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Nonstructural components (NSCs) are the systems that are attached to the floors of a building structure. NSCs have become critical in sustaining post-earthquake functionality while constructing seismic-resilient structures. The seismic behavior of the NSCs primarily depends upon the behavior of the structure to which the NSC is attached. Building structures are subjected to earthquake loads and behave differently when the supporting soil type varies. In light of this, this study investigates the seismic demands on NSC attached to the floors of an elastic-reinforced concrete building frame supported by different soil types. The present study considered a regular building frame and a building frame with mass irregularity on the lower story. A total of 3 sets of 11 horizontal spectral-matched ground motions consistent with each soil type are considered. Floor response spectra (FRS) can be used to measure the seismic load on non-structural components. Primarily, it was found that the ordinates of FRS depend on the floor height, the vibration periods of the building, and the soil type. The presence of mass irregularity at the lower story amplified the floor response at all floor levels. Additionally, the values of floor spectral acceleration increase as soil flexibility increases. The amplification factors are critical for generating the floor response spectra, and their variation along the building height is discussed. The floor acceleration was found to vary non-linearly with the height of the building. Finally, artificial neural networks (ANNs) are employed to develop the prediction models for dynamic amplification factors. The results calculated by the dynamic time history analyses are utilized to validate the proposed prediction models.

Moment-independent importance (MII) analysis is known as a global sensitivity measurement in qualifying the influence of uncertainties, which is taken as a crucial step towards seismic performance analysis. Most MII analysis is based on Monte Carlo simulation, which leads to a high computational cost since a large number of nonlinear time history analyses are required to obtain the probability density function. To address this limitation, this study presents a computational efficient MII analysis to investigate the uncertain parameters in the seismic demands of bridges. A modified four-point-estimate method is derived from Rosenblueth’s two-point-estimate method. Thus, the statistical moments of a bridge’s seismic demands can be obtained by several sampling points and their weights. Then, the shifted generalized lognormal distribution method is adopted to estimate the unconditional and conditional probability density functions of seismic demands, which are used for the MII analysis. The analysis of seismic demands based on piers and bearings in a finite element model of a continuous girder bridge is taken as a validation example. The MII measures of the uncertain parameters are estimated by just several nonlinear time history analyses at the point-estimate sampling points, and the results by the proposed method are compared with those found by Monte Carlo simulation.

The complex characteristics of explosion load as well as its increasingly high frequency in the civil environment highlight the need to develop models representing the behavior of structures under blast load. This work presents a probabilistic study of the performance of framed reinforced concrete buildings designed according to the current Italian NTC18 and European EC8 technical standards. First, a simplified single degree of freedom model representing the structural system under blast load has been developed. Then, a probabilistic approach based on Monte Carlo simulation analysis highlighted the influence of seismic demand on the behavior of Reinforced Concrete RC buildings subjected to blast load.