In the present study, a comparative assessment on the performance of conventional and advanced tunnel lining materials subjected to blast loading is done using a three-dimensional non-linear finite element analysis procedure. The conventional tunnel lining materials analyzed herein are plain concrete, steel, reinforced cement concrete, and steel fiber-reinforced concrete. The advanced tunnel lining materials analyzed herein are dytherm, polyurethane, and aluminum syntactic foam sandwich panels with steel-foam-steel composites. The pressure generated by 10 kg Trinitrotoluene (TNT) is applied to each element on the inner wall of the tunnel which has an effect equal to the scaled distance Z = 1.16 m/kg 1/3. Analyses are conducted by varying the thickness of lining materials for a tunnel built in rock domain. The response of the tunnel lining materials, for example, deformation, stresses, and strains generated at different interfaces, is compared with each other to assess the best suitable material for the present blast scenario discussed herein. It is observed from the simulations that the reinforced cement concrete and steel-aluminum syntactic foam (90 µm)-steel are found to be the suitable tunnel lining materials for the present blasting scenario described herein. Moreover, a set of probabilistic analysis is also performed for the suitable tunnel lining materials decided through deterministic analyses using Monte Carlo simulations. The results obtained are normal random distribution curves depicting the extent of deformation in lining materials. A probability failure curve is also proposed for the suitable lining materials.

Thermal conductivity and specific heat of concrete are highly influential parameters for the heat transfer into the material during fire exposure. Reviewing the available literature has shown that there is a large scatter in the data for these thermal parameters. To quantify that uncertainty, novel probabilistic models for thermal conductivity and specific heat of concrete at elevated temperatures are developed.Analysis of available experimental data indicates that a temperature-dependent Gamma distribution can be recommended for both thermal properties. Closed-form equations for the temperature-dependent mean and standard deviation are derived. Thus, for both the thermal conductivity and the specific heat, a continuous probability distribution as a function of temperature is obtained, which can be easily implemented in numerical simulations. Using the example of the probabilistic analysis of a simply supported concrete slab exposed to the standard fire, the models are compared with the commonly used deterministic representation of the thermal properties. It is shown that the calculated probabilities of failure using the deterministic models are an order of magnitude lower and therefore unconservative. This analysis suggests that accounting for the uncertainty in thermal properties for concrete slabs can have a significant effect on evaluating the safety and therefore should not be ignored in cases of high importance.

The interest in probabilistic methodologies to demonstrate structural fire safety has increased significantly in recent times. However, the evaluation of the structural behavior under fire loading is computationally expensive even for simple structural models. In this regard, machine learning-based surrogate modeling provides an appealing way forward. Surrogate models trained to simulate the behavior of structural fire engineering (SFE) models predict the response at negligible computational expense, thereby allowing for rapid probabilistic analyses and design iterations. Herein, a framework is proposed for the probabilistic analysis of fire exposed structures leveraging surrogate modeling. As a proof-of-concept a simple (analytical) non-linear model for the capacity of a concrete slab and an advanced (numerical) model for the capacity of a concrete column are considered. First, the procedure for training surrogate models is elaborated. Subsequently, the surrogate models are developed, followed by a probabilistic analysis to evaluate the probability density functions for the capacity. The results show that fragility curves developed based on the surrogate model agree with those obtained through direct sampling of the computationally expensive model, with the 10 -2 capacity quantile predicted with an error of less than 5%. Moreover, the computational cost for the probabilistic studies is significantly reduced by the adoption of surrogate models.

Common structural fire design relies on recommendations from design codes, or (a single or small set of) more advanced numerical analyses. When applying such procedures to the design of structures under normal loading conditions, adequate safety is ensured through calibrated safety factors and ample experience with structural failures. This is however not the case when considering accidental fire loading, where the stochasticity in the structural fire behaviour is rarely fully acknowledged. Therefore, a significant interest in the use of probabilistic approaches to evaluate structural fire performance, which take into account the uncertainty associated with model parameters, can be observed among researchers, with a special focus on the development of fragility curves. The calculation of fragility curves is, however, a laborious task, demanding huge computational expense, mainly attributed to the adoption of advanced calculation procedures and the need for a large number of model evaluations. The present study contributes to addressing the limitations imposed by these computational requirements through the development of surrogate models for fire exposed structural members. To achieve this, a framework for carrying out probabilistic studies of structures under fire through the use of surrogate modelling is presented. The framework is applied to a concrete column subjected to a standard fire and proves efficiency and accurateness for the selected simple example. Future studies will investigate the applicability of the framework to structural assemblies under physically-based fires.

Purpose Despite recognizing the significance of risk-based frameworks in fire safety engineering, the usual approach in structural fire design is largely member/component level, wherein effect of uncertainties influencing the fire resistance of structures are not explicitly considered. In this context, a probabilistic framework is presented to investigate the vulnerability of a reinforced concrete (RC) members and structure under fire loading scenario. Design/methodology/approach The RC structures exposed to fire are modeled in a finite element (FE) platform incorporating material and geometric nonlinearity, in which the transient thermo-mechanical analysis is carried out by suitably incorporating the temperature variation of thermal and mechanical properties of both concrete and steel rebar. The stochasticity in the system is considered in structural resistance, thermal and fire model parameters, and the subsequent fragility curves are developed considering threshold limit state of deflection. Findings The fire resistance of RC structure is reported to be significantly lower in comparison to the RC members, thereby illustrating the current prescriptive design approaches based on studies of structural member behavior to be crucial from a safety and reliability point of view. Practical implications The framework developed for the vulnerability assessment of RC structures under fire hazard through FE analysis can be effectively used to estimate the structural fire resistance for other similar structure to enhance safety and reliability of structures under such extreme threats. Originality/value The paper proposes a novel methodology for vulnerability assessment of three-dimensional RC structures under fire hazard through FE analysis and provides comparison of the structural fragility with fragility developed for structural members. Moreover, the research emphasizes to assume 3D behavior of the structure rather than the approximate 2D behavior.