A two-phase neuro-modal linear method for seismic analysis of structures

Abstract

In this paper a two-phase neuro-modal solution for seismic analysis of skeletal structures was developed. Seismic analyses are required to design resisting structures against potential ground motions. Such analyses are, however, computationally intense because of coupled systems of differential equations, time-dependent analyses, uncertainty of seismic loads, and large number of degrees of freedom in high-rise structures. Here, through integration of modal analysis with Long Short-Term Memory neural networks, a method was developed to model and solve dynamic equations of motion more efficiently. Specifically, the method allowed us to convert a time-dependent problem to a recurrent neural network with fixed architecture, functions, and parameters so that the required CPU time for analysis of the problem reduced from order of 10−2 s to the order of 10−5 s for a single degree-of-freedom system under a seismic load. The model was validated through comparison between model predictions and ground truth values obtained from simulated and real data. The correlation between predictions and target values was between 0.98626 and 1 for different loading conditions. This level of accuracy was equivalent to error values ranging from ~ 0 to 1.7% for predicted displacements of the structures under the seismic loads. The developed model can be used to tackle iterative procedures such as design optimizations, risk analysis, Monte Carlo simulations, etc. for large systems such as high-rise skeletal buildings in more efficient time. Also, the proposed platform can be extended to perform vibration and dynamic analyses for continuous systems, plates and shells, bridges, and offshore structures, and under loading conditions such as tornadic wind loads, moving Loads, wave and sea loads, etc.