• Smart Structures and Systems
• /
• v.24 no.1
• /
• pp.1-14
• /
• 2019

Transmission tower-line system is one of most critical lifeline systems to cities. However, it is found that the transmission tower-line system is prone to be damaged by earthquakes in past decades. To mitigate seismic demands, this study introduces a tuned-mass damper (TMD) using superelastic shape memory alloy (SMA) spring for the system. In addition, considering the dynamic characteristics of both tower-line system and SMA are affected by temperature change. Particular attention is paid on the effect of temperature variation on seismic behavior. In doing so, the SMA-TMD is installed into the system, and its properties are optimized through parametric analyses. The considered temperature range is from -40 to $40^{\circ}C$. The seismic control effect of using SMA-TMD is investigated under the considered temperatures. Interested seismic performance indices include peak displacement and peak acceleration at the tower top and the height-wise deformation. Parametric analyses on seismic intensity and frequency ratio were carried out as well. This study indicates that the nonlinear behavior of SMA-TMD is critical to the control effect, and proper tuning before application is advisable. Seismic demand mitigation is always achieved in this wide temperature range, and the control effect is increased at high temperatures.

• Smart Structures and Systems
• /
• v.21 no.3
• /
• pp.287-303
• /
• 2018

Applications of energy harvesting from mechanical vibrations is becoming popular but the full potential of such applications is yet to be explored. This paper addresses this issue by considering an application of energy harvesting for the dual objective of serving as an indicator of structural health monitoring (SHM) and extent of control. Variation of harvested energy from an undamaged baseline is employed for this purpose and the concept is illustrated by implementing it for active vibrations of a pipe structure. Theoretical and experimental analyses are carried out to determine the energy harvesting potential from undamaged and damaged conditions. The use of energy harvesting as indicator for control is subsequently investigated, considering the effect of the introduction of a tuned mass damper (TMD). It is found that energy harvesting can be used for the detection and monitoring of the location and magnitude of damage occurring within a pipe structure. Additionally, the harvested energy acts as an indicator of the extent of reduction of vibration of pipes when a TMD is attached. This paper extends the range of applications of energy harvesting devices for the monitoring of built infrastructure and illustrates the vast potential of energy harvesters as smart sensors.

• Smart Structures and Systems
• /
• v.13 no.2
• /
• pp.177-201
• /
• 2014

With the increased size and flexibility of the tower and blades, structural vibrations are becoming a limiting factor towards the design of even larger and more powerful wind turbines. Research into the use of vibration mitigation devices in the turbine tower has been carried out but the use of dampers in the blades has yet to be investigated in detail. Mitigating vibrations will increase the design life and hence economic viability of the turbine blades and allow for continual operation with decreased downtime. The aim of this paper is to investigate the effectiveness of Semi-Active Tuned Mass Dampers (STMDs) in reducing the edgewise vibrations in the turbine blades. A frequency tracking algorithm based on the Short Time Fourier Transform (STFT) technique is used to tune the damper. A theoretical model has been developed to capture the dynamic behaviour of the blades including the coupling with the tower to accurately model the dynamics of the entire turbine structure. The resulting model consists of time dependent equations of motion and negative damping terms due to the coupling present in the system. The performances of the STMDs based vibration controller have been tested under different loading and operating conditions. Numerical analysis has shown that variation in certain parameters of the system, along with the time varying nature of the system matrices has led to the need for STMDs to allow for real-time tuning to the resonant frequencies of the system.

