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This study presents a novel approach based on advancements in Evolutionary Computation for data-driven modeling of complex multi-dimensional memory-dependent systems. The investigated example is a benchmark coupled three-dimensional system that incorporates 6 Bouc-Wen elements, and is subjected to external excitations at three points. The proposed technique of this research adapts Genetic Programming for discovering the optimum structure of the differential equation of an auxiliary variable associated with every specific degree-of-freedom of this system that integrates the imposed effect of vibrations at all other degrees-of-freedom. After the termination of the first phase of the optimization process, a system of differential equations is formed that represent the multi-dimensional hysteretic system. Then, the parameters of this system of differential equations are optimized in the second phase using Genetic Algorithms to yield accurate response estimates globally, because the separately obtained differential equations are coupled essentially, and their true performance can be assessed only when the entire system of coupled differential equations is solved. The resultant model after the second phase of optimization is a low-order low-complexity surrogate computational model that represents the investigated three-dimensional memory-dependent system. Hence, this research presents a promising data-driven modeling technique for obtaining optimized representative models for multi-dimensional hysteretic systems that yield reasonably accurate results, and can be generalized to many problems, in various fields, ranging from engineering to economics as well as biology.
This study presents a novel approach based on advancements in Evolutionary Computation fordata-driven modeling of complex multi-dimensional memory-dependent systems. The investigated exampleis a benchmark coupled three-dimensional system that incorporates 6 Bouc-Wen elements, and is subjectedto external excitations at three points. The proposed technique of this research adapts Genetic Programmingfor discovering the optimum structure of the differential equation of an auxiliary variable associated withevery specific degree-of-freedom of this system that integrates the imposed effect of vibrations at all otherdegrees-of-freedom. After the termination of the first phase of the optimization process, a system ofdifferential equations is formed that represent the multi-dimensional hysteretic system. Then, the parametersof this system of differential equations are optimized in the second phase using Genetic Algorithms to yieldaccurate response estimates globally, because the separately obtained differential equations are coupledessentially, and their true performance can be assessed only when the entire system of coupled differentialequations is solved. The resultant model after the second phase of optimization is a low-orderlow-complexity surrogate computational model that represents the investigated three-dimensionalmemory-dependent system. Hence, this research presents a promising data-driven modeling technique forobtaining optimized representative models for multi-dimensional hysteretic systems that yield reasonablyaccurate results, and can be generalized to many problems, in various fields, ranging from engineering toeconomics as well as biology
A particle tuned mass damper (PTMD) system is the combination of a traditional tuned mass damper (TMD) and a particle damper (PD). This paper presents the results of an experimental and analytical study of the damping performance of a PTMD attached to the top of a benchmark model under wind load excitation. The length ratio of the test model is 1:200. The vibration reduction laws of the system were explored by changing some system parameters (including the particle material, total auxiliary mass ratio, the mass ratio between container and particles, the suspending length, and wind velocity). An appropriate analytical solution based on the concept of an equivalent single-unit impact damper is presented. Comparison between the experimental and analytical results shows that, with the proper use of the equivalent method, reasonably accurate estimates of the dynamic response of a primary system under wind load excitation can be obtained. The experimental and simulation results show the robustness of the new damper and indicate that the damping performance can be improved by controlling the particle density, increasing the amount of particles, and aggravating the impact of particles etc.
The semi-active impact damper (SAID) is proposed to improve the damping efficiency of traditional passive impact dampers. In order to investigate its damping mechanism and vibration control effects on realistic engineering structures, a 20-story nonlinear benchmark building is used as the main structure. The studies on system parameters, including the mass ratio, damping ratio, rigid coefficient, and the intensity of excitation are carried out, and their effects both on linear and nonlinear indexes are evaluated. The damping mechanism is herein further investigated and some suggestions for the design in high-rise buildings are also proposed. To validate the superiority of SAID, an optimal passive particle impact damper (PIDopt) is also investigated as a control group, in which the parameters of the SAID remain the same, and the optimal parameters of the PIDopt are designed by differential evolution algorithm based on a reduced-order model. The numerical simulation shows that the SAID has better control effects than that of the optimized passive particle impact damper, not only for linear indexes (e.g., root mean square response), but also for nonlinear indexes (e.g., component energy consumption and hinge joint curvature).
The recent advancements in sensing technologies allow us to record measurements from target structures at multiple locations and with relatively high spatial resolution. Such measurements can be used to develop data-driven methodologies for condition assessment, control, and health monitoring of target structures. One of the state-of-the-art technologies, Fiber Optic Strain Sensors (FOSS), is developed at NASA Armstrong Flight Research Center, and is based on Fiber Bragg Grating (FBG) sensors. These strain sensors are accurate, lightweight, and can provide almost continuous strainfield measurements along the length of the fiber. The strain measurements can then be used for real-time shape-sensing and operational load-estimation of complex structural systems. While several works have demonstrated the successful implementation of FOSS on large-scale real-life aerospace structures (i.e., airplane wings), there is paucity of studies in the literature that have investigated the potential of extending the application of FOSS into civil structures (e.g., tall buildings, bridges, etc.). This work assesses the feasibility of using FOSS to predict operational loads (e.g., wind loads) on chain-like structures. A thorough investigation is performed using analytical, computational, and experimental models of a 4-story steel building test specimen, developed at the University of Southern California. This study provides guidelines on the implementation of the FOSS technology on building-like structures, addresses the associated technical challenges, and suggests potential modifications to a load-estimation algorithm, to achieve a robust methodology for predicting operational loads using strain-field measurements.