Parametric Analysis of Moment-Resisting Timber Frames Combined with Cross Laminated Timber Walls and Prediction Models Using Nonlinear Regression and Artificial Neural Networks
Autor(en): |
Osama Abdelfattah Hegeir
Haris Stamatopoulos Kjell Arne Malo |
---|---|
Medium: | Fachartikel |
Sprache(n): | Englisch |
Veröffentlicht in: | Buildings, 25 August 2024, n. 9, v. 14 |
Seite(n): | 2975 |
DOI: | 10.3390/buildings14092975 |
Abstrakt: |
The light weight and moderate stiffness of multistorey timber buildings make them susceptible to increased lateral displacements and accelerations under service-level wind loading. Therefore, the fulfilment of serviceability requirements is a major challenge. In this study, linear elastic finite element analysis was used to perform a parametric study of moment-resisting timber frames combined with cross laminated timber walls. In the parametric study, various mechanical and geometrical parameters were varied within practical ranges. The results of the parametric study were used to derive simplified analytical expressions and to train artificial neural networks which can be used to estimate fundamental frequency, mode shape, top floor displacement, maximum inter-storey drift, and wind-induced acceleration. The analytical expressions and the artificial neural networks can be used for the preliminary assessment of serviceability performance of moment-resisting timber frames with and without cross laminated timber walls, under service-level wind loading. |
Copyright: | © 2024 by the authors; licensee MDPI, Basel, Switzerland. |
Lizenz: | Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden. |
13.8 MB
- Über diese
Datenseite - Reference-ID
10799897 - Veröffentlicht am:
23.09.2024 - Geändert am:
23.09.2024