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The motivation for using artificial neural networks in this study stems from their computational efficiency and ability to model complex, high-level abstractions. Deep learning models were utilized to predict the structural responses of reinforced concrete (RC) buildings subjected to earthquakes. For this aim, the dataset for training and evaluation was derived from complex computational dynamic analyses, which involved scaling real ground motion records at different intensity levels (spectral acceleration Sa(T1) and the recently proposed INp). The results, specifically the maximum interstory drifts, were characterized for the output neurons in terms of their corresponding statistical parameters: mean, median, and standard deviation; while two input variables (fundamental period and earthquake intensity) were used in the neural networks to represent buildings and seismic risk. To validate deep learning as a robust tool for seismic predesign and rapid estimation, a prediction model was developed to assess the seismic performance of a complex RC building with buckling restrained braces (RC-BRBs). Additionally, other deep learning models were explored to predict ductility and hysteretic energy in nonlinear single degree of freedom (SDOF) systems. The findings demonstrated that increasing the number of hidden layers generally reduces prediction error, although an excessive number can lead to overfitting.

In this work, reliability-based ductility reduction factors surfaces are obtained through uniform annual failure rate (UAFR) spectra. For this aim, several nonlinear single degree of freedom (SDOF) systems that include various hysteretic behavior models, constant ductility performance and structural reliability levels are subjected to two sets of ground motions recorded on Mexico City. The first set includes 50 ground motion records located on stiff soils, while the second set correspond to 50 soft soil ground motions. In order to compute the UAFR spectra, the ground motion records were scaled at different values of a particular case of the generalized intensity measure IBg named INp for spectral acceleration. The study discusses the influence of different post-yielding stiffness on UAFR spectra obtained for the selected Bojorquez intensity measure, through the bilinear hysteretic model. Additionally, the Takeda model is used to represent structures that exhibit the effect of stiffness degradation. This study is aimed to compute the effect to obtain ductility reduction factors for various uniform annual failure rates, different degrees of post-yielding stiffness, and different ductility capacity for various nonlinear structures. The analyses of the results indicate that strength reduction (through ductility reduction factors) is mainly influenced by parameters such as ductility, failure rate, period of the system and soil conditions. Finally, reliability-based ductility reduction factor surfaces for stiff and soft soil are provided for different UAFR. It is important to say that this is the first time that ductility reduction factors are proposed based on IBg or INp as intensity measure.

In this study, the seismic responses of moment-resisting steel frames (MRSFs) with welded and post-tensioned connections under 28 artificial seismic sequences (mainshock–aftershock) are compared. For this aim, the mainshock are scaled at different ground motion intensity levels as a function of the spectral pseudo-acceleration corresponding to the fundamental period of vibration of the structure Sa(T1), whereas different intensity levels of the aftershocks are used for a percentage of the peak maximum acceleration of the mainshock. The seismic performance comparison of both structural systems is computed for the maximum, residual inter-story drift and hysteretic energy demands. The results show that post-tensioned frames significantly reduce the structural demands, especially in the case of residual inter-story drifts and hysteretic energy in comparison with moment-resisting steel frames with welded connections. The reductions in the structural response tend to be larger as the intensity of the aftershock tends to increase. Therefore, it is concluded that the use of post-tensioned connections is a great alternative to mitigate the seismic response of buildings subjected to seismic sequences.

Although some studies have been conducted to compute fragility surfaces of buildings using vector-valued seismic intensity measures (IMs), in all the cases, the first component of the vector usually is the spectral acceleration at first mode of vibration of the structure Sa(T1). In this study, fragility surfaces of three reinforced concrete buildings (RCB) subjected to narrow-band ground motions obtained from soft soil of Mexico City are computed considering vector-valued IMs based not only on Sa(T1), but also the velocity V(T1), pseudo-velocity Sv(T1), and normalized input energy by the mass EI/m(T1) as the first component. As second component of the vector-valued IMs, the Peak Ground Acceleration (PGA), Peak Ground Velocity (PGV), effective duration (tD), earthquake damage potential (ID) and four Np spectral shape-based parameters obtained through spectral acceleration (NpSa), velocity (NpV), pseudo-velocity (NpSv), and input energy (NpEI) have been analyzed. In order to obtain fragility surfaces, Multinomial Logistic Regression (MLR) was applied. It is observed that those vector-valued IMs based on the spectral shape proxies were more efficient to predict the probability of failure of RCB. For this reason, it is important to consider spectral shape vector-valued IMs in order to reduce uncertainty in the structural response of buildings under earthquakes. Thus, the use of two parameters instead of a single intensity measure improves the efficiency. Moreover, the fragility surfaces can be used for the seismic risk evaluation of buildings.

In this study, with the objective to develop a reliability-based seismic design tool, ductility and dissipated hysteretic energy uniform annual failure rate (UAFR) spectra are obtained and compared using the spectral acceleration at first mode of vibration of the structure Sa(T1) and the well-known spectral shape-based intensity measure INp. Notice that this is the first time in the literature that UAFR spectra are obtained for the advanced spectral shape intensity measure INp. For this aim, 110 simulated ground motions recorded from the soft soil of Mexico City were selected due to their large energy amount demanded to the structures; moreover, four elastoplastic hysteretic behavior models are considered for the dynamic analyses with post-yielding stiffness of 0, 3, 5, and 10%. It is observed that the use of elasto-perfectly plastic models provided similar UAFR spectra in comparison with hysteretic models with different post-yielding stiffness. This conclusion is valid for the two selected intensity measures. In addition, the lateral resistance required to achieve similar structural reliability levels is larger when the INp intensity measure is used, especially for buildings with vibration periods equal or larger than the soil period, in such a way that the traditional use of Sa(T1) could provide structures with less structural reliability levels.