| Author: | Nan, Zhuojun |
| Title: | Towards safer buildings : an integrated approach for structural fire analysis, design, and collapse prediction |
| Advisors: | Usmani, Asif (BEEE) Jiang, Liming (BEEE) Huang, Xinyan (BEEE) |
| Degree: | Ph.D. |
| Year: | 2023 |
| Subject: | Fire extinction Buildings -- Fires and fire prevention Structural analysis (Engineering) Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Building Environment and Energy Engineering |
| Pages: | xxii, 208 pages : color illustrations |
| Language: | English |
| Abstract: | Fire-induced structural collapses are catastrophic events that can have dire consequences, including loss of life and property damage. With the erection of high-rise buildings, there have been several devastating fires that caused the partial or progressive collapse of structures. For instance, the World Trade Centre Towers fire was a tragic event that had a profound impact on the field of structural fire engineering and even the world as a whole. Collapse is also a major concern during firefighting and rescue operations. However, structural fire responses are complex and difficult to predict, owing to the capricious nature of fire, as well as the intricate interplay between fire and structure. To analyses the behaviour of structures under fire, numerical modelling techniques, Computational Fluid Dynamics (CFD) and Finite Element Method (FEM), are commonly coupled. Concurrently, a series of theoretical representations of advanced travelling fire models have been developed in response to related fire accident investigations and experimental results. Nonetheless, due to the complexity of modern buildings and fire dynamics, structural fire analysis has become more sophisticated, and identifying the worst possible fire scenario is challenging. Today’s performance-based structural fire design still relies on the experience of engineers, whether for fire scenarios design, or assessment of structural responses. Therefore, better understanding structural fire behaviours under realistic fire conditions and developing accurate and reliable methods for predicting structural responses in fires are crucial. This also discusses why the use of Artificial Intelligence (AI) is a promising area of further research in this area. This thesis establishes a practical workflow for structural analysis under realistic fire scenarios, which involved conducting full-scale fire spread tests with non-uniform fuel distribution, developing a natural fire model with travelling behaviour of localised burning in OpenSees, and integrating the fire models with the Integrated Simulation Environment (GiD+OpenSees) for heat transfer and thermomechanical analysis. The integrated package is able to produce large datasets, which could be incorporated into advanced data analysis and machine learning techniques. It not only provides a comprehensive solution for structural fire response analysis, but also facilitates fast forecasting of fire-induced structural collapses and enables smart performance-based structural fire design. Fires may burn locally or spread by “travelling” with non-uniform temperature distribution in today’s large open-plan compartments. It is a significant challenge in representing the fire exposures for structural fire design. The “travelling” nature of fire has been observed in many fire accidents, some of the fire buildings partially or fully collapsed. Over ten full-scale travelling fire experiments have been conducted to investigate travelling fire behaviours. A series of travelling fire models have been developed, i.e., Clifton’s travelling fire model, Travelling Fires Methodology (TFM) and its subsequent refined versions, and Extended Travelling Fire Methodology (ETFM) framework. Further discussion is necessary regarding the application of “travelling fire” methodology in performance-based structural fire design. Chapter 2 reviews and presents the applicability and limitations of the existing travelling fire models to understand their suitability for structural fire assessment. This chapter also investigates the structural behaviour of a 3D steel-composite structure under eleven different travelling fire scenarios using ETFM, to identify which travelling fire design parameters (i.e., fire spread rates, fuel load densities and opening factors), or related structural design parameters (i.e., fire protection, slab thicknesses and steel reinforcements), are most critical to fulfilling the design goals of structural fire safety. Compared with the inverse opening factor, fire spread rates and fuel load densities are more critical in identifying the worst travelling fire scenario for the structural responses. Moreover, a “slow” travelling fire with a modest heat release rate (HRR), or a “fast” travelling fire with a high HRR, would both induce significant thermal/structural responses leading to failure. Based on the summarization of the structural responses, solely satisfying the critical temperature and deflection may not guarantee a safe structural