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Summary: The active tuned mass damper (ATMD) is a reliable energy‐dissipating device to effectively protect structures from serious damages due to earthquake excitations. This study proposes the optimal design of sliding sector control (SSC) for the seismic protection of an 11‐story shear building structure equipped with ATMD. First, the SSC controller is optimally designed for the seismic control of the structure subjected to an artificial earthquake. Then, the effectiveness of the optimized SSC (OSSC) is assessed in reducing the seismic responses of the structure subjected to four near‐ and far‐fault earthquake excitations. The efficient performance of the OSSC technique is also validated and compared with that of a number of the control techniques such as linear quadratic regulator (LQR), fuzzy logic control (FLC), proportional‐integral‐derivative (PID), and optimal sliding mode control (OSMC). Comparative results demonstrate the efficiency and robustness of the proposed OSSC in comparison with those of the other controllers. [ABSTRACT FROM AUTHOR]

The efficacy of a tuned mass damper (TMD) in dissipating the seismic energy transmitted to a structure depends on the TMD's and structure's characteristics and the structural response(s) that the TMD is intended to control. This study presents an approach to designing TMDs considering seismic damage as a representative of structural response. Damage is quantified by the Park–Ang damage index, which integrates deformation-induced damages with the cumulative hysteretic energy dissipated by structural members. The design problem is formulated into an optimization framework. The objective function is assumed to be the maximum value of story damage averaged over an ensemble of independently applied, scaled historical ground motions, and constraints are defined such that an adjustably uniform distribution of the mean story damage along the height of the structure is achieved. The approach is demonstrated by finding the optimum design of a TMD for a 10-story inelastic steel moment-resisting frame (SMRF) using the passive congregation particle swarm and grey wolf optimization techniques and showing that the optimized TMD substantially mitigates local damage in beams and columns, achieves a more uniform damage distribution in stories, yields an improved balance of reduced drifts and accelerations in the structure, and reduces the seismic fragility of the SMRF at different performance levels. [ABSTRACT FROM AUTHOR]

In this paper, aluminum beams in undamaged status and with single and double damage scenarios as slots with the ratio of the slot depth to the beam thickness of 7% and 28% were constructed at the University of Lisbon, Portugal. Then, with the help of the shrearography method, the modal rotations of each beam were calculated for the first to third vibration mode shapes. By deriving the modal rotations, the modal strains were obtained and introduced as the input of 22 different families of 2D wavelet transforms with three different scales 1, 7, and 15. Utilizing the wavelet coefficients as damage indices, the results showed that the sensitivity of modal curvatures is higher than other modal data for identifying the location of damages. In addition, among scales 1, 7, and 15, considering scale 7 for wavelet families provides more suitable results. On the other hand, the sinc and isodog wavelet families showed a better ability to reveal the damage location than other wavelets. Investigating the ratio of the maximum value of the wavelet coefficients in the middle part of the beams to the maximum value of the wavelet coefficients in the boundaries showed that among the two selected wavelets, sinc and isodog, the sinc wavelet is more sensitive than the isodog wavelet in identifying damages with obtained results of 0.81, 7.81 and 27.10 for first, second and third damage scenarios, respectively. And therefore, it can be considered the best wavelet for detecting artificial damage in the tested aluminum beams. [ABSTRACT FROM AUTHOR]

Modern building codes increasingly enforce evaluating the inelastic response of structures to ensure their safety in major seismic events. Although an inelastic dynamic analysis provides the most realistic and accurate measure for the seismic response, its application for large-scale structures is hampered by the excessive computational burden involved. This is particularly the case for the optimization of inelastic structures subjected to dynamic loads using metaheuristic algorithms where numerous analyses are required before the design converges to the optimum. In this regard, developing predictive models with sufficient accuracy will significantly help to reduce the computational demand, thus making the seismic analysis and optimization of large structures more feasible and common practice. Motivated by this need, this paper reports a study on the capabilities of a wavelet weighted least squares support vector machine (WWLSSVM) and a feedforward, backpropagation artificial neural network (ANN) to accurately predict the inelastic seismic responses of structures. The force- and displacement-based seismic responses of an 18-story reinforced concrete frame subjected to different earthquake ground motion records scaled to the design basis earthquake and maximum considered earthquake levels are used to train the models and examine their accuracies. The first three natural periods of the frame and combinations thereof are considered as the inputs for the model. The results indicate that both models exhibit satisfactory prediction performances, with the ANN model having a slight edge on accuracy in most of the cases studied, especially when a smaller number of samples are used for training. A parametric sensitivity analysis shows that the seismic responses predicted by the ANN model generally exhibit less sensitivity to the inputs than do those predicted by the WWLSSVM model. The results also indicate that force- and displacement-based responses exhibit the highest sensitivity to the first and second natural periods, respectively. [ABSTRACT FROM AUTHOR]

