Study of dynamic response of crane system via surrogates based on Karhunen–Loève expansion and neural networks
Overhead cranes are widely used in construction sites and manufacturing/assembly lines for various tasks (loading, unloading, lifting, transporting,. . . ). This paper investigates the dynamic response of the main beam (i.e., girder) of an overhead crane by a surrogate technique based on truncated Karhunen–Loève (KL) expansion and neural networks. First, the physical modeling and the motion equations of the crane system are derived using the Lagrange equation. Then, the dynamic responses of the overhead crane system with a number of the input parameters (i.e., configurations) are estimated by the numerical Newmark-beta integral method. Finally, the surrogates based on the truncated KL expansion and neural networks are constructed for studying the dynamic responses of the girder within a limited reference dataset. It can be stated that with a convergence property, the reconstructed surrogate performs good predictability for characterizing the dynamic responses of the structure. The presented method allows us to study the dynamic analysis and optimization of the crane according to the design conditions in the actual applications.
Copyright (c) 2023 Hanoi University of Civil Engineering
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
1. The Author assigns all copyright in and to the article (the Work) to the Journal of Science and Technology in Civil Engineering (JSTCE) – Hanoi University of Civil Engineering (HUCE), including the right to publish, republish, transmit, sell and distribute the Work in whole or in part in electronic and print editions of the Journal, in all media of expression now known or later developed.
2. By this assignment of copyright to the JSTCE, reproduction, posting, transmission, distribution or other use of the Work in whole or in part in any medium by the Author requires a full citation to the Journal, suitable in form and content as follows: title of article, authors’ names, journal title, volume, issue, year, copyright owner as specified in the Journal, DOI number. Links to the final article published on the website of the Journal are encouraged.
3. The Author and the company/employer agree that any and all copies of the final published version of the Work or any part thereof distributed or posted by them in print or electronic format as permitted herein will include the notice of copyright as stipulated in the Journal and a full citation to the Journal as published on the website.