• Structural Engineering and Mechanics
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• v.85 no.4
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• pp.469-484
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• 2023

A deep recursive bidirectional Cuda Deep Neural Network Long Short Term Memory (Bi-CuDNNLSTM) layer is recruited in this paper to predict the entire force time histories, and the corresponding hysteresis and backbone curves of reinforced concrete (RC) bridge piers using experimental fast and slow cyclic tests. The proposed stacked Bi-CuDNNLSTM layers involve multiple uncertain input variables, including horizontal actuator displacements, vertical actuators axial loads, the effective height of the bridge pier, the moment of inertia, and mass. The functional application programming interface in the Keras Python library is utilized to develop a deep learning model considering all the above various input attributes. To have a robust and reliable prediction, the dataset for both the fast and slow cyclic tests is split into three mutually exclusive subsets of training, validation, and testing (unseen). The whole datasets include 17 RC bridge piers tested experimentally ten for fast and seven for slow cyclic tests. The results bring to light that the mean absolute error, as a loss function, is monotonically decreased to zero for both the training and validation datasets after 5000 epochs, and a high level of correlation is observed between the predicted and the experimentally measured values of the force time histories for all the datasets, more than 90%. It can be concluded that the maximum mean of the normalized error, obtained through Box-Whisker plot and Gaussian distribution of normalized error, associated with unseen data is about 10% and 3% for the fast and slow cyclic tests, respectively. In recapitulation, it brings to an end that the stacked Bi-CuDNNLSTM layer implemented in this study has a myriad of benefits in reducing the time and experimental costs for conducting new fast and slow cyclic tests in the future and results in a fast and accurate insight into hysteretic behavior of bridge piers.

• Smart Structures and Systems
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• v.20 no.1
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• pp.61-74
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• 2017

One of the most reliable and simplest tools for structural vibration control in civil engineering is Tuned Mass Damper, TMD. Provided that the frequency and damping parameters of these dampers are tuned appropriately, they can reduce the vibrations of the structure through their generated inertia forces, as they vibrate continuously. To achieve the optimal parameters of TMD, many different methods have been provided so far. In old approaches, some formulas have been offered based on simplifying models and their applied loadings while novel procedures need to model structures completely in order to obtain TMD parameters. In this paper, with regard to the nonlinear decision-making of fuzzy systems and their enough ability to cope with different unreliability, a method is proposed. Furthermore, by taking advantage of both old and new methods a fuzzy system is designed to be operational and reduce uncertainties related to models and applied loads. To design fuzzy system, it is required to gain data on structures and optimum parameters of TMDs corresponding to these structures. This information is obtained through modeling MDOF systems with various numbers of stories subjected to far and near field earthquakes. The design of the fuzzy systems is performed by three methods: look-up table, the data space grid-partitioning, and clustering. After that, rule weights of Mamdani fuzzy system using the look-up table are optimized through genetic algorithm and rule weights of Sugeno fuzzy system designed based on grid-partitioning methods and clustering data are optimized through ANFIS (Adaptive Neuro-Fuzzy Inference System). By comparing these methods, it is observed that the fuzzy system technique based on data clustering has an efficient function to predict the optimal parameters of TMDs. In this method, average of errors in estimating frequency and damping ratio is close to zero. Also, standard deviation of frequency errors and damping ratio errors decrease by 78% and 4.1% respectively in comparison with the look-up table method. While, this reductions compared to the grid partitioning method are 2.2% and 1.8% respectively. In this research, TMD parameters are estimated for a 15-degree of freedom structure based on designed fuzzy system and are compared to parameters obtained from the genetic algorithm and empirical relations. The progress up to 1.9% and 2% under far-field earthquakes and 0.4% and 2.2% under near-field earthquakes is obtained in decreasing respectively roof maximum displacement and its RMS ratio through fuzzy system method compared to those obtained by empirical relations.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.4
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• pp.737-752
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• 1992

Analysis of elasto-dynamic deformation of flexible linkage mechanism is conducted using the finite element method. The equations of motion of the system are derived from the static structural problem in which dynamic inertia, gravitational and driving forces are treated as external loads. Linear spring model is included in the formulation of equation of motions to represent the effects of deformation of elastic bearings of revolute joints on the system behavior. A computer program is constructed and applied to analyze a specific crank-lever 4-bar mechanism. The algorithm of the program is as follows. First, the natural frequencies and the mode shapes of the system are calculated by solving the eigenproblem of the mechanism system which can be considered as a static structure by assuming the input shaft (crank shaft) to be fixed at any given configuration of mechanism. And finally, the elasto-dynamic deformation of the whole system is obtained using mode superposition method for the case of constant input speed. The effect of geometric stiffness on the mechamism is included in the program with the axial forces of links obtained through the quasi-static displacement analysis. It is found that the geometric stiffness exerts an important effect upon the elasto-dynamic behavior of the flexible linkage mechanism. Elastic deformation of bearing lowers the natural frequencies of the system, resulting smaller elastic displacement at the mid-point of the links and bigger elestic displacement at the ends of the links than rigid bearing. The above investigation of flexible linkage mechanism shows that the effects of the elastic deformation of bearing on the mechanism should be considered to design the mechanism which satisfies more preciously the purpose and the condition of design.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.4
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• pp.9-19
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• 2020

An integrated finite element model composed of a boiler and its supporting steel structure for a 375-MW coal-fired power plant was developed. This study used the developed model for seismic analysis using SAP2000 software. For the complex superheaters, reheaters, economizers, and membrane walls of the boiler, which consisted of numerous tubes, a method of modeling them by the equivalent elements in the viewpoint of stiffness and/or inertia was proposed. In addition, a method of modeling for the connection between the boiler and steel structure was proposed. Many hangers that connect the boiler to the girders of a steel structure were transformed into equivalent hangers by decreasing the number. The displacements of the boiler stoppers on the buckstay and the posts of the steel structure were coupled by considering their interface condition. Static analysis under the self-loading condition for the developed integrated model was implemented, and the results of deformation indicated that the behavior of the steel members and the major components of the boiler were appropriate. In conclusion, the integrated model developed in this study can be used to evaluate the safety of the boiler and steel structure under seismic loads.