The lateral load distributions specified by seismic design provisions are primarily based on elastic behaviour of fixed-base structures without considering the effects of soil-structure-interaction (SSI).Consequently, such load patterns may not be suitable for seismic design of non-linear flexible-base structures. In this paper, a practical optimization technique is introduced to obtain optimum seismic design loads for non-linear shear-buildings on soft soils based on the concept of uniform damage distribution. SSI effects are taken into account by using the cone model. Over 30,000 optimum load patterns are obtained for 21 earthquake excitations recorded on soft soils to investigate the effects of fundamental period of the structure, number of stories, ductility demand, earthquake excitation, damping ratio, damping model, structural post yield behaviour, soil flexibility and structural aspect ratio on the optimum load patterns.The results indicate that the proposed optimum load patterns can significantly improve the seismic performance of flexible-base buildings on soft soils.

This article introduces a robust hybrid computational method for the data‐driven model‐free identification of nonlinear systems that exhibit hysteretic behavior. The proposed approach combines Genetic Programming, which incorporates discontinuous basis functions, for discovering the structure of the governing differential equations, Genetic Algorithms for optimizing the parameters of the differential equations, and Computer Algebra for simplifying mathematical expressions symbolically, and consequently, controlling bloat through condensing dependent terms. A similar technique has been previously proposed by the authors for the identification of nonhysteretic Single Degree of Freedom (SDOF) systems. That technique is extended in this article and is utilized to provide parsimonious differential equations that represent the Bouc–Wen model and the bilinear hysteretic oscillator—both exhibit abrupt change in their memory‐dependent response. The representative models are subjected to validation excitations that are substantially different from the probing signals to confirm the generalizability of the models for different dynamical phenomena. The results verify the effectiveness of the approach and the accuracy of the subsequent differential operators that characterize the behavior of the studied hysteretic systems even under new dynamical conditions. Beside the presented application of this approach, the introduced methodology is more general and can be employed across different disciplines, specifically in data analytics for mathematical modeling of various complex systems.

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