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European Journal of Wood and Wood Products

, Volume 74, Issue 3, pp 393–405 | Cite as

Cyclic behaviour of glulam shear walls with bolted connections

  • Daniela WrzesniakEmail author
  • Massimo Fragiacomo
Original
  • 272 Downloads

Abstract

Dowels and bolts are well established types of fasteners for timber connections. They are fast and easy to assemble which made them become a common solution for joining heavy timber elements. This study investigates the feasibility of bolted connections as a possible anchoring solution for heavy shear walls under seismic loads. Fully reversed cyclic tests were performed on glued laminated (glulam) timber shear walls with un-reinforced and reinforced bolted connections. This study builds up on a previous research conducted on single bolted connections tested under quasi-static tensile loading. Testing an entire wall provides additional information on the performance of the connections under the actual load distribution (tensile and shear force) due to horizontal load which varies the shear force distribution in the fasteners compared to the case of a simple connection tested in tension. The influence of the fastener spacing and distance from the loaded and unloaded edge on the failure mechanism was investigated, as well as the effect of the reinforcement on the cyclic response of the connection in comparison to the unreinforced solution. The experimental study proved that conventional bolted connections are a feasible solution for anchoring shear walls to the foundation. Reinforcing the connection area with self-tapping screws resulted in reduced splitting, increased connection ductility and increased equivalent damping. However, bolted connections experience significant irreversible damage, namely plasticization of the bolts and timber crushing at the interface with the bolt, at the end of the seismic event.

Keywords

Timber Plastic Hinge Slenderness Ratio Yield Displacement Laminate Veneer Lumber 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Am Bahnhof Westend 3BerlinGermany
  2. 2.University of L’AquilaL’AquilaItaly

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