Prediction of temperature difference effect in the buckling of a bi-material column with interface crack using ANN and FE

Hosseinzadeh, A.; Rezaeiha, M.

Abstract: Buckling is one of the most complicated concepts in mechanical engineering. Buckling often happens by compressive loads on thin structures. Thermal gradient between two ends of a column may cause a deflection in it. This will add an extra deformation to the one provided by compressive loads on the column. This phenomenon occurs when two ends of the column are at different temperatures, which can be seen at various structures. Because of the considered temperature gradients, the critical load of the column will decrease. In the current paper, various columns are modeled and the effect of thermal gradient and compressive load and other parameters on the Bi-material columns are studied. In other words, influences of compressive load and temperature gradient on critical load of Bi-material columns with interface crack are investigated. Effect of change in each parameter on critical load of column and crack opening was investigated. First, the thermal gradient was only applied to the model and in the next step; only the effect of mechanical loading was studied. Furthermore, artificial neural network (ANN) was used to extend the results to a bigger range of temperature conditions through the columns. Based on the results, ANN and finite element results are in a good agreement and the thermal effects may have a significant role in buckling of the column.

How to cite this paper

 Hosseinzadeh, A & Rezaeiha, M. (2014). Prediction of temperature difference effect in the buckling of a bi-material column with interface crack using ANN and FE.Engineering Solid Mechanics, 2(1), 15-20.

References

ABAQUS/standard user’s manual, Hibbitt, K. (2003). Sorensen Inc. version 6.4. 1. Pawtucket, RI: Hibbitt, Karlsson, & Sorensen.

AISC. (2001) Manual of steel construction, load and resistance factor design. Chicago, IL: American Institute of Steel Construction.

Al-Haik, M. S., Hussaini, M. Y., & Garmestani, H. (2006). Prediction of nonlinear viscoelastic behavior of polymeric composites using an artificial neural network. International journal of plasticity, 22(7), 1367-1392.

BSSC. (2000). NEHRP recommended provisions for the development of seismic regulations for new buildings and other structures, Federal Emergency Management Agency, Washington, DC; 2000.

Easley, J. T., & McFarland, D. E. (1969). Buckling of light-gage corrugated metal shear diaphragms. Journal of the Structural Division, ASCE, 95(7), 1497-1516.

EUROCODE (1995). “Projet de l’EUROCODE No”. 8: Re`gles unifie´es communes pour les constructions en zones sismiques. Commission des Communaute´s Europe´ennes, Bruxelles.

ICC. (2000) International building code. Falls Church, Virginia: International Code Council; 2000.

Kim, K. B., Yoon, D. J., Jeong, J. C., & Lee, S. S. (2004). Determining the stress intensity factor of a material with an artificial neural network from acoustic emission measurements. NDT & E International, 37(6), 423-429.

Koiter, WT., (1974). A consistent first approximation in general theory of buckling of structures, In: IUTAM Symposium, Harvard University, 133–147.

Sirat, M., & Talbot, C. J. (2001). Application of artificial neural networks to fracture analysis at the ?sp? HRL, Sweden: fracture sets classification. International Journal of Rock Mechanics and Mining Sciences, 38(5), 621-639.