ANALYSIS OF COMPOSITE BUILDINGS UNDER FIRE CONDITIONS

In this paper, the performances of a generic three dimensional 45m x 45m composite floor subjected to ISO834 Fire and Natural Fire are investigated. The influences of reinforcing steel mesh and vertical support conditions on the tensile membrane action of floor slabs are investigated in details. Two robust 2-node connection element models developed by the authors are used to model the behaviour of end-plate and partial end-plate connections of composite structures under fire conditions. The impact of connections on the 3D behaviour of composite floor is considered. Based on the results obtained, some design recommendations are proposed to enhance the fire safety design of composite buildings

A simplified model for modelling flexible end-plate connection in fire

In this paper a simplified robust 2-noded connection element has been developed for modelling the flexible end-plate connections at elevated temperatures. In this model, the two stage behaviours of flexible end-plate connection are considered. The model has the advantages of both the simple and component-based models. It is computationally efficient and has very good numerical stability under static solver conditions. A total of 14 tests are used to validate the model, demonstrating that this new connection model has the capability to accurately predict the behaviour of the flexible end-plate connections at elevated temperatures. The model can be used to simulate the flexible end-plate connections in real performance-based fire resistance design of steel-framed composite buildings

An embedded FE model for modelling reinforced concrete slabs in fire

This is the post-print version of the final paper published in Engineering Structures. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2008 Elsevier B.V.It is evident from a series of tests on simply supported reinforced concrete slabs that the failure of the slabs at large deflections is due to the formation of individual large cracks. This failure mode was also observed in the Cardington full-scale fire tests. Previous research indicates that the global behaviour of concrete slabs subject to large deflections can be well predicted by the smeared cracking model; however, the model cannot quantitatively predict the openings of individual cracks within the slabs at large deflections. For the discrete approach it is usually assumed that the cracks are formed along element edges, therefore continuous re-meshing is required during the analysis. Consequently, the results are mesh-dependent and the computing cost is high. In recent years, mesh independent finite element procedures, such as embedded (EFEM) and extended (XFEM) approaches, were widely used for modelling of the crack initiation and growth in structural members. However, most of the meshless models developed are either based on in-plane loading conditions or confined to thin shells with assumed full-depth cracks, which form apparent displacement jumps within an element. For a reinforced concrete slab, an out-of-plane load causes coupled stretching and bending of the slab, cracks are usually initiated at discrete positions and then propagated, until at last some individual full-depth cracks are formed. Pure stretching or assumed full-depth cracking is inadequate for modelling this kind of failure. Therefore, in this research, a non-linear layered procedure with embedded weak discontinuity is developed to quantitatively model the progressive tensile failure of reinforced concrete slabs subjected to large deflections. The current model inherits the advantage of the smeared approach, and at the same time, introduces the opening width of crack explicitly by taking the advantage of the better description of the kinematic characteristics of the EFEM approach. A series of validations have been conducted against test data at both ambient and elevated temperatures, and the research shows that the model developed in this paper is not sensitive to the FE mesh size and the aspect ratio of the slab. The results predicted by the model developed agreed well with the test data in terms of deflection and crack open width, also agreeing well with those modelled by the smeared model. Hence, this new approach provides a numerical method to predict the load capacity as well as identifying the occurrence and severity of crack failure in reinforced concrete slabs subjected to extreme loading conditions, such as fire

Diminishing Mott gap by doping electrons through depositing one monolayer thin film of Rb on Ca2_{2}CuO2_{2}Cl2_{2}

Understanding the doping evolution from a Mott insulator to a superconductor probably holds the key for resolving the mystery of unconventional superconductivity in copper oxides. To elucidate the evolution of the electronic state starting from the Mott insulator, we dose the surface of the parent phase Ca$_{2}$CuO$_{2}$Cl$_{2}$ by depositing one monolayer thin film of Rb atoms which are supposed to donate electrons to the CuO$_{2}$ planes underneath. We successfully achieved the Rb thin films with periodic structures, and the scanning tunneling microscopy or spectroscopy (STM or STS) measurements on the surface show that the Fermi energy is pinned within the Mott gap but more close to the edge of the charge transfer band (CTB). However, the electron doping does not reduce the spectra weight of the upper Hubbard band (UHB) for the double occupancy as expected from the rigid model, but instead increase it; meanwhile, further doping will create a new wide spread in gap states derivative from the UHB, and the Mott gap will be significantly diminished. Our results provide new clues to understand the strong correlation effect of parent Mott insulators for cuprates and shed new light on the origin of high-temperature superconductivity

A convolutional neural network based deep learning methodology for recognition of partial discharge patterns from high voltage cables

It is a great challenge to differentiate partial discharge (PD) induced by different types of insulation defects in high-voltage cables. Some types of PD signals have very similar characteristics and are specifically difficult to differentiate, even for the most experienced specialists. To overcome the challenge, a convolutional neural network (CNN)-based deep learning methodology for PD pattern recognition is presented in this paper. First, PD testing for five types of artificial defects in ethylene-propylene-rubber cables is carried out in high voltage laboratory to generate signals containing PD data. Second, 3500 sets of PD transient pulses are extracted, and then 33 kinds of PD features are established. The third stage applies a CNN to the data; typical CNN architecture and the key factors which affect the CNN-based pattern recognition accuracy are described. Factors discussed include the number of the network layers, convolutional kernel size, activation function, and pooling method. This paper presents a flowchart of the CNN-based PD pattern recognition method and an evaluation with 3500 sets of PD samples. Finally, the CNN-based pattern recognition results are shown and the proposed method is compared with two more traditional analysis methods, i.e., support vector machine (SVM) and back propagation neural network (BPNN). The results show that the proposed CNN method has higher pattern recognition accuracy than SVM and BPNN, and that the novel method is especially effective for PD type recognition in cases of signals of high similarity, which is applicable for industrial applications

PARP inhibitor-related haemorrhages: What does the real-world study say?

BackgroundPARP inhibitors (PARPis) are novel molecular targeted therapeutics for inhibition of DNA repair in tumor cells, which are commonly used in ovarian cancer. Recent case reports have indicated that haemorrhages-related adverse events may be associated with PARPis. However, little is known about the characteristics and signal strength factors of this kind of adverse event.MethodsA pharmacovigilance study from January 2004 to March 2022 based on the FDA adverse event reporting system (FAERS) database was conducted by adopting the proportional imbalance method based on the four algorithms, including the reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural networks (BCPNN) and multi-item gamma Poisson shrinker (MGPS).Results725 cases of PARPi-haemorrhages-related adverse events were identified with a fatality rate of 4.72% (30/725) and a median age of 67 years. About 88% of the adverse events occurred within 6 months, and the median duration (IQR) was 68 days. Most adverse events (n=477, 75.11%) were related to the treatment of niraparib. Importantly, niraparib exposure was associated with a significant increase in haemorrhages-related adverse events (ROR (95% CI), 1.13(1.03,1.23), PRR (χ2), 1.12(7.32), IC (IC 025), 0.17(0.15). In addition, petechiae, gingival bleeding, bloody urine, as well as rectal haemorrhage should be monitored when using niraparib.ConclusionRecognition and management of PARPi-haemorrhages-related adverse events is of significance to clinical practice. In this study, we provided a safety signal that haemorrhage-related adverse events should be monitored for when using niraparib. However, larger and more robust post-market safety studies are needed to improve the quality of this evidence