design, utilisation of structural components should also be examined under travelling fires. This work also identified the importance of the concrete slab inclusion into the 3D structural model under travelling fires, in comparison with a 2D structural model without a slab. Chapter 3 presents the results of five full-scale wood-crib fire spread tests, conducted with the representative fuel load setup in a sufficiently ventilated space. The experimental work aimed to characterize fire spread and burning behaviour on non-uniform fuel beds in a large open-plan space. Two basic types of wood crib layouts were used in the tests: non-uniform and continuous, and non-uniform and discontinuous. The burning behaviour, including the development of fire spread rate and fire size, and their impact on surroundings (i.e., temperature and heat flux) are analysed. The results reveal that the non-uniform fuel load distribution significantly changes the spreading behaviours of fire. Especially, a multi-centre fire appeared due to the leaping of localised burning between wood cribs with the discontinuous fuel load distribution. When wood cribs are placed non-uniformly, the fire dynamics depend more on the localised burning size and gaps between fuels. Distinct fire behaviours were observed with different porosities of wood cribs. For performance-based design, considering the realistic fuel load distribution can contribute to the development of advanced fire models and help identify worst-case fire scenarios. Chapter 4 summarises the travelling behaviour of natural fires with time-variant fire sizes and fire spread rates by the results from full-scale large compartment fire tests. A natural fire model has been developed in OpenSees for fire that comprises travelling behaviour model, localised fire models of various modes, and the effect of the smoke layer. The model was demonstrated with the representative fire impacts, including a fast-spread fire and a slow-spread fire, based on experimental results of full-scale travelling fire tests. The time-varying travelling fire can cause local failure of structural components that are not fully captured by constant travelling fires. The thermal responses of the steel beam are dominated by the “stay time” and intensity of the near field. By introducing travelling behaviour models, it is possible to identify design fire scenarios with locally high fire impact. Based on the natural fire model, the design fire models can be extended as a library to examine the fire performance of structures regarding the realistic fire load and potential fire scenarios. To investigate fire-induced progressive structural collapse, an aluminium reticulated roof structure was constructed at the Sichuan Fire Research Institute. In Chapter 5, a FE roof structural model was established in the Integrated Simulation Environment (ISE) and analysed using OpenSees, for both heat transfer and subsequent structural responses analysis. Approximately a thousand fire scenarios were generated using the natural fire model developed in Chapter 4 to investigate structural fire behaviour. The results indicate that the structural response of the roof varies greatly with the travelling behaviour, location, and intensity of the fire. It highlights the importance of considering fire scenarios comprehensively for risk assessment and performance-based design. The structural fire response patterns of the roof were also discussed in this chapter. The structural responses in a fire are usually nonlinear and time-dependent, as the structure undergoes significant changes in behaviour over the course of the fire. Therefore, it is imperative to consider the effect of localised failure, especially the failure of key structural components, on the global structural responses. A modular artificial intelligence (AI) system for forecasting the structural responses in fires is presented in Chapter 6. The system consists of several AI models, each of which predicts the displacement at a specific monitoring point. These individual predictions were then combined to generate a comprehensive forecast of the global structural fire behaviour. The individual AI model utilized was a Long Short-Term Memory Recurrent Neural Network (LSTM RNN). The generated simulation results in Chapter 5 were then cleaned and the data at several key locations were extracted as the database and used to train individual AI models. The modular design allows different models to be modified or added as needed, making the system flexible and adaptable, and improving the accuracy and reliability of the predictions. The results demonstrate the effectiveness of the modular AI approach in reasonably forecasting fire-induced structural collapses as indicated by the sensitivity the local models can have. Chapter 7 provides an overview of the research outcomes and significance, as well as exploring potential directions for future research. |
| Rights: | All rights reserved |
| Access: | open access |
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