt joint welded metals using FSW and TIG methods. Steel and Composite Structures, 28(6), 759-766.
Tan, Z. X., Thambiratnam, D. P., Chan, T. H. T., & Razak, H. A. (2017). Detecting damage in steel beams using modal strain energy based damage index and Artificial Neural Network. Engineering Failure Analysis, 79, 253-262.
Teidj, S., Khamlichi, A., & Driouach, A. (2016). Identification of beam cracks by solution of an inverse problem. Procedia Technology, 22, 86-93.
Thatoi, D. N., Choudhury, S., Das, H. C., Jena, P. K., & Agrawal, G. (2014). CFBP Network–A Technique for Crack Detection. Procedia materials science, 6, 10-17.
Vakil-Baghmisheh, M. T., Peimani, M., Sadeghi, M. H., & Ettefagh, M. M. (2008). Crack detection in beam-like structures using genetic algorithms. Applied soft computing, 8(2), 1150-1160.
Wang, D., Zhang, H., Gong, B., & Deng, C. (2016). Residual stress effects on fatigue behaviour of welded T-joint: a finite fracture mechanics approach. Materials & Design, 91, 211-217.
Wang, Q., Liu, X., Wang, W., Yang, C., Xiong, X., & Fang, H. (2017). Mixed mode fatigue crack growth behavior of Ni-Cr-Mo-V high strength steel weldments. International Journal of Fatigue, 102, 79-91.
Akbardoost, J. (2014). Size and crack length effects on fracture toughness of polycrystalline graphite. Engineering Solid Mechanics, 2(3), 183-192.
Akbardoost, J., Ayatollahi, M. R., Aliha, M. R. M., Pavier, M. J., & Smith, D. J. (2014). Size-dependent fracture behavior of Guiting limestone under mixed mode loading. International Journal of Rock Mechanics and Mining Sciences, 71, 369-380.
Aliha, M. R. M., & Gharehbaghi, H. (2017). The effect of combined mechanical load/welding residual stress on mixed mode fracture parameters of a thin aluminum cracked cylinder. Engineering Fracture Mechanics, 180, 213-228.
Aliha, M. R. M., Berto, F., Bahmani, A., Akhondi, S., & Barnoush, A. (2016). Fracture assessment of polymethyl methacrylate using sharp notched disc bend specimens under mixed mode I+ III loading. Physical Mesomechanics, 19(4), 355-364.
Aliha, M. R. M., Heidari-Rarani, M., Shokrieh, M. M., & Ayatollahi, M. R. (2012). Experimental determination of tensile strength and K (IC) of polymer concretes using semi-circular bend(SCB) specimens. Structural Engineering and Mechanics, 43(6), 823-833.
Aliha, M. R. M., Mahdavi, E., & Ayatollahi, M. R. (2017b). The influence of specimen type on tensile fracture toughness of rock materials. Pure and Applied Geophysics, 174(3), 1237-1253.
Aliha, M. R. M., Razmi, A., & Mansourian, A. (2017a). The influence of natural and synthetic fibers on low temperature mixed mode I+ II fracture behavior of warm mix asphalt (WMA) materials. Engineering Fracture Mechanics, 182, 322-336.
Ayatollahi, M. R., & Aliha, M. R. M. (2011). On the use of an anti‐symmetric four‐point bend specimen for mode II fracture experiments. Fatigue & Fracture of Engineering Materials & Structures, 34(11), 898-907.
Carpinteri, A., & Ingraffea, A. R. (Eds.). (2012). Fracture mechanics of concrete: Material characterization and testing: Material Characterization and Testing (Vol. 3). Springer Science & Business Media.
Dimarogonas, A. D. (1996). Vibration of cracked structures: a state of the art review. Engineering fracture mechanics, 55(5), 831-857.
Fayed, A. (2018). Numerical evaluation of mode I/II SIF of quasi-brittle materials using cracked semi-circular bend specimen. Engineering Solid Mechanics, 6(2), 175-186.
Frommherz, M., Scholz, A., Oechsner, M., Bakan, E., & Vaßen, R. (2016). Gadolinium zirconate/YSZ thermal barrier coatings: Mixed-mode interfacial fracture toughness and sintering behavior. Surface and coatings technology, 286, 119-128.
Ince, R. (2004). Prediction of fracture parameters of concrete by artificial neural networks. Engineering Fracture Mechanics, 71(15), 2143-2159.
Li, H., He, C., Ji, J., Wang, H., & Hao, C. (2005). Crack damage detection in beam-like structures using RBF neural networks with experimental validation. International Journal of Innovative Computing Information and Control, 1(4), 625-634.
