Optimization of Structures Subject to Stochastic Dynamic Loading

Xu, Jiaqi; Spencer, Billie F.; Lü, Xilin; Chen, Xinzhong; Lu, Lei

doi:10.1111/mice.12274

Cited by 65 publications

(35 citation statements)

References 41 publications

(47 reference statements)

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“…To avoid the intensive computation and time demands of MCM, random vibration theory has been applied to structural analysis. [ 38–40 ] In particular, the Lyapunov equation has been applied to both linear systems [ 41–44 ] and nonlinear systems after an iterative equivalent linearization on the nonlinear part is performed. [ 45, 46 ] To improve the iteration convergence for systems with strong nonlinearity such as NES systems, Gomez et al [ 47 ] proposed an efficient sequential Lyapunov equation solver that resulted in accurate response predictions.…”

Section: Introductionmentioning

confidence: 99%

Dynamic analysis of track nonlinear energy sinks subjected to simple and stochastice excitations

Wang

Wierschem

et al. 2020

Earthq Engng Struct Dyn

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Track nonlinear energy sinks (track NESs) have been shown to be an effective and applicable strategy to mitigate structural response in recent years. However, previous studies on track NESs has mainly focused on demonstrating the benefits of track NESs through numerical simulations and experiments, with relatively little attention paid to the analytical understanding of the unique dynamics of track NESs. This study analyzes the responses of a track NES when subjected to impulsive and harmonic excitations by the harmonic balance method. Special attention is given to the cause and effect of the peaking behavior that is a prominent characteristic of the track NES's restoring forcedisplacement relationship. Analytical results reveal that the special energy-frequency characteristics of track NESs can be, at times, utilized to enhance the energy robustness that is absent in the conventional cubic NESs. Based on the analytical response expression, an equivalent linearization method (ELM) for the track NESs is developed for stochastic analysis. This ELM is numerically validated on the systems with strong nonlinearities. Stochastic optimization built on the ELM is performed to obtain design parameters of the track NES that can lead to minimum response variances of the primary structure. In particular, the proposed optimization procedure can be applied to seismic optimum design in which the seismic excitations are modeled as filtered whitenoise ground motions. The analytical techniques provided in this study lay the groundwork for the practical implementation of track NESs as a robust and effective control strategy for engineering structures. K E Y W O R D Sharmonic balance method, harmonic excitation, impulsive excitation, peaking behavior, stochastic optimization, track nonlinear energy sink

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Section: Introductionmentioning

confidence: 99%

Dynamic analysis of track nonlinear energy sinks subjected to simple and stochastice excitations

Wang

Wierschem

et al. 2020

Earthq Engng Struct Dyn

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“…In a further perspective, the research is ongoing to consider additional criteria related to the construction costs, which has been shown in Sarma and Adeli (2000) to result in substantial cost savings, as well as dynamic loading conditions (Xu, Spencer, Lu, Chen, & Lu, 2017), including crowd-structure interaction (Zawidzki, Chraibi, & Nishinari, 2014), and to exploit the congruency of modules in a modular approach to local monitoring (Hou, Jankowski, & Ou, 2013), decentralized structural control (Gutierrez Soto & Adeli, 2017;Bakule, Rehák, & Papík, 2016;El-Khoury & Adeli, 2013) and decentralized vibration damping (Poplawski, Mikułowski, Mróz, & Jankowski, 2018;Pisarski, 2018). An approach similar to Kociecki and Adeli (2014) can be exploited to include simultaneous optimization of the internal topology of the module.…”

Section: Resultsmentioning

confidence: 99%

Multiobjective optimization of modular structures: Weight versus geometric versatility in a Truss‐Z system

