Dissertation Defence: Fire Performance Evaluation of Mass Timber Structures: Component to Building System Levels
April 2 at 11:00 am - 3:00 pm
Tongchen Han, supervised by Dr. Solomon Tesfamariam, will defend their dissertation titled “Fire Performance Evaluation of Mass Timber Structures: Component to Building System Levels” in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Civil Engineering.
An abstract for Tongchen Han’s dissertation is included below.
Examinations are open to all members of the campus community as well as the general public. Please email Solomon.Tesfamariam@ubc.ca to receive the Zoom link for this exam.
Abstract
This thesis advances the evaluation of fire performance in mass-timber structures, from individual components to a complete building system, through the development of novel numerical modelling approaches and the integration of machine-learning techniques. Outcomes from the thesis enrich the numerical tools for structural fire performance assessment, and several of the developed programs are released as open-source resources to facilitate practical implementation and support the broader design community.
Firstly, mass timber compartment fire scenarios are generated based on the one-zone fire model and long short-term memory network (LSTM). Onezone compartment temperature–time curves derived for a representative design compartment are used to construct the training database for the LSTM. A user-oriented program, Compartment Fire Predictor, is developed to enable customized, compartment-specific input parameters. The predictive capability of the predictor is comprehensively validated through benchmarking against large-scale compartment fire tests.
Subsequently, for glulam members exposed to fire, an equivalent section temperature (EST) is proposed to simplify the highly non-uniform temperature field within the timber cross-section into a single representative temperature that captures the associated strength and stiffness degradation. The formulation of EST improves the flexibility of modelling and accelerates the sequential thermal-structural simulation. The modelling of the glulam column and beam with EST is validated based on experimental test results. The reliability assessment is carried out for the glulam column under standard fire exposure.
Instead of using solid elements to construct high-fidelity models, a component-based glulam beam-column connection model is proposed, in which nonlinear springs are used to represent the responses of shear planes. It significantly simplifies the modelling procedure and improves the computational efficiency. A multi-fidelity neural network (MFNN) is then introduced to integrate the high-fidelity model and component-based model. The MFNN manages to balance the trade-off between the modelling accuracy and calculation speed, enabling sufficiently accurate prediction of connection response under fire using only a limited number of high-fidelity simulations.
Next, a numerical modelling approach for the layer fall-off of CLT panel under fire is developed, in which layer fall-off is governed by a critical bondline temperature (CBLT) criterion. The developed numerical model is calibrated against the experimental standard fire test. Based on this framework, the thermal response of CLT panels is evaluated under probabilistically distributed CBLT values, and the reliability of the post-protection factor is assessed. The performance of the model under natural fire exposure is also examined.
Finally, a prototype ten-storey timber frame building model is developed by integrating component models developed above. Compartment fire scenarios are formulated to explicitly account for the layer fall-off of the CLT ceiling under fire. The structural fire performance of the building is evaluated under multiple fire scenarios. The efficiencies of fire load density and opening factor as candidates for the intensity measure are assessed. The fragility assessments are carried out based on the engineering demand parameters in terms of thermal and structural responses, respectively.
