Optimized seismic retrofit of steel-concrete composite buildings

This is an accepted manuscript of an article published by Elsevier in Engineering Structures on 18/04/2020, available online: https://doi.org/10.1016/j.engstruct.2020.110573 The accepted version of the publication may differ from the final published version.© 2020 Elsevier Ltd This work is focused on comparatively assessing the cost-effectiveness of three seismic retrofit approaches for non-code-conforming frame buildings with steel-concrete composite columns. The first two of the assessed retrofit approaches aim in indirectly enhancing structural system performance by strengthening individual composite columns using reinforced concrete jackets or concrete-covered steel cages. The third retrofit approach considered aims in upgrading the composite building frame at hand by installing steel bracings at selected bays. A specially developed structural optimization procedure is used to perform an objective comparison of the cost-effectiveness of the three retrofit approaches. The objective of the optimization procedure is to minimize the total retrofit material cost, while constraints are imposed to ensure the satisfaction of design requirements for the retrofitted structure regarding member capacities (according to Eurocodes 3 and 4 for steel beams and composite columns, respectively), structural system performance under horizontal loading (based on interstorey drifts calculated by pushover analyses) and fundamental periods (obtained from eigenvalue analyses). By defining 30 cases of under-designed 2-storey, 4-storey and 6-storey composite buildings (i.e. buildings with steel-concrete composite columns), an extensive numerical investigation involving 120 retrofit optimization runs was conducted. The results obtained provide insight into the relative cost-effectiveness of the three seismic retrofit approaches and reveal certain conditions under which each approach is economically most viable.Accepted versio

Optimum design of shell structures with random geometric, material and thickness imperfections

AbstractThe optimum design of isotropic shell structures with random initial geometric, material and thickness imperfections is investigated in this paper and a robust and efficient methodology is presented for treating such problems. For this purpose, the concept of an initial “imperfect” structure is introduced involving not only geometric deviations of the shell structure from its perfect geometry but also a spatial variability of the modulus of elasticity as well as of the thickness of the shell. An efficient reliability-based design optimization (RBDO) formulation is proposed. The objective function is considered to be the weight of the structure while both deterministic and probabilistic constraints are taken into account. The overall probability of failure is taken as the global probabilistic constraint for the optimization procedure. Numerical results are presented for a cylindrical panel, demonstrating the efficiency as well as the applicability of the proposed methodology in obtaining rational optimum designs of imperfect shell-type structures

Soft computing methodologies for structural optimization

The paper examines the efficiency of soft computing techniques in structural optimization, in particular algorithms based on evolution strategies combined with neural networks, for solving large-scale, continuous or discrete structural optimization problems. The proposed combined algorithms are implemented both in deterministic and reliability based structural optimization problems, in an effort to increase the computational efficiency as well as the robustness of the optimization procedure. The use of neural networks was motivated by the time-consuming repeated finite element analyses required during the optimization process. A trained neural network is used to perform either the deterministic constraints check or, in the case of reliability based optimization, both the deterministic and the probabilistic constraints checks. The suitability of the neural network predictions is investigated in a number of structural optimization problems in order to demonstrate the computational advantages of the proposed methodologies