Efficient method for probabilistic fire safety engineering

A growing interest exists within the fire safety community for the topics of risk and reliability. However, due to the high computational requirements of most calculation models, traditional Monte Carlo methods are in general too time consuming for practical applications. In this paper a computationally very efficient methodology is for the first time applied to structural fire safety. The methodology allows estimating the probability density function which describes the uncertain response of the fire exposed structure or structural member, while requiring only a very limited number of model evaluations. The application of the method to structural fire safety is illustrated by two examples in the area of concrete elements exposed to fire

Reconstructing the Forest of Lineage Trees of Diverse Bacterial Communities Using Bio-inspired Image Analysis

Cell segmentation and tracking allow us to extract a plethora of cell attributes from bacterial time-lapse cell movies, thus promoting computational modeling and simulation of biological processes down to the single-cell level. However, to analyze successfully complex cell movies, imaging multiple interacting bacterial clones as they grow and merge to generate overcrowded bacterial communities with thousands of cells in the field of view, segmentation results should be near perfect to warrant good tracking results. We introduce here a fully automated closed-loop bio-inspired computational strategy that exploits prior knowledge about the expected structure of a colony's lineage tree to locate and correct segmentation errors in analyzed movie frames. We show that this correction strategy is effective, resulting in improved cell tracking and consequently trustworthy deep colony lineage trees. Our image analysis approach has the unique capability to keep tracking cells even after clonal subpopulations merge in the movie. This enables the reconstruction of the complete Forest of Lineage Trees (FLT) representation of evolving multi-clonal bacterial communities. Moreover, the percentage of valid cell trajectories extracted from the image analysis almost doubles after segmentation correction. This plethora of trustworthy data extracted from a complex cell movie analysis enables single-cell analytics as a tool for addressing compelling questions for human health, such as understanding the role of single-cell stochasticity in antibiotics resistance without losing site of the inter-cellular interactions and microenvironment effects that may shape it

Image Analysis on Bacteria Time-Lapse Movies

This study has been developed a methodology for identifying accurately the boundaries of individual bacterial cells and tracking them from frame to frame so as to construct the cells’ genealogy (bacterial cell segmentation and lineage tree construction) even in large-size microbial communities where there is great difficulty in identifying the individual cell boundaries

Probabilistic Finite Element Analysis of Structures using the Multiplicative Dimensional Reduction Method

It is widely accepted that uncertainty may be present for many engineering problems, such as in input variables (loading, material properties, etc.), in response variables (displacements, stresses, etc.) and in the relationships between them. Reliability analysis is capable of dealing with all these uncertainties providing the engineers with accurate predictions of the probability of a structure performing adequately during its lifetime. In probabilistic finite element analysis (FEA), approximate methods such as Taylor series methods are used in order to compute the mean and the variance of the response, while the distribution of the response is usually approximated based on the Monte Carlo simulation (MCS) method. This study advances probabilistic FEA by combining it with the multiplicative form of dimensional reduction method (M-DRM). This combination allows fairly accurate estimations of both the statistical moments and the probability distribution of the response of interest. The response probability distribution is obtained using the fractional moments, which are calculated from M-DRM, together with the maximum entropy (MaxEnt) principle. In addition, the global variance-based sensitivity coefficients are also obtained as a by-product of the previous analysis. Therefore, no extra analytical work is required for sensitivity analysis. The proposed approach is integrated with the OpenSees FEA software using Tcl programing and with the ABAQUS FEA software using Python programing. OpenSees is used to analyze structures under seismic loading, where both pushover analysis and dynamic analysis is performed. ABAQUS is used to analyze structures under static loading, where the concrete damage plasticity model is used for the modeling of concrete. Thus, the efficient applicability of the proposed method is illustrated and its numerical accuracy is examined, through several examples of nonlinear FEA of structures. This research shows that the proposed method, which is based on a small number of finite element analyses, is robust, computational effective and easily applicable, providing a feasible alternative for finite element reliability and sensitivity analysis of practical and real life problems. The results of such work have significance in future studies for the estimation of the probability of the response exceeding a safety limit and for establishing safety factors related to acceptable probabilities of structural failures