• Advances in concrete construction
• /
• v.13 no.5
• /
• pp.385-394
• /
• 2022

Recently, an increasing number of cutting-edged studies have shown that designing a smart active control for real-time implementation requires piles of hard-work criteria in the design process, including performance controllers to reduce the tracking errors and tolerance to external interference and measure system disturbed perturbations. This article proposes an effective artificial-intelligence method using these rigorous criteria, which can be translated into general control plants for the management of civil engineering installations. To facilitate the calculation, an efficient solution process based on linear matrix (LMI) inequality has been introduced to verify the relevance of the proposed method, and extensive simulators have been carried out for the numerical constructive model in the seismic stimulation of the active rigidity. Additionally, a fuzzy model of the neural network based system (NN) is developed using an interconnected method for LDI (linear differential) representation determined for arbitrary dynamics. This expression is constructed with a nonlinear sector which converts the nonlinear model into a multiple linear deformation of the linear model and a new state sufficient to guarantee the asymptomatic stability of the Lyapunov function of the linear matrix inequality. In the control design, we incorporated H Infinity optimized development algorithm and performance analysis stability. Finally, there is a numerical practical example with simulations to show the results. The implication results in the RMS response with as well as without tuned mass damper (TMD) of the benchmark building under the external excitation, the El-Centro Earthquake, in which it also showed the simulation using evolved bat algorithmic LMI fuzzy controllers in term of RMS in acceleration and displacement of the building.

• Earthquakes and Structures
• /
• v.27 no.1
• /
• pp.1-15
• /
• 2024

In order to enhance the seismic performance of base-isolated structures on soft foundations, the hybrid system of base-isolated system (BIS) and shape memory alloy inerter (SMAI), referred to as BIS+SMAI, is for the first time here proposed. Considering the nonlinear hysteretic relationships of both the isolation layer and SMA, and soil-structure interaction (SSI), the equivalent linearized state space equation is established of the structure-BIS+SMAI system. The displacement variance based on the H₂ norm is then formulated for the structure with BIS+SMAI. Employing the particle swarm optimization, the optimization design methodology of BIS+SMAI is presented in the frequency domain. The evolvement rules of BIS+SMAI in the effectiveness, robustness, SMA driving force, inertia force, stroke, and damping enhancement effect are revealed in the frequency domain through changing the inerter-mass ratio, structural height, aspect ratio, and relative stiffness ratio between the soil and structure. Meanwhile, the validation of BIS+SMAI is conducted using real earthquake records. Results demonstrate that BIS+SMAI can effectively reduce the isolation layer displacement. The inerter can significantly increase the hysteretic displacement of SMA and thus enhance its energy dissipation capacity, implying that BIS+SMAI has better effectiveness than BIS+SMA. Although BIS+SMAI and BIS+ tuned inerter damper (TID) have practically the same effectiveness, BIS+SMAI has the lower optimum damping, significantly smaller inertia force, and higher robustness to perturbations of the optimum parameters. Therefore, BIS+SMAI can be used as a more engineering realizable hybrid system for enhancing the performance of base-isolated structures in soft soil areas.

• Smart Structures and Systems
• /
• v.33 no.5
• /
• pp.365-374
• /
• 2024

This paper adopts a new approach in which nonlinear vibrations can be controlled using fuzzy controllers by optimal grey evolutionary algorithm. If the fuzzy controller cannot stabilize the systems, then the high frequency is injected into the system to assist the controller, and the system is asymptotically stabilized by adjusting the parameters. This paper uses the GM (grey model) and the neural network prediction model. The structure of the neural network is improved from a single factor, and multiple data inputs are extended to various factors and numerous data inputs. The improved model expands the applicable range of uncontrolled elements and improves the accuracy of controlled prediction, using the model that has been trained and stabilized by multiple learning. The simulation results show that the improved gray neural network model has higher prediction accuracy and reliability than the traditional GM model, improving controlled management and pre-control ability. In the combined prediction, the time series parameters and the predicted values obtained from the GM (1,1) (Grey Model of first order and one variable) are simultaneously used as the input terms of the neural network, considering the influence of the non-equal spacing of the data, which makes the results of the combined gray neural network model more rationalized. By adjusting the model structure and system parameters to simulate and analyze the controlled elements, the corresponding risk change trend graphs and prediction numerical calculation results are obtained, which also realize the effective prediction of controlled elements. According to the controlled warning principle and objective, the fuzzy evaluation method establishes the corresponding early warning response method. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage.