Fatigue improvement of rib-to-deck welded joints using adhesively bonded steel patches - LEFM-based parametric study

Rib-to-deck welded joints in orthotropic steel decks of bridges are prone to develop fatigue cracks. Repairing such cracks allows for prolonging the life of the infrastructure, provided that structural safety is guaranteed. Among others, the repair technique of adhesively bonded steel patches is relatively simple and inexpensive. Moreover, it has the advantage that the original structure is not altered, e.g. as in the case of crack stop holes. This technique has been used in other details like the diaphragm of steel bridges. However, limited information is available when applied to rib-to-deck joints, which are the most common joint type in orthotropic steel decks. This study proposes two repair schemes using adhesively bonded steel patches for the rib-to-deck joints. A 3-D finite element model which takes account of the crack size is established. Based on linear elastic fracture mechanics (LEFM), a parametric study is performed to investigate the effect of patch geometry and adhesive thickness on the performance of repair schemes

Fight Fire with Fire: Combating Adversarial Patch Attacks using Pattern-randomized Defensive Patches

Object detection has found extensive applications in various tasks, but it is also susceptible to adversarial patch attacks. Existing defense methods often necessitate modifications to the target model or result in unacceptable time overhead. In this paper, we adopt a counterattack approach, following the principle of "fight fire with fire," and propose a novel and general methodology for defending adversarial attacks. We utilize an active defense strategy by injecting two types of defensive patches, canary and woodpecker, into the input to proactively probe or weaken potential adversarial patches without altering the target model. Moreover, inspired by randomization techniques employed in software security, we employ randomized canary and woodpecker injection patterns to defend against defense-aware attacks. The effectiveness and practicality of the proposed method are demonstrated through comprehensive experiments. The results illustrate that canary and woodpecker achieve high performance, even when confronted with unknown attack methods, while incurring limited time overhead. Furthermore, our method also exhibits sufficient robustness against defense-aware attacks, as evidenced by adaptive attack experiments

Time-dependent rheological behaviour of bacterial cellulose hydrogel

This paper was accepted for publication in the journal Materials Science and Engineering C and the definitive published version is available at http://dx.doi.org/10.1016/j.msec.2015.08.019© 2015 Elsevier B.V. All rights reserved. This work focuses on time-dependent rheological behaviour of bacterial cellulose (BC) hydrogel. Due to its ideal biocompatibility, BC hydrogel could be employed in biomedical applications. Considering the complexity of loading conditions in human body environment, time-dependent behaviour under relevant conditions should be understood. BC specimens are produced by Gluconacetobacter xylinus ATCC 53582 at static-culture conditions. Time-dependent behaviour of specimens at several stress levels is experimentally determined by uniaxial tensile creep tests. We use fraction-exponential operators to model the rