Machine learning techniques for investigating the Coulomb friction and hysteresis in structural joints

Machine learning techniques for investigating the Coulomb friction and hysteresis in structural joints: A data driven approach for monitoring non-linearity in engineering systems

MAHAJAN, SAURABH (TU Delft Civil Engineering & Geosciences)

Cicirello, A. (mentor)
Kavoura, Dr. Florentia (graduation committee)
Lourens, E. (graduation committee)

Delft University of Technology

Civil Engineering | Structural Engineering

2023-07-06

Structural joints influence the design strength, material requirement of a structure. Structural joints experience damping dissipation due to friction damping or hysteresis damping. Damping is often used for reducing the vibrations in a structure. However, large amount of energy dissipation leads to deterioration of the material used for constructing the joint. Hence it is important to identify the system parameters like stiffness, viscous damping, friction force as well as the hysteretic restoring force that cause the energy dissipation in the structure.
For identifying the uncertain system parameters like stiffness, viscous damping and magnitude of friction force, the SINDy algorithm is extended by using stick and slip temporal constraints. This is done by segregating the data of external forcing and response of SDoF system, applying the existing SINDy algorithm and applying the sticking and slipping conditions in the time domain. The proposed Extended SINDy approach estimates the system parameters more accurately compared to the existing SINDy algorithm.
For studying the hysteresis in the structural joints, a pinned column base-plate was considered in an elastic region. Further, the Dahl model with different slope parameter for each branch of moment-rotation hysteresis is employed. The correct values of parameters are estimated using the Bayesian Optimization technique. This procedure yields a functional form representing a resisting hysteretic moment-rotation behaviour in a structural joint with good accuracy.

Stick and slip temporal constraints
nonlinear dynamic system identification
SINDy
Epistemic uncertainty
Discontinuous nonlinearity
Coulomb friction
Pinned column base-plate connection
Unsymmetric hysteresis
Dahl model
Bayesian optimization

http://resolver.tudelft.nl/uuid:9b7bdfcc-1e29-4487-af99-1939887698fd

Student theses

master thesis

© 2023 SAURABH MAHAJAN