Conventional Piezoelectric Energy Harvesters (CPEH) have been extensively studied for maximizing their electrical output through material selection, geometric and structural optimization, and adoption of efficient interface circuits. In this paper, the performance of Stepped Piezoelectric Energy Harvester (SPEH) under harmonic base excitation is studied analytically, numerically and experimentally. The motivation is to compare the energy harvesting performance of CPEH and SPEHs with the same characteristics (resonant frequency). The results of this study challenge the notion of achieving higher voltage and power output through incorporation of geometric discontinuities such as step sections in the harvester beams. A CPEH consists of substrate material with a patch of piezoelectric material bonded over it and a tip mass at the free end to tune the resonant frequency. A SPEH is designed by introducing a step section near the root of substrate beam to induce higher dynamic strain for maximizing the electrical output. The incorporation of step section reduces the stiffness and consequently, a lower tip mass is used with SPEH to match the resonant frequency to that of CPEH. Moreover, the electromechanical coupling coefficient, forcing function and damping are significantly influenced because of the inclusion of step section, which consequently affects harvester's output. Three different configurations of SPEHs characterized by the same resonant frequency as that of CPEH are designed and analyzed using linear electromechanical model and their performances are compared. The variation of strain on the harvester beams is obtained using finite element analysis. The prototypes of CPEH and SPEHs are fabricated and experimentally tested. It is shown that the power output from SPEHs is lower than the CPEH. When the prototypes with resonant frequencies in the range of 56-56.5 Hz are tested at 1 m/s2, three SPEHs generate power output of 482 μW, 424 μW and 228 μW when compared with 674 μW from CPEH. It is concluded that the advantage of increasing dynamic strain using step section is negated by increase in damping and decrease in forcing function. However, SPEHs show slightly better performance in terms of specific power and thus making them suitable for practical scenarios where the ratio of power to system mass is critical.

The conventional Kalman Filter (KF) provides a promising way for structural state estimation. However, the physical parameters of structural systems or models should be available for the estimation. Moreover, it is not applicable when the loadings applied to the structures are unknown. To circumvent the aforementioned limitations, a two-stage KF with unknown input approach is proposed for the simultaneous identification of structural parameters and unknown loadings. In stage 1, a modified observation equation is employed. The structural state vector is estimated by KF on the basis of structural parameters identified at the previous time-step. Then, the unknown input is identified by Least Squares Estimation (LSE). In stage 2, based on the concept of sensitivity matrix, the structural parameters are updated at the current time-step by using the estimated structural states obtained from stage 1. The effectiveness of the proposed approach is numerically validated via a five-story shearing model under random and earthquake excitations. Shaking table tests on a five-story structure are also employed to demonstrate the performance of the proposed approach. It is demonstrated from numerical and experimental results that the proposed approach can be used for the identification of parameters of structure and the external force applied to it with acceptable accuracy.

This paper investigates the safety of the wind turbine blade against excessive deformation. For this purpose, the performance of the blade in the along-wind direction is improved by longitudinal stiffener made of shape memory alloy. The rationale behind the selection of this smart material is due to its ability to offer excellent thermo-mechanical behaviour at low strain. Here, Liang-Roger model is adopted for vibration control, and the super-elastic effects are utilised for blade stiffening. Turbulent wind fields are generated at the hub height using TurbSim and the corresponding loads are evaluated using blade element momentum theory. An efficient switching algorithm is developed along with performance curves that enable the designer to select an optimal mode of heating depending upon the operational scenario. Numerical results presented in this paper clearly demonstrate the performance envelope of the proposed stiffener and its influence on the reliability of the blade.

The present study investigates the employing of piezoelectric patches in active control of a sandwich plate. Indeed, the active control and optimal patch distribution on this structure are presented together. A sandwich plate with honeycomb core and composite reinforced by carbon nanotubes in facesheet layers is considered so that the optimum position of actuator/sensor patches pair is guaranteed to suppress the vibration of sandwich structures. The sandwich panel consists of a search space which is a square of 200 × 200 mm with a numerous number of candidates for the optimum position. Also, different dimension of square and rectangular plates to obtain the optimal placement of piezoelectric actuator/senor patches pair is considered. Based on genetic algorithm and LQR, the optimum position of patches and fitness function is determined, respectively. The present study reveals that the efficiency and performance of LQR control is affected by the optimal placement of the actuator/sensor patches pair to a large extent. It is also shown that an intelligent selection of the parent, repeated genes filtering, and 80% crossover and 20% mutation would increase the convergence of the algorithm. It is noted that a fitness function is achieved by collection actuator/sensor patches pair cost functions in the same position (controllability). It is worth mentioning that the study of the optimal location of actuator/sensor patches pair is carried out for different boundary conditions of a sandwich plate such as simply supported and clamped boundary conditions.

Identification of structure parameters is crucial in Structural Health Monitoring (SHM) context for activities such as model validation, damage assessment and signal processing of structure response. In this paper, guided waves generated by piezoelectric transducers are used for in-situ and non-destructive structural parameter estimation based on Bayesian approach. As Bayesian approach needs iterative process, which is computationally expensive, this paper proposes a method in which an analytical model is selected and developed in order to decrease computational time and complexity of modeling. An experimental set-up is implemented to estimate three target elastic and geometrical parameters: Young's modulus, Poisson ratio and thickness of aluminum and steel plates. Experimental and simulated data are combined in a Bayesian framework for parameter identification. A significant accuracy is achieved regarding estimation of target parameters with maximum error of 8, 11 and 17 percent respectively. Moreover, the limitation of analytical model concerning boundary reflections is addressed and managed experimentally. Pulse excitation is selected as it can excite the structure in a wide frequency range contrary to conventional tone burst excitation. The results show that the proposed non-destructive method can be used in service for estimation of material and geometrical properties of structure in industrial applications.

