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 E_I/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 (t_D), earthquake damage potential (I_D) and four Np spectral shape-based parameters obtained through spectral acceleration (Np_Sa), velocity (Np_V), pseudo-velocity (Np_Sv), and input energy (Np_EI) 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.

The control of vibrations and damage in traditional reinforced concrete (RC) buildings under earthquakes is a difficult task. It requires the use of innovative devices to enhance the seismic behavior of concrete buildings. In this paper, we design RC buildings with buckling restrained braces (BRBs) to achieve this objective. For this aim, three traditional RC framed structures with 3, 6, and 9 story levels are designed by using the well‐known technique nondominated sorting genetic algorithm (NSGA‐II) in order to reduce the cost and maximize the seismic performance. Then, equivalent RC buildings are designed but including buckling restrained braces. Both structural systems are subjected to several narrow‐band ground motions recorded at soft soil sites of Mexico City scaled at different levels of intensities in terms of the spectral acceleration at first mode of vibration of the structure S_a(T₁). Then, incremental dynamic analysis, seismic fragility, and structural reliability in terms of the maximum interstory drift are computed for all the buildings. For the three selected structures and the equivalent models with BRBs, it is concluded that the annual rate of exceedance is considerably reduced when BRBs are incorporated. For this reason, the structural reliability of the RC buildings with BRBs has a better behavior in comparison with the traditional reinforced concrete buildings. The use of BRBs is a good option to improve strength and seismic behavior and hence the structural reliability of RC buildings subjected to strong earthquake ground motions.

Artificial intelligence (AI) has enabled several optimization techniques for structural design, including machine learning, evolutionary algorithms, as in the case of genetic algorithms, reinforced learning, deep learning, etc. Although the use of AI for weight optimization in steel and concrete buildings has been extensively studied in recent decades, multi-objective optimization for reinforced concrete (RC) and steel buildings remains challenging due to the difficulty in establishing independent objective functions and obtaining Pareto fronts. The well-known Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is an efficient genetic algorithm approach for multi-objective optimization. In this work, the NSGA-II approach is considered for the multi-objective structural optimization of three-dimensional RC buildings subjected to earthquakes. For the objective of this study, two function objectives are considered: minimizing total cost and the probability of structural failure, which are obtained via several nonlinear seismic analyses of the RC buildings. Beams and columns’ cross-sectional dimensions are selected as design variables, and the Mexican Building Code (MBC) specifications are imposed as design constraints. Pareto fronts are obtained for two RC-framed buildings located in Mexico City (soft soil sites), which demonstrate the efficiency and accuracy of NSGA-II for structural optimization.

This paper addresses the importance of engineering asset management decisions and control. For this purpose, a Life‐Cycle Cost (LCC) analysis is conducted for typical reinforced concrete (R/C) buildings located in Mexico City. The objective of this study is to develop an artificial neural network (ANN) model that can estimate the total expected cost of R/C buildings by using LCC functions. The total cost includes the initial cost and the cost of the damage caused by future possible ground motions at the site of interest. The present value of the cost includes: initial cost, repair or reconstruction cost, cost of damage to the contents, costs associated with the loss of life or injuries and economic losses. The structural performance is evaluated using probabilistic models, artificial neural networks models are used to obtain the seismic response of the buildings. The methodology is applied to a set of reinforced concrete buildings with 4, 8, and 12 stories which are located at the soft soil of Mexico City. Finally, it is concluded that the life‐cycle cost is efficiently obtained using artificial neural network models for estimating the structural reliability of reinforced concrete buildings, in such a way that it can be used as an excellent planning tool that covers long spans of time.