MODELLING UNKNOWN STRUCTURAL SYSTEMS THROUGH THE USE OF NEURAL NETWORKS

• Published in: Earthquake engineering & structural dynamics Vol. 25; no. 2; pp. 117 - 128
• Main Authors: CHASSIAKOS, A. G., MASRI, S. F.
• Format: Journal Article
• Language: English
• Published: New York John Wiley & Sons, Ltd 01.02.1996; Wiley
• Subjects: Applied sciences; Buildings. Public works; dynamics; Earth sciences; Earth, ocean, space; Earthquakes, seismology; Exact sciences and technology; identification; Internal geophysics; modelling; neural; nonparametric; Stresses. Safety; Structural analysis. Stresses; systems; models; dynamics; Neural network; earthquake engineering; earthquakes; wave damping
• ISSN: 0098-8847; 1096-9845; 1096-9845
• DOI: 10.1002/(SICI)1096-9845(199602)25:2<117::AID-EQE541>3.0.CO;2-A

This paper explores the potential of using neural networks to identify the internal forces of typical systems encountered in the field of earthquake engineering and structural dynamics. After formulating the identification task as a neural network learning procedure, the method is applied to a representative chain‐like system under deterministic and stochastic excitations. The neural network based identification method provides very good results for general classes of multi‐degree‐of‐freedom structural systems. The range of validity of the approach is demonstrated, and some application issues are discussed for (a) partially known multi‐degree‐of‐freedom systems and (b) completely unknown systems.