Highway bridges in fire: characterisation of fire loading and structural behaviour

In bridge design, extreme hazards have been considered as design loads for years, including wind, earthquake, snow, and floods; but fire hazard is not usually considered in the design process. However, severe fire accidents occurring near or under bridges are not as rare as generally perceived compared to the other extreme hazards, especially earthquakes and floods. Therefore fire resistance of bridges along the most critical arteries of transport networks, carrying heavy traffic, should be considered. This should ideally be based upon an estimation of the consequences of a particular level of bridge damage in terms of social and economic costs. Since there are no codes or standards relating to fire resistance of bridges, assessment must rely upon a performance-based engineering approach. In conducting performance-based studies of bridge fire resistance, most previous researchers have used code-based fire curves, such as the ISO 834 standard or Hydrocarbon fires, which assume uniform heating along the entire bridge span. However, a real vehicle fire will naturally create a non-uniform, localised fire under the bridge span and the hazard intensity will decay with distance away from the burning vehicle. If such a scenario could be implemented in a more realistic fire model, then more realistic thermal and thermo-mechanical response of structures could be predicted, resulting in more reliable estimates of performance. This thesis consists of three main parts. Part I investigates the structural performance of composite steel-framed bridges and the influence of bridge shape on failure time under code-based Hydrocarbon fire loading. Part II uses the CFD-based fire dynamics simulation code FDS to generate design fire curves for four different classes of vehicles. The design fire curves include the expected decay in the intensity of the heat flux due to the fire along the bridge span. These curves were then generalised as mathematical functions that can be easily used by engineers and designers in the assessment of the performance of existing bridges under realistic hazard scenarios, for fire resistance design. Rectangular bridge models were subjected to the most extreme class of design fire (fuel tanker fires) in order to compare with the Hydrocarbon fire. The analysis showed that, for the bridge structure considered, there is no failure for the model in the fuel tanker fire scenario, even with conservative assumptions. However, failure may occur if a higher heat release rate is used, which is possible for large fuel tanker fires. In Part III the new design curves (developed as mathematical functions) were implemented into the OpenSees software framework to enable a seamless simulation from fire, to heat transfer and structural response

Fire resistance of composite steel & concrete highway bridges

201807 bcw

Quantized cooperative output regulation of continuous-time multi-agent systems over switching graph

summary:This paper investigates the problem of quantized cooperative output regulation of linear multi-agent systems with switching graphs. A novel dynamic encoding-decoding scheme with a finite communication bandwidth is designed. Leveraging this scheme, a distributed protocol is proposed, ensuring asymptotic convergence of the tracking error under both bounded and unbounded link failure durations. Compared with the existing quantized control work of MASs, the semi-global assumption of initial conditions is not required, and the communication graph is only required to be jointly connected. Finally, two simulation examples demonstrate the effectiveness of the proposed distributed protocol for bounded and unbounded link failure durations

An integrated tool for performance based engineering of structures in fire

Performance based engineering (PBE) is increasingly recognised as the gold standard for ensuring structural safety under extreme loading conditions such as a post-flashover fire. While no universally agreed methodology exists for implementing PBE for various kinds of extreme loadings in general, there are three clearly defined stages for doing so in order to design or assess structural resistance under fire loading. The fire loading is characterised in the first stage, which may range from simple prescribed time-temperature relationships if standard fires are adopted, which is against the spirit of PBE, to an expensive computational fluid dynamics simulation, which in most cases would constitute overkill. A number of options are available and gradually being developed that lie between these two extremes. A realistic characterisation of the load should in general allow the possibility of non-uniform heat fluxes to structural surfaces, which makes the second stage of determining structural temperatures very tedious. Furthermore, the computational models used in the third stage of determining nonlinear structural response are usually very different from the models used in the second stage thereby requiring significant manual intervention by the analyst. In the author’s view this, bar the need for further research on realistic fire scenarios, is the greatest obstacle in carrying out PBE for structural fire resistance design. This paper presents a simulation tool developed within the open source software framework OpenSees with the aim of integrating all the stages of the analysis discussed earlier in order to make PBE feasible even for design offices with modest resources in terms of trained analysts and computing hardware

CASM-AMFMNet: A Network based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases

CASM-AMFMNet: A Network based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases