Fire spread scenarios associated with concealed cavity spaces have been relatively less discussed. The variation in studies with respect to geometry, influential parameters, and protection strategies has been an obstacle to deriving more generalized solutions in terms of cavity fire in buildings. A systematic literature review was conducted following the PRISMA method to identify the conclusive fire behaviour, safety risks, and protection strategies to enable future researchers to address cavity fire scenarios effectively, avoiding catastrophic disasters. This study identified that relative to open-fire scenarios, cavity fires could result in up to 10 times higher flame spread, up to 14 times higher heat exposure, and temperature conditions 13 times higher. Increased toxicity and smoke velocity are also found with cavity fires. Fire protection strategies and their efficiency were identified for a range of cavity geometries. Altogether, cavity spaces, especially narrow ones, cannot be neglected during fire safety, and proper risk identification is required to ensure the safety of the buildings and the occupants in a fire scenario.

In recent years, fiber-reinforced polymers (FRP) have emerged as a highly effective solution for strengthening reinforced concrete (RC) structures. However, accurately assessing the fire resistance of FRP-strengthened members remains a significant challenge due to the limited guidance available in current building codes, often leading to conservative and cost-intensive evaluations. Experimental testing and numerical analysis required for such assessments are resource-demanding, highlighting the need for more efficient methods. This study investigates the application of machine learning (ML) techniques to predict the fire resistance of FRP-strengthened RC beams. Twelve ML models, including eight ensemble methods and four traditional approaches, were employed. The models were trained using a comprehensive dataset comprising over 21,000 data points obtained from numerical simulations and experimental tests. The dataset captured variations in geometric configurations, insulation strategies, loading conditions, and material properties. To enhance predictive accuracy, Bayesian optimization and k-fold cross-validation were applied for model tuning, while the Shapley Additive Explanations (SHAP) method was utilized to assess the relative importance of features influencing fire resistance. Among the models tested, Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Light Gradient Boosting (LGBoost), and Gradient Boosting (GRB) demonstrated superior performance, achieving accuracy rates exceeding 92%. Key factors identified as significantly affecting fire resistance included loading ratio, area of tensile reinforcement, insulation depth, concrete cover thickness, and FRP area. The findings underscore the potential of ensemble ML techniques over traditional methods for accurately predicting the fire resistance of FRP-strengthened RC beams, offering critical insights for optimizing design practices and enhancing structural fire safety.

Fire safety is one of the critical concerns for the design and construction of modular structures. The lack of understanding of cavity fire spread in modular construction could create variations in the fire performance of structural members. This study aimed to assess the impact of cavity fire spread in modular buildings initiated by a room fire using validated fire dynamics and structural numerical models. A comprehensive parametric study was conducted to identify critical thermal conditions affecting adjacent structural members under plausible cavity fire scenarios. The identified critical cavity fire thermal conditions were used to examine the structural performance of cold-formed steel intermediate column specimens while varying geometric configurations, material properties, and boundary conditions. The results highlighted two distinct phases of restrained thermal expansion and lateral deformations under material yielding and buckling, resulting in the loss of structural integrity. The restrained thermal expansion significantly increased axial/restraint forces, reaching up to 155% of the initial load. This behavior decreased axial load capacity by 2.4% to 35% of the ambient capacity. Further, the study identifies a requirement for designing the intermediate columns and the connected intermodular connections for increased design action equivalent to 56% of the service load.

Facade fire testing is a critical component of modern building safety, as it enables the assessment of the fire performance of exterior wall systems and cladding materials under representative fire conditions. With the growing number of skyscrapers worldwide and the widespread use of new construction materials, many of which are combustible or composite, the risk of fire spreading along building facades has become a major concern for architects, engineers, fire authorities, and building owners. The primary purpose of facade fire testing is to understand how different facade configurations and material combinations respond when exposed to fire, determine the likelihood of flame spread and propagation in both horizontal and vertical directions, and support effective fire-safe design decisions. A few established international fire testing standards, such as BS 8414 (United Kingdom) and NFPA 285 (United States), have been developed to determine the fire resistance and flame spread characteristics of facade systems. These standards require large-scale test setups to replicate real fire exposure conditions, including combustible materials, openings, insulation, and fastenings. However, such comprehensive tests are often impractical in developing countries due to economic constraints, limited testing facilities, and a lack of technical expertise. In resource-limited nations like Sri Lanka, where the construction sector is evolving with the introduction of new materials, there is a strong need for a cost-effective, scalable, and scientifically sound alternative. The present study recommends developing a customised methodology for assessing facade fire behaviour in the Sri Lankan context. The proposed approach integrates small-scale fire experiments with computer-based fire simulations using PyroSim software, a graphical interface for the Fire Dynamics Simulator (FDS). The research begins with a systematic survey of facade types commonly used in Sri Lankan buildings. Based on prevalence, material properties, and flammability, a representative facade design is selected for detailed experimental investigation. Controlled small-scale fire tests are then conducted to evaluate key fire parameters such as ignition time, flame spread rate, heat release, and smoke production. Experimental data obtained from the scientific tests are used to develop and validate a numerical model in PyroSim. The model incorporates detailed input parameters such as material properties, fire source characteristics, environmental conditions, and boundary geometries. The simulation is validated by comparing its results with experimental observations to achieve a realistic representation of fire spread behaviour. The developed methodology will serve as a valuable guide for Sri Lankan engineers, architects, and fire safety officials to evaluate facade system performance at lower costs without relying on expensive testing. It supports safer building design, informed material selection, and alignment with performance-based fire safety approaches. Ultimately, the study bridges a critical knowledge gap in Sri Lanka and other developing regions by providing a cost-effective, validated, and adaptable solution for facade fire performance assessment.