Bayesian Regression Based Approach for Beam Deflection Estimation

• Autorzy: Batbooti Raed S. , Mohammed Bassam A. , Jabbar Tahseen Ali , Faisal Safaa Hameed

Identyfikatory

DOI

10.12913/22998624/166313

• Języki publikacji: EN

Abstrakty

EN

Deflection of a beam is the movement of the beam from its initial position to another position depending on the applied load. Beam deflection estimation gives an indication about the possible deformation of the beam. A parametric Bayesian linear based model is introduced to mimic the experimentally collected data to estimate the stochastic deflection of a simply supported beam. A Gaussian noise is assumed to understand the stochastic behavior of the beam deflection as well as a Gaussian prior. The model mapping function used in this work is known as radial basis function, which can be linear or nonlinear. Three basis functions are compared, namely are linear, Gaussian and modified Gaussian function proposed in this work. The modified Gaussian function is a simple function introduced in this work. The performance of the functions is analyzed for three central concentrated loads. The best model can describe the observed data is found to be the modified Gaussian model with regularization factor of 0.9 for three loading cases. The prediction based linear basis function is better than the use of the Gaussian basis function prediction according to error of estimation. The maximum RMS error obtained for modified Gaussian radial basis function corresponding to central load of 4kg is smaller than that of a theoretical based model for the same loading conditions.

Słowa kluczowe

EN

radial basis functions; beam defelection; Bayessian inference

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2023
• Tom: Vol. 17, no 3
• Strony: 206--213
• Opis fizyczny: Bibliogr. 18 poz., fig., tab.

Twórcy

autor

Batbooti Raed S.

• Basra Engineering Technical College, Southern Technical University, Basra, Iraq

• https://orcid.org/0000-0003-3058-4652

autor

Mohammed Bassam A.

• Basra Engineering Technical College, Southern Technical University, Basra, Iraq

autor

Jabbar Tahseen Ali

• Basra Engineering Technical College, Southern Technical University, Basra, Iraq

autor

Faisal Safaa Hameed

• Basra Engineering Technical College, Southern Technical University, Basra, Iraq

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).