Mahdavi, E., Obara, Y., & Ayatollahi, M. (2015). Numerical investigation of stress intensity factor for semi-circular bend specimen with chevron notch. Engineering Solid Mechanics, 3(4), 235-244.
Mirsayar, M. M., Razmi, A., Aliha, M. R. M., & Berto, F. (2018). EMTSN criterion for evaluating mixed mode I/II crack propagation in rock materials. Engineering Fracture Mechanics, 190, 186-197.
Mirsayar, M., Shi, X., & Zollinger, D. (2017). Evaluation of interfacial bond strength between Portland cement concrete and asphalt concrete layers using bi-material SCB test specimen. Engineering Solid Mechanics, 5(4), 293-306.
Nasiri, S., Khosravani, M. R., & Weinberg, K. (2017). Fracture mechanics and mechanical fault detection by artificial intelligence methods: A review. Engineering Failure Analysis, 81, 270-293.
Pan, D. G., Lei, S. S., & Wu, S. C. (2010, October). Two-stage damage detection method using the artificial neural networks and genetic algorithms. In International Conference on Information Computing and Applications (pp. 325-332). Springer, Berlin, Heidelberg.
Park, J. H., Kim, J. T., Hong, D. S., Ho, D. D., & Yi, J. H. (2009). Sequential damage detection approaches for beams using time-modal features and artificial neural networks. Journal of Sound and Vibration, 323(1-2), 451-474.
Prabhakar, M. S. (2009). Vibration analysis of cracked beam (Doctoral dissertation, Master Thesis, National Institute of Technology Rourkela, Rourkela).
Rosales, M. B., Filipich, C. P., & Buezas, F. S. (2009). Crack detection in beam-like structures. Engineering Structures, 31(10), 2257-2264.
Rossi, P., & Le Maou, F. (1989). New method for detecting cracks in concrete using fibre optics. Materials and structures, 22(6), 437-442.
Sahin, M., & Shenoi, R. A. (2003). Quantification and localisation of damage in beam-like structures by using artificial neural networks with experimental validation. Engineering Structures, 25(14), 1785-1802.
Satpute, D., Baviskar, P., Gandhi, P., Chavanke, M., & Aher, T. (2017). Crack detection in cantilever shaft beam using natural frequency. Materials Today: Proceedings, 4(2), 1366-1374.
Scholey, J. J., Wilcox, P. D., Wisnom, M. R., Friswell, M. I., Pavier, M., & Aliha, M. R. (2009). A GENERIC TECHNIQUE FOR ACOUSTIC EMISSION SOURCE LOCATION. Journal of Acoustic Emission, 27.
Suresh, S., Omkar, S. N., Ganguli, R., & Mani, V. (2004). Identification of crack location and depth in a cantilever beam using a modular neural network approach. Smart Materials and Structures, 13(4), 907.
Sutar, M. K., Pattnaik, S., & Rana, J. (2015). Neural Based Controller for Smart Detection of Crack in Cracked Cantilever Beam. Materials Today: Proceedings, 2(4-5), 2648-2653.
Taheri-Behrooz, F., Aliha, M. R., Maroofi, M., & Hadizadeh, V. (2018). Residual stresses measurement in the butt joint welded metals using FSW and TIG methods. Steel and Composite Structures, 28(6), 759-766.
Tan, Z. X., Thambiratnam, D. P., Chan, T. H. T., & Razak, H. A. (2017). Detecting damage in steel beams using modal strain energy based damage index and Artificial Neural Network. Engineering Failure Analysis, 79, 253-262.
Teidj, S., Khamlichi, A., & Driouach, A. (2016). Identification of beam cracks by solution of an inverse problem. Procedia Technology, 22, 86-93.
Thatoi, D. N., Choudhury, S., Das, H. C., Jena, P. K., & Agrawal, G. (2014). CFBP Network–A Technique for Crack Detection. Procedia materials science, 6, 10-17.
Vakil-Baghmisheh, M. T., Peimani, M., Sadeghi, M. H., & Ettefagh, M. M. (2008). Crack detection in beam-like structures using genetic algorithms. Applied soft computing, 8(2), 1150-1160.
Wang, D., Zhang, H., Gong, B., & Deng, C. (2016). Residual stress effects on fatigue behaviour of welded T-joint: a finite fracture mechanics approach. Materials & Design, 91, 211-217.
Wang, Q., Liu, X., Wang, W., Yang, C., Xiong, X., & Fang, H. (2017). Mixed mode fatigue crack growth behavior of Ni-Cr-Mo-V high strength steel weldments. International Journal of Fatigue, 102, 79-91.