Carleton University
Integrating Structural Fire Analysis with Hybrid Fire Testing: A Framework for Enhanced Stability, Software development and Full-Scale Evaluation.
Abstract
dc:description.abstractAbstract Understanding how structures behave in a fire is becoming increasingly important as engineers move toward performance-based design. Conventional fire-resistance tests evaluate isolated components under simplified and constant boundary conditions, and numerical models remain constrained by uncertainties in high-temperature material behaviour and difficulty representing realistic restraint. As a result, system-level fire response is still poorly understood. This thesis addresses these limitations through four integrated research contributions spanning Hybrid Fire Testing (HFT), large-scale experimentation, high-strength concrete material modelling, and software development. First, the thesis develops a stabilised HFT methodology capable of reliably coupling an experimentally heated physical specimen with a numerically modelled surrounding structure. A new convergence algorithm, inspired by ternary-search optimisation, is introduced to enforce equilibrium and compatibility with significantly improved stability compared to existing HFT approaches. A dedicated communication platform enables automated, real-time data exchange between furnace equipment and finite element models. Verification studies show the method is robust under both force- and displacement-controlled procedures and independent of stiffness-ratio imbalance. Second, the thesis presents a large-scale HFT of a concrete-filled steel tube (CFST) column within a simulated moment-resisting frame. The test captures key system-level behaviours, including axial restraint, load redistribution, and force reversal during heating and contraction, that cannot be reproduced in traditional furnace tests. Comparison with whole-structure numerical simulations confirms that HFT provides a more realistic assessment of CFST behaviour and failure mechanisms under fire. Third, to improve predictive modelling capabilities, an explicit transient thermal strain model for high-strength concrete is developed. The model captures irreversible deformation and nonlinear strain escalation associated with thermochemical degradation and is calibrated against experimental data. Implemented in OpenSees for Fire [1], it significantly improves simulation accuracy during heating and cooling phases. Finally, the thesis introduces OpenSFire, a graphical environment that integrates geometry creation, finite-element heat-transfer analysis, fire-exposure definition, and thermo-mechanical simulation. The platform extends OpenSees for Fire to support direct mapping of two-dimensional temperature fields into fibre-section analysis, enabling consistent and efficient modelling workflows validated against experimental and benchmark results. Collectively, this thesis provides a coherent and experimentally supported framework that advances system-level structural fire engineering by improving methodological stability, material modelling accuracy, and practical modelling tools.
Degree
thesis:*- Name thesis:degree_name
- Doctor of Philosophy (Ph.D.)
- Level thesis:degree_level
- Doctoral
- Discipline thesis:degree_discipline
- Engineering, Civil
- Grantor dc:publisher
- Carleton University
- Year dc:date.issued
- 2026
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Hamidi Iravani, Majid
Rights
dc:rights- Statement dc:rights
-
- Copyright © 2026 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, distribution to students, research and scholarship. Theses may only be shared by linking to the Carleton University Institutional Repository and no part may be copied without proper attribution to the author; no part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
- Language dc:language.iso
- en
Identifiers
dc:identifier.*- OAI identifier oai:identifier
- oai:carleton.scholaris.ca:20.500.14718/45157