Zawidzki

Jankowski

2019

Computer aided Civil Eng

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This paper proposes an approach for multicriterial optimization of modular structures with respect to their structural and geometrical properties. The approach is tested using the quickly deployable and reconfigurable modular ramp system Truss‐Z intended for pedestrian traffic. The focus is on modular structures composed of a moderate number of relatively complex modules, which feature an irregular, noncuboidal geometry. Such modules can be assembled into a variety of geometrically different configurations which do not adhere to any predefined spatial grid; their global geometry can be treated as free‐form and determined in situ during construction. The optimization variables represent local‐level geometrical and structural properties of a single module. The Pareto front is used to balance between two kinds of objectives. The geometrical objective quantifies the ability of the modules to generate geometrically versatile global structures that are well‐suited to comply with spatial constraints of real construction sites. The structural objective is formalized in analogy to the minimum weight problem with upper bound constraints imposed on the von Mises stress and the Euler buckling load ratio. A two‐level optimization scheme is employed with NSGA‐II at the top level and a simulated annealing with adaptive neighborhood at the lower level.

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“…Considering three types of dynamic loads, that is, earthquake, wind, and blast loads, Saleh and Adeli (, ) also demonstrate that LQR control algorithm is effective for vibration control of multistory building structures. Further, on the basis of the LQR control algorithm, Adeli and Saleh () present an innovative integrated structural/control optimization solution for active control of smart structures subjected to different dynamic loadings through adroit integration of four different computing paradigms and technologies: control theory, optimization theory (Wang & Szeto, ; Xu, Spencer, Lu, Chen, & Lu, ), sensor/actuator technology (Fernandez‐Luque, Perez, Zapata, & Ruiz, ; Matarazzo & Pakzad, ; Yin, Yuen, Lam, & Zhu, ), and high‐performance computing (Park, Torbol, & Kim, ). The solution is based on simultaneous minimization of structural weight and the required level of control forces using parallel processing on multiprocessor computers (Adeli & Kamal, ).…”

Section: Linear Control Algorithmsmentioning

confidence: 99%

“…control theory, optimization theory (Wang & Szeto, 2017;Xu, Spencer, Lu, Chen, & Lu, 2017), sensor/actuator technology (Fernandez-Luque, Perez, Zapata, & Ruiz, 2016;Matarazzo & Pakzad, 2018;Yin, Yuen, Lam, & Zhu, 2017), and high-performance computing (Park, Torbol, & Kim, 2018). The solution is based on simultaneous minimization of structural weight and the required level of control forces using parallel processing on multiprocessor computers (Adeli & Kamal, 1993).…”

Section: Linear Quadratic Regulator Controlmentioning

confidence: 99%

Control methodologies for vibration control of smart civil and mechanical structures

Adeli

2018

Expert Systems

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Artificial intelligence and expert system remains a key technology in the 21st century. Using active controllers, a structure can adaptively adjust its behaviour during dynamic loads. Such structures with self‐modifying capabilities are referred to as intelligent or smart structures. Smart structure technology has the potential to be a game changer in the structural engineering field. It promises to have enormous consequences in terms of preventing loss of life and damage to structure and their content especially for large structures with hundreds or thousands of components. A key element in successful implementation of smart active control technology is an effective control algorithm to compute the magnitudes of actual forces to be applied to the structure. In this paper, an overview of main active control methodologies for vibration control of smart civil and mechanical structures subjected to external dynamic loads is presented. The advantages and the disadvantages of different control algorithms are discussed. Finally, new trends in control algorithm research are pointed out including multiparadigm strategies, decentralized control, application of deep neural network machine learning techniques, control design for sustainability, and unification of the two fields of structural health monitoring and vibration control.

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Optimization of Structures Subject to Stochastic Dynamic Loading

Cited by 65 publications

References 41 publications

Dynamic analysis of track nonlinear energy sinks subjected to simple and stochastice excitations

Dynamic analysis of track nonlinear energy sinks subjected to simple and stochastice excitations

Multiobjective optimization of modular structures: Weight versus geometric versatility in a Truss‐Z system

Control methodologies for vibration control of smart civil and mechanical structures

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