In this research a seismic retrofit system consisting of a pin-jointed steel frame and rotational friction dampers is developed for the seismic retrofit of reinforced concrete soft-first-story structures, and its efficiency is evaluated through theoretical formulation, cyclic loading test, and analysis of a case study structure. In this system, pin-jointed steel frames are attached to a framed structure at the first story, and a rotational friction damper is installed at each corner of the steel frames. A theoretical formulation is derived for the amplification mechanism of the rotational friction damper and is used to design the geometry of the damper in such a way that its rotation is maximized for a given lateral drift of the structure. The experimental and analysis results show that the proposed system can be used efficiently to prevent the collapse of the case study structure and reduce interstory drift ratios to the code-stipulated limit states.

In previous studies, reinforced concrete (RC) frame–shear wall structures subjected to partial wall removals have been investigated using the nonlinear dynamic alternate path method (APM). However, in the case of an arrested collapse, the APM is not able to obtain the residual capacity. Moreover, less attention has been paid to resisting mechanisms of partially damaged shear walls due to extreme loading on different stories. In the present study a perimeter dual system from a prototype RC frame–shear wall building is investigated using pushdown analysis. The structure is modeled based on a reliable framework established by validating modeling approaches. Load redistribution mechanisms of partially damaged shear walls are indicated and the collapse resistance is quantitatively evaluated following wall (column) removals from different stories. Based on the results, shear walls show considerably high resistance to progressive collapse, which can be implemented in collapse-resistant design. Detailed descriptions of modeling approaches and validations in this study provide guidelines for simulation of different RC structural elements under large deformations.

Viscoelastic polymer composites are widely used for vibration control in different fields of engineering like aerospace, mechanical, and structural engineering. The viscoelastic properties of these materials are strain rate-dependent and are highly related to frequency. Yet to date, less attention has been paid to quantifying the effects of these parameters and their interactions on damping properties and providing an approximation method for further applications. In the present research, a series of experimental tests was conducted on a viscoelastic material and the experimental data were numerically analyzed in detail. Sensitivity analyses are usually applied to quantify uncertainty using sampling techniques. However, in this study a method was proposed to derive a closed-form solution using the response surface function and a derivative-based global sensitivity analysis to evaluate the output contribution of each parameter. These effects were quantified and several approximation statistics were provided for future engineering implementations. The computational evaluation conducted in this study gives a detailed insight into the mechanical behavior of viscoelastic materials.

There have been increasing advances in the sophisticated approaches like fuzzy randomness to handle different uncertainties in civil engineering; however, less attention has been paid to the formulation of a sensitivity analysis for fuzzy random structural systems. In this study, the main objective is to present the formulation of fuzzy Sobol sensitivity indices to quantify the influence of fuzzy random structural parameters. Meanwhile, uncertainty in derivation of limit states and acceptance criteria in collapse analysis is addressed briefly and treated using fuzzy model parameters. To show the application of the established sensitivity test, the collapse behavior of a steel moment frame subjected to sudden column removal is evaluated thoroughly. The proposed fuzzy sensitivity indices are determined for the problem and the overall influence of fuzzy acceptance criteria on the collapse assessment is shown using fragility analysis. The results show that the presented fuzzy sensitivity analysis can give detailed insight into the characteristics of fuzzy random systems, and the epistemic uncertainty in derivation of limit states can have significant effects on the reliability‐based collapse analysis. It is worth mentioning that to alleviate high computational demands in fuzzy probabilistic collapse analysis, a neural network metamodel is applied in conjunction with the genetic algorithm which is also of practical value to engineers and researchers.