In recent years, the use of Fiber Reinforced Polymers (FRPs) as one of the most common ways to increase the strength of concrete samples, has been introduced. Evaluation of the final strength of these specimens is performed with different experimental methods. In this research, due to the variety of models, the low accuracy and impact of different parameters, the use of new intelligence methods is considered. Therefore, using artificial intelligent-based models, a new solution for evaluating the bond strength of FRP is presented in this paper. 150 experimental samples were collected from previous studies, and then two new hybrid models of Imperialist Competitive Algorithm (ICA)-Artificial Neural Network (ANN) and Artificial Bee Colony (ABC)-ANN were developed. These models were evaluated using different performance indices and then, a comparison was made between the developed models. The results showed that the ICA-ANN model's ability to predict the bond strength of FRP is higher than the ABC-ANN model. Finally, to demonstrate the capabilities of this new model, a comparison was made between the five experimental models and the results were presented for all data. This comparison showed that the new model could offer better performance. It is concluded that the proposed hybrid models can be utilized in the field of this study as a suitable substitute for empirical models.

Investigation of structural integrity has been a critical issue in the field of civil engineering for years. Visual inspection is one of the most available methods to explore deteriorative components in structures. Still, this method is not applicable to invisible damage of structures. Alternatively, system identification methods are capable of tracking modal properties of structures over time. The deviation of these dynamic properties can serve as indicators to access structural integrity. In this study, a modal tracking technique using frequency-domain system identification from seismic responses of structures is proposed. The method first segments the measured signals into overlapped sequential portions and then establishes multiple Hankel matrices. Each Hankel matrix is then converted to the frequency domain, and a temporal-average frequency-domain Hankel matrix can be calculated. This study also proposes the frequency band selection that can divide the frequency-domain Hankel matrix into several portions in accordance with referenced natural frequencies. Once these referenced natural frequencies are unavailable, the first few right singular vectors by the singular value decomposition can offer these references. Finally, the frequency-domain stochastic subspace identification tracks the natural frequencies and mode shapes of structures through quick stabilization diagrams. To evaluate performance of the proposed method, a numerical study is carried out. Moreover, the long-term monitoring strong motion records at a specific site are exploited to assess the tracking performance. As seen in results, the proposed method is capable of tracking modal properties through seismic responses of structures.

To effectively extract the vast wind resource, offshore wind turbines are designed with large rotor and slender tower, which makes them vulnerable to external vibration sources such as wind and wave loads. Substantial research efforts have been devoted to mitigate the unwanted vibrations of offshore wind turbines to ensure their serviceability and safety in the normal working condition. However, most previous studies investigated the vibration control of wind turbines in one direction only, i.e., either the out-of-plane or in-plane direction. In reality, wind turbines inevitably vibrate in both directions when they are subjected to the external excitations. The studies on both the in-plane and out-of-plane vibration control of wind turbines are, however, scarce. In the present study, the NREL 5 MW wind turbine is taken as an example, a detailed three-dimensional (3D) Finite Element (FE) model of the wind turbine is developed in ABAQUS. To simultaneously control the in-plane and out-of-plane vibrations induced by the combined wind and wave loads, another carefully designed (i.e., tuned) spring and dashpot are added to the perpendicular direction of each Tuned Mass Damper (TMD) system that is used to control the vibrations of the tower and blades in one particular direction. With this simple modification, a bi-directional TMD system is formed and the vibrations in both the out-of-plane and in-plane directions are simultaneously suppressed. To examine the control effectiveness, the responses of the wind turbine without control, with separate TMD system and the proposed bi-directional TMD system are calculated and compared. Numerical results show that the bi-directional TMD system can simultaneously control the out-of-plane and in-plane vibrations of the wind turbine without changing too much of the conventional design of the control system. The bi-directional control system therefore could be a cost-effective solution to mitigate the bi-directional vibrations of offshore wind turbines.

