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Structures susceptible to soft story mechanisms are particularly vulnerable to earthquakes because damage concentrated at a single story can lead to premature failure of the structure. The strongback, a stiff vertical spine pinned at the structure’s base and running its height, has been proposed as a way to impose a more uniform pattern of floor displacements and prevent soft story mechanisms. However, changes in the impact of strongbacks on the performance of structures remain unclear when considering vertical stiffness irregularities at different positions along the height of a structure and different arrangements of energy dissipation devices in a structure. This study aims to address these gaps through an extensive parametric experimental investigation varying the location of vertical stiffness irregularities and the arrangement of dampers in a small-scale four-story elastic structure with and without a strongback. For this study, each configuration of the structure is loaded with shake table-produced seismic ground motion. The results of this study show that, regardless of which story a stiffness irregularity is located, the strongback significantly reduces the maximum story drift in the structure. Furthermore, with the strongback, the maximum story and roof drift are insensitive to damper position and distribution, whereas, without it, the damper position significantly impacts the structural performance. The strongback’s ability to protect against soft story vertical irregularities, regardless of their locations, and the insensitivity of structural performance to damper arrangement when utilizing a strongback, presents promising new options for structural design, architectural design, and remediation efforts. 

Many structural systems are susceptible to soft-story instabilities during earthquakes that are lifethreatening and can lead to damage that is too costly to repair. One way to mitigate damage and reduce the potential for soft-story instability is through the addition of an elastic spine that distributes drifts across the height of a structure. One such system is the strongback braced frame, which replaces one side of a buckling-restrained braced frame with a strongback truss. With the strongback providing vertical continuity, an expanded design space is made available for the arrangement of buckling-restrained braces (BRBs) or other energy-dissipating members. An example of this expanded design space is that a designer could opt to not include BRBs at every story. Methods for proportioning the energy-dissipating resistance in strongback braced frames have been proposed. However, most methods don't allow exploitation of the full design space. The objective of this work is to propose and evaluate a potential method of proportioning energy-dissipating members for arbitrary vertical arrangements within strongback braced frames. For a prototypical building, the BRBs are designed in various configurations using existing methods and with the new method. Nonlinear time history analyses of the resulting designs coupled with a rigid strongback are performed and the results are compared. The impacts of overstrength and P-Δ effects are quantified. The findings support the proposed method of BRB design that enables exploration of the wide design space made available by the strongback. 

Rotational inertial mechanisms can produce mass amplification effects with only a small physical mass by converting translation to the rotation of a fly-wheel, which makes them attractive for structural control applications. A variable inertia rotational mechanism (VIRM) is a nonlinear mechanism in which masses in the flywheel can move radially, causing variable inertia. The performance of the VIRM depends on its parameters and the objectives considered. This paper presents the optimum parameters of the VIRM in a single-degree-of-freedom (SDOF) system using an artificial neural network (ANN) model. Optimum VIRM values of several sets of SDOF systems are used to train the ANN model. These values are determined using numerical simulations, and the RMS amplitude of total energy in the system is considered the optimization objective. Numerical simulations of VIRM systems are presented to demonstrate the effective-ness and examine the ANN-based machine learning optimization process's performance.