Tuned mass dampers (TMDs) are a subclass of dynamic vibration absorbers that consist of a mass-spring-damper unit that is attached to a structure to adjust its response to seismic and wind loads. The efficacy, performance and optimum design of a TMD strongly depend not only on its mass, stiffness and damping as well as the input energy and the structure characteristics, but also on the structural response parameter(s) that the TMD is intended to mitigate. In that respect, this study evaluates the suitability of four objective functions for the optimum design of the TMD of an inelastic, steel moment-resisting frame (SMRF) under an artificial, white-noise excitation. The objective functions include 1) the maximum roof lateral displacement, 2) the maximum drift, 3) the root mean square of drifts and 4) the cumulative hysteretic energy of the SMRF. The results indicate that the SMRF equipped with a TMD optimized using the cumulative hysteretic energy of the SMRF as the objective function exhibits the best seismic response under the artificial earthquake. Further examining the response of the TMD-equipped SMRF under four historic earthquake records shows that equipping a structure with a TMD optimized using an artificial earthquake will not warrant that the structure will exhibit a better seismic performance in all measures compared with when no TMD is used. Put other way, while the minimization of cumulative hysteretic energy could be the best objective function for a case subjected to an artificial earthquake, under real earthquakes, none of the objective functions consistently results in a better seismic performance. This behavior is attributed to detuning effects arising from major structural damages and significant period shifts that occur during strong earthquakes. [ABSTRACT FROM AUTHOR]

Summary: Tuned mass damper (TMD) has been proposed as one of the vibration control methods for rehabilitation of buildings. Because the parameters of TMD can significantly affect the seismic performance of structures, many researches focused on finding the optimum parameters. Because earthquakes are random phenomena and future earthquakes in comparison with past earthquakes may be more destructive, the optimum design of TMD subjected to selected earthquakes can be nonconservative. Hence, the main contribution of this paper is to present the optimal design of TMD for the seismic vibration control of a structure subjected to a critical earthquake that produces the most severe response of a structure. In order to achieve this purpose, the parameters of TMD are optimized through minimizing the maximum displacement of the roof. First, three optimization methods are used to obtain the optimal parameters of TMD for a 10‐story shear building subjected to the critical earthquakes. Finally, the responses of the controlled and uncontrolled buildings such as the roof displacement, strokes, transfer function, and different forms of energy are compared. Results show that the optimum designs of TMD not only effectively reduce the roof displacement but also improve the seismic performance of the building. [ABSTRACT FROM AUTHOR]

A reliable seismic-resistant design of structures is achieved in accordance with the seismic design codes by designing structures under seven or more pairs of earthquake records. Based on the recommendations of seismic design codes, the average time-history responses (ATHR) of structure is required. This paper focuses on the optimal seismic design of reinforced concrete (RC) structures against ten earthquake records using a hybrid of particle swarm optimization algorithm and an intelligent regression model (IRM). In order to reduce the computational time of optimization procedure due to the computational efforts of time-history analyses, IRM is proposed to accurately predict ATHR of structures. The proposed IRM consists of the combination of the subtractive algorithm (SA), K-means clustering approach and wavelet weighted least squares support vector machine (WWLS-SVM). To predict ATHR of structures, first, the input-output samples of structures are classified by SA and K-means clustering approach. Then, WWLS-SVM is trained with few samples and high accuracy for each cluster. 9- and 18-storey RC frames are designed optimally to illustrate the effectiveness and practicality of the proposed IRM. The numerical results demonstrate the efficiency and computational advantages of IRM for optimal design of structures subjected to time-history earthquake loads. [ABSTRACT FROM AUTHOR]

The optimal seismic design of structures requires that time history analyses (THA) be carried out repeatedly. This makes the optimal design process inefficient, in particular, if an evolutionary algorithm is used. To reduce the overall time required for structural optimization, two artificial intelligence strategies are employed. In the first strategy, radial basis function (RBF) neural networks are used to predict the time history responses of structures in the optimization flow. In the second strategy, a binary particle swarm optimization (BPSO) is used to find the optimum design. Combining the RBF and BPSO, a hybrid RBF-BPSO optimization method is proposed in this paper, which achieves fast optimization with high computational performance. Two examples are presented and compared to determine the optimal weight of structures under earthquake loadings using both exact and approximate analyses. The numerical results demonstrate the computational advantages and effectiveness of the proposed hybrid RBF-BPSO optimization method for the seismic design of structures. [ABSTRACT FROM AUTHOR]