Design and Modelling of Ballscrew Linear Guide Actuator for Earthquake Shaking Table (EST) Based on Neural Network

Halimatus Sa’diyah, Purwadi Agus Darwito, Tavio Tavio, Murry Raditya

Abstract


Earthquake Shaking Table (EST) is a device which can simulate an earthquake motion. This device is used to test the strength of a building structure against an earthquake motions before it’s actually made. EST uses a variety of actuators one of them is ball-screw linear guide actuator. The EST used in this project is a bi-axial type which uses 2 linear actuators to simulate the x-axis and y-axis movement of earthquake, each of them used bipolar stepper motor as the main rotary-actuating device. This project models the linear guide actuator using backpropagation neural-network algorithm. The model is built with empirical method using datas taken from the real behavior of both linear actuators. The datas include acceleration, displacement, and velocity of both actuators and they are used to train the neural network using backpropagation with Levenberg-Marquadt method. Simulation is done using Simulink and the results show that model is able to produces nearly same exact movement with the real hardware with error approximately 0,214 % and 0,685% respectively for both actuators.

Keywords


Ballscrew Linear Guide Actuator; Earthquake Shaking Table; Neural Network

Full Text:

PDF

References


T. Baran, A. K. Tanrikulu, C. Dundar and A. Tanrikulu, "Construction and performance test of a low-cost shake table," Experimental Technique, pp. 8-16, 2011.

R. T. Severn, "The development od shaking tables-A historical note," Earthquake Engineering and Structural Dynamics, vol. 40, pp. 195-213, 2011.

I. Flood and R. R. A. Isaa, "Empirical Modeling Methodologies for Construction," Journal of Construction Engineering and Management, vol. 136, no. 1, pp. 36-48, 2010.

S. Yerramareddy, S. C.-Y. Lu and K. F. Arnold, "Developing Empirical Models from Observational Data Using Artificial Neural Networks," Journal of Intelligent Manufacturing, pp. 33-41, 1993.

M. Scardi, "Artificial neural networks as empirical models for estimating phytoplankton production," Marine Ecology Progress Series, vol. 139, pp. 289-299, 1996.

. T. Ninchuewong, S. Tirawanichakul and Y. Tirawanichakul, "Empirical Model and Artificial Neural Network Model Approach for Air Dried Sheets (ADS) Rubber," Advanced Materials Research, Vols. 622-623, pp. 69-74, 2013.

Y. Wang and I. Flood, "Empirically-Based Modelling Approaches To The Truck Weigh-In-Motion Problem" in Proceedings of the 2015 Winter Simulation Conference, 2015.

M. Li and J. Wang, "An Empirical Comparison of Multiple LinearRegression and Artificial Neural Network forConcrete Dam Deformation Modelling," Mathematical Problem in Engineering, vol. 2019, pp. 1-13, 2019.




DOI: http://dx.doi.org/10.12962/j23546026.y2020i6.11125

Refbacks

  • There are currently no refbacks.


View my Stat: Click Here

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.