Although model updating has been widely applied using a specific optimization algorithm with a single objective function using frequencies, mode shapes or frequency response functions, there are few studies that investigate hybrid optimization algorithms for real structures. Many of them did not take into account the sensitivity of the updating parameters to the model outputs. Therefore, in this paper, optimization algorithms and sensitivity analysis are applied for model updating of a real cable-stayed bridge, i.e., the Kien bridge in Vietnam, based on experimental data. First, a global sensitivity analysis using Morris method is employed to find out the most sensitive parameters among twenty surveyed parameters based on the outputs of a Finite Element (FE) model. Then, an objective function related to the differences between frequencies, and mode shapes by means of MAC, COMAC and eCOMAC indices, is introduced. Three metaheuristic algorithms, namely Gravitational Search Algorithm (GSA), Particle Swarm Optimization algorithm (PSO) and hybrid PSOGSA algorithm, are applied to minimize the difference between simulation and experimental results. A laboratory pipe and Kien bridge are used to validate the proposed approach. Efficiency and reliability of the proposed algorithms are investigated by comparing their convergence rate, computational time, errors in frequencies and mode shapes with experimental data. From the results, PSO and PSOGSA show good performance and are suitable for complex and time-consuming analysis such as model updating of a real cable-stayed bridge. Meanwhile, GSA shows a slow convergence for the same number of population and iterations as PSO and PSOGSA.

This is the first research on the smart control and vibration analysis of a Graphene nanoplatelets (GPLs) Reinforced Composite (GPLRC) porous cylindrical shell covered with piezoelectric layers as sensor and actuator (PLSA) in the framework of numerical based Generalized Differential Quadrature Method (GDQM). The stresses and strains are obtained using the First-order Shear Deformable Theory (FSDT). Rule of the mixture is employed to obtain varying mass density and Poisson's ratio, while the module of elasticity is computed by modified Halpin-Tsai model. The external voltage is applied to sensor layer and a Proportional-Derivative (PD) controller is used for sensor output control. Governing equations and boundary conditions of the GPLRC cylindrical shell are obtained by implementing Hamilton's principle. The results show that PD controller, length to radius ratio (L/R), applied voltage, porosity and weight fraction of GPL have significant influence on the frequency characteristics of a porous GPLRC cylindrical shell. Another important consequence is that at the lower value of the applied voltage, the influence of the smart controller on the frequency of the micro composite shell is much more significant in comparison with the higher ones.

The negative stiffness spring and inerter are both characterized by the negative stiffness effect in the force-displacement relationship, potentially yielding an amplifying mechanism for dashpot deformation by being incorporated with a series tuning spring. However, resisting forces of the two mechanical elements are dominant in different frequency domains, thus leading to necessary complementarity in terms of vibration control and the amplifying benefit. Inspired by this, this study proposes a Negative Stiffness Inerter System (NSIS) as an earthquake protection system and developed analytical design formulae by fully utilizing its advantageous features. The NSIS is composed of a sub-configuration of a negative stiffness spring and an inerter in parallel, connected to a tuning spring in series. First, closed-form displacement responses are derived for the NSIS structure, and a stability analysis is conducted to limit the feasible domains of NSIS parameters. Then, the dual advantageous features of displacement reduction and the dashpot deformation amplification effect are revealed and clarified in a parametric analysis, stimulating the establishment of a displacement-based optimal design framework, correspondingly yielding the design formulae in analytical form. Finally, a series of examples are illustrated to validate the derived formulae. In this study, it is confirmed that the synergistic incorporation of the negative stiffness spring and the inerter has significant energy dissipation efficiency in a wide frequency band and an enhanced control effect in terms of the displacement and shear force responses. The developed displacement-based design strategy is suitable to utilize the dual benefits of the NSIS, which can be accurately implemented by the analytical design formulae to satisfy the target vibration control with increased energy dissipation efficiency.

