Cost-optimization based target reliability for fire design of insulated steel columns

Adequacy of a structural fire design can in theory be demonstrated through a probabilistic risk assessment (PRA) where the compliance with tolerability limits and the ALARP requirement are explicitly ascertained. However, explicit assessment of ALARP requirement is challenging and impractical for day-to-day designs, due to the burden of estimating uncertain future costs and current safety investment costs. For normal design conditions, the use of target reliability indices has been recommended instead. These target reliability indices however have not been defined for structural design under fire events. To address this gap, the current study demonstrates a method to derive target reliability indices for a fire-exposed structure. As a case study, an insulated steel column (with varying levels of ISO fire rating) exposed to parametric natural fires is considered. The target reliability indices are derived for the steel column for varying fire exposure scenarios, considering different fire load densities and opening factor, relating to the building occupancies. This study thus investigates the important issue of adopting target reliability indices in fire design that are cost-optimized from quantitative analyses considering natural fire exposures, which has significant implications for fire safety and rational use of resources in the construction industry

Generalized fragility curves for concrete columns exposed to fire through surrogate modelling

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

Simplified modelling of the performance of concrete tunnels during fire and post-fire damage classification

The performance of concrete tunnel structures during and after fire is not well understood. This is an obstacle to the adoption of risk-based approaches for fire safety design of tunnel structures. Upon the request of the Belgian fire safety consultancy FESG, a simplified assessment of the collapse probability and post-fire damages for a reference tunnel structure has been made. The structural system is modelled through 2D beam finite elements, where spalling rates have been assumed based on available literature data. Structural stability is verified for both the heating and cooling phases of the fire. In those cases where the structure survives up to burnout, the residual deformations and thermal damage to the tunnel structure are assessed

Probabilistic models for thermal properties of concrete

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

Computed tomography diagnosis of truncus arteriosus type IV: a case report

Truncus arteriosus (TA) is an uncommon congenital cardiac anomaly of which type IV is considered a rare variant. Recognition is crucial for proper treatment planning. The prognosis without treatment is poor. Echocardiography alone may not be useful in evaluation. Computed tomography (CT) finding is complicated. We report an 18 months child with ventricular septal defect (VSD), diagnosed on echocardiography, and further review by CT showed VSD with descending thoracic aorta giving rise to the pulmonary arteries suggestive of pseudo truncus (Collet and Edwards Truncus arteriosus Type IV) and right sided aortic arch with mirror image branching. Keywords: echocardiography, Collet and Edwards, computed tomography (CT), truncus arteriosus, ventricular septal defect (VSD

Perception of Availability, Accessibility, and Affordability of COVID-19 Vaccines and Hesitancy: A Cross-Sectional Study in India

Background: The current study aimed to identify the perceptions and issues regarding the affordability, availability, and accessibility of COVID-19 vaccination and determine the extent of vaccine hesitancy among non-vaccinated individuals. Methods: A Prospective cross-sectional study was conducted among 575 individuals for a period of six months. All the relevant information was collected using the peer-validated survey questionnaire. An independent t-test was applied to check the association between variables. Result: Among 575 participants, 80.8% were vaccinated, and 19.2% were non-vaccinated. Among the vaccinated, 35.1% were vaccinated in private centres and 64.9% in public health centres (PHC). In total, 32% had accessibility issues and 24.5% had availability issues. However, responders vaccinated at PHC were having more issues in comparison to other groups which was statistically significant (p < 0.05). Among the 163 privately vaccinated participants, 69.9% found it completely affordable. Another 26.9% and 3.1% found vaccines partly affordable and a little unaffordable. Among the 110 non-vaccinated, 38.1% were found to be vaccine-hesitant. Conclusions: Individuals vaccinated at PHC experienced issues such as long waiting times, unavailability of doses, and registration. Further, a significant level of hesitancy towards COVID-19 vaccines was observed. The safety and efficacy of COVID-19 vaccines contributed to negative attitudes

Rapeseed-Mustard Breeding in India: Scenario, Achievements and Research Needs

Brassica spp., commonly known as rapeseed-mustard, plays a significant role in the Indian economy by providing edible oils, vegetables, condiments and animal feed. Globally, India holds second and third position in rapeseed-mustard area under cultivation and production, respectively. However, anthropogenically accelerated climate change thwarts yield potential of rapeseed-mustard by employing abiotic (drought, flood, temperature variation and salinity) and biotic (disease and insects) stresses. Various approaches such as molecular breeding, pre-breeding, −omics and biotechnological interventions have been used to develop varieties for improved yield and oil quality, climate resilient and resistance or tolerance to abiotic and biotic stresses. In this context, this chapter highlighted the different cytoplasmic male sterility (CMS) sources and their potential use for hybrid development. At the end, this chapter also enlisted salient achievement by the government and non-government institutes and briefly described the future perspective for improvement of rapeseed-mustard in India

Potential of surrogate modelling for probabilistic fire analysis of structures

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

Cost-optimization based target reliability for fire design of insulated steel columns

Adequacy of a structural fire design can in theory be demonstrated through a probabilistic risk assessment (PRA) where the compliance with tolerability limits and the ALARP requirement are explicitly ascertained. However, explicit assessment of ALARP requirement is challenging and impractical for day-to-day designs, due to the burden of estimating uncertain future costs and current safety investment costs. For normal design conditions, the use of target reliability indices has been recommended instead. These target reliability indices however have not been defined for structural design under fire events. To address this gap, the current study demonstrates a method to derive target reliability indices for a fire-exposed structure. As a case study, an insulated steel column (with varying levels of ISO fire rating) exposed to parametric natural fires is considered. The target reliability indices are derived for the steel column for varying fire exposure scenarios, considering different fire load densities and opening factor, relating to the building occupancies. This study thus investigates the important issue of adopting target reliability indices in fire design that are cost-optimized from quantitative analyses considering natural fire exposures, which has significant implications for fire safety and rational use of resources in the construction industry

Regression-based surrogate models for the probabilistic study of fire exposed composite structures considering tensile membrane action

The probabilistic study of fire exposed structures is laborious and computationally challenging, especially when using advanced numerical models. Moreover, fragility curves developed through traditional approaches apply only to a particular design (structural detailing, fire scenario). Any alteration in design necessitates the computationally expensive re-evaluation of the fragility curves. Considering the above challenges, the use of surrogate models has been proposed for the probabilistic study of fire exposed structures. Previous contributions have confirmed the potential of surrogate models for developing fragility curves for single structural members including reinforced concrete slabs and columns. Herein, the potential of regression-based surrogate models is investigated further with consideration of structural systems. Specifically, an advanced finite element model for evaluating the fire performance of a composite slab panel acting in tensile membrane action is considered. A surrogate model is developed and used to establish fire fragility curves. The results illustrate the potential of surrogate modeling for probabilistic structural fire design of composite structures