The early prediction of Compressive Strength of Concrete (CSC) is a significant task in the civil engineering construction projects. This study, therefore, is dedicated to introducing two novel hybrids of neural computing, namely Shuffled Complex Evolution (SCE) and Teaching-Learning-Based Optimization (TLBO) for predicting the CSC. The algorithms are applied to a Multi-Layer Perceptron (MLP) network to create the SCE-MLP and TLBO-MLP ensembles. The results revealed that, first, intelligent models can properly handle analyzing and generalizing the non-linear relationship between the CSC and its influential parameters. For example, the smallest and largest values of the CSC were 17.19 and 58.53 MPa, and the outputs of the MLP, SCE-MLP, and TLBO-MLP range in [17.61, 54.36], [17.69, 55.55] and [18.07, 53.83], respectively. Second, applying the SCE and TLBO optimizers resulted in increasing the correlation of the MLP products from 93.58 to 97.32 and 97.22%, respectively. The prediction error was also reduced by around 34 and 31% which indicates the high efficiency of these algorithms. Moreover, regarding the computation time needed to implement the SCE-MLP and TLBO-MLP models, the SCE is a considerably more time-efficient optimizer. Nevertheless, both suggested models can be promising substitutes for laboratory and destructive CSC evaluative models.

Collapse of bridges in recent earthquakes demonstrates the need to deepen the understanding of the behaviour of these structures against seismic actions. This paper presents a highly detailed numerical model of an actual bridge subjected to extreme seismic action which results in its collapse. Normally, nonlinear numerical models have high difficulties to achieve convergence when reinforced concrete is intended to be represented. The main objective of this work is to determine the efficiency of different passive control strategies to prevent the structural collapse of an existing bridge. Metallic dampers and seismic isolation by decoupling the mass were evaluated. The response is evaluated not only in terms of reduction of displacements, but also in increasing of shear force and axial force in key elements, which can be a negative characteristic of the systems studied. It can be concluded that the use of a metallic damper significantly reduces the horizontal displacements and ensures the integrity of the structure from extreme seismic actions. Moreover, the isolation of the deck, which in principle seems to be the most effective solution to protect existing bridges, proves inadequate for the case analysed due to its dynamic characteristics and its particular geometry and an unpredictable type of axial pounding in the columns. This unexpected effect on the isolation system would have been impossible to identify with simplified models.

Prediction of total sediment load is essential in an extensive range of problems such as the design of the dead volume of dams, design of stable channels, sediment transport in the rivers, calculation of bridge piers degradation, prediction of sand and gravel mining effects on river-bed equilibrium, determination of the environmental impacts and dredging necessities. This paper is aimed to investigate and predict the total sediment load of the Wadi Arbaat in Eastern Sudan. The study was estimated the sediment load by separate total sediment load into bedload and Suspended Load (SL), independently. Although the sediment records are not sufficient to construct the discharge-sediment yield relationship and Sediment Rating Curve (SRC), the total sediment loads were predicted based on the discharge and Suspended Sediment Concentration (SSC). The turbidity data NTU in water quality has been used for prediction of the SSC in the estimation of suspended Sediment Yield (SY) transport of Wadi Arbaat. The sediment curves can be used for the estimation of the suspended SYs from the watershed area. The amount of information available for Khor Arbaat case study on sediment is poor data. However, the total sediment load is essential for the optimal control of the sediment transport on Khor Arbaat sediment and the protection of the dams on the upper gate area. The results show that the proposed model is found to be considered adequate to predict the total sediment load.

Due to the influence of nonlinearity and time-variation, it is difficult to establish an accurate model of concrete frame structures that adopt active controllers. Fuzzy theory is a relatively appropriate method but susceptible to human subjective experience to decrease the performance. This paper proposes a novel artificial intelligence based EBA (Evolved Bat Algorithm) controller with machine learning matched membership functions in the complex nonlinear system. The proposed affine transformed membership functions are adopted and stabilization and performance criterion of the closed-loop fuzzy systems are obtained through a new parametrized linear matrix inequality which is rearranged by machine learning affine matched membership functions. The trajectory of the closed-loop dithered system and that of the closed-loop fuzzy relaxed system can be made as close as desired. This enables us to get a rigorous prediction of stability of the closed-loop dithered system by establishing that of the closed-loop fuzzy relaxed system.

In cells, the microtubules are surrounded by viscoelastic medium. Microtubules, though very small in size, perform a vital role in transportation of protein and in maintaining the cell shape. During performing these functions waves propagate and this propagation of waves has been investigated using nonlocal elastic theory. But the effect of surrounding medium was not taken into account. To fill this gap, this study considers the viscoelastic medium along with nonlocal elastic theory. The analytical formulas of the velocity of waves, and the results reveal that the presence of medium reduces the velocity. The axisymmetric and nonaxisymmetric waves are separately discussed. Furthermore, the results are compared with the results gained from the studies of free microtubules. The presence of medium around microtubules results in the increase of the flexural rigidity causing a significant decrease in radial wave velocity as compared to axial and circumferential wave velocities. The effect of viscoelastic medium is more obvious on radial wave velocity, to a lesser extent on torsional wave velocity and least on longitudinal wave velocity.