Monitoring of complex processes faces several challenges mainly due to the lack of relevant sensory information or insufficient elaborated decision-making strategies. These challenges motivate researchers to adopt complex data processing and analysis in order to improve the process representation. This paper presents the development and implementation of quality monitoring framework based on a model-driven approach using embedded artificial intelligence strategies. In this work, the strategies are applied to the supervision of a microfabrication process aiming at showing the great performance of the framework in a very complex system in the manufacturing sector. The procedure involves two methods for modelling a representative quality variable, such as surface roughness. Firstly, the hybrid incremental modelling strategy is applied. Secondly, a generalized fuzzy clustering c-means method is developed. Finally, a comparative study of the behavior of the two models for predicting a quality indicator, represented by surface roughness of manufactured components, is presented for specific manufacturing process. The manufactured part used in this study is a critical structural aerospace component. In addition, the validation and testing are performed at laboratory and industrial levels, demonstrating proper real-time operation for non-linear processes with relatively fast dynamics. The results of this study are very promising in terms of computational efficiency and transfer of knowledge to manufacturing industry.

In order to achieve effective corrosion monitoring of buried metal pipelines, a Novel nondestructive Testing (NDT) methodology using ultra-long (250 mm) Polymer Optical Fiber (POF) sensors coated with the Fe-C alloy film is proposed in this study. The theoretical principle is investigated to clarify the monitoring mechanism of this method, and the detailed fabrication process of this novel POF sensor is presented. To validate the feasibility of this novel POF sensor, exploratory research of the proposed method was performed using simulated corrosion tests. For simplicity, the geometric shape of the buried pipeline was simulated as a round hot-rolled plain steel bar. A thin nickel layer was applied as the inner plated layer, and the Fe-C alloy film was coated using an electroless plating technique to precisely control the thickness of the alloy film. In the end, systematic sensitivity analysis on corrosion severity was further performed with experimental studies on three sensors fabricated with different metal layer thicknesses of 25 ㎛, 30 ㎛ and 35 ㎛. The experimental observation demonstrated that the sensor coated with 25 ㎛ Fe-C alloy film presented the highest effectiveness with the corrosion sensitivity of 0.3364 mV/g at Δm = 9.32 × 10^-4 g in Stage I and 0.0121 mV/g in Stage III. The research findings indicate that the detection accuracy of the novel POF sensor proposed in this study is satisfying. Moreover, the simple fabrication of the high-sensitivity sensor makes it cost-effective and suitable for the on-site corrosion monitoring of buried metal pipelines.

In this paper, a new interleaved high step-up converter with low voltage stress on the switches is proposed. In the proposed converter, soft switching is provided for all switches by just one auxiliary switch, which decreases the conduction loss of auxiliary circuit. Also, the auxiliary circuit is expanded on the converter with more input branches. In the converter all main switches operate under zero voltage switching condition and auxiliary switch operate under zero current switching condition. Because of the interleaved structure, the reliability of converter increases and input current ripples decreases. The clamp capacitor in the converter not only absorb the voltage spikes across the switch due to leakage inductance, but also improve voltage gain. The proposed converter is fully analyzed and to verify the theoretical analysis, a 100 W prototype was implemented. Also, to show the effectiveness of auxiliary circuit on conduction EMI, EMI of the proposed converter comprised with hard switching counterpart.

Effective bridge maintenance reduces bridge operation costs and extends its service life. The possibility of storing bridge life-cycle data in a 3D parametric model of the bridge through Bridge Information Modeling (BrIM) provides new opportunities to enhance current practices of bridge maintenance management. This study develops a Decision Support System (DSS), namely BrDSS, which employs BrIM and an efficient optimization model for bridge maintenance planning. The BrIM model in BrDSS extracts basic data of elements required for the optimization process and visualizes the inspection data and the optimization results to the user to help in decision makings. In the optimization module of the DSS, the specifically formulated Genetic Algorithm (GA) eliminates the chances of producing infeasible solutions for faster convergence. The practicality of the presented DSS was explored by utilizing the DSS in the maintenance planning of a bridge under operation in the southwest of Iran.