In recent years, fires in tall buildings have become more frequent, which costs billions of dollars each year and the loss of many human lives. The façade fire in the Grenfell tower made the structure uninhabitable, and the collapse of the three World Trade Center (WTC) towers is the total structural failure caused by fire. Despite such events, no well-defined methodology exists to reconstruct both fire and structural behaviors and carrys out the forensic investigation of a building fire. This Part Ⅰ paper collects the evidence of the Plasco Building fire and generates a coherent timeline to reconstruct the fire processes. The vertical and horizontal fire spread of the building is reconstructed using computational fluid dynamics (CFD) fire modeling and calibrated against the evidence library. The spatio-temporal temperature history from the fire modeling provides realistic fire scenarios to simulate the structural response. The fire simulation results are used as boundary conditions to be transferred to a finite element analysis tool for a detailed structural analysis to determine the likely collapse mechanism of the Plasco Building in Part Ⅱ. The methodology presented in this paper to reconstruct the fire can also guide the structural fire safety engineers to improve the building fire-safety and life-safety strategies.

The extreme temperature induced by fire and hot toxic smokes in tunnels threaten the trapped personnel and firefighters. To alleviate the potential casualties, fast while reasonable decisions should be made for rescuing, based on the timely prediction of fire development in tunnels. This paper targets to achieve a real-time prediction (within 1 s) of the spatial-temporal temperature distribution inside the numerical tunnel model by using artificial intelligence (AI) methods. A CFD database of 100 simulated tunnel fire scenarios under various fire location, fire size, and ventilation condition is established. The proposed AI model combines a Long Short-term Memory (LSTM) model and a Transpose Convolution Neural Network (TCNN). The real-time ceiling temperature profile and thousands of temperature-field images are used as the training input and output. Results show that the predicted temperature field 60 s in advance achieves a high accuracy of around 97%. Also, the AI model can quickly identify the critical temperature field for safe evacuation (i.e., a critical event) and guide emergency responses and firefighting activities. This study demonstrates the promising prospects of AI-based fire forecasts and smart firefighting in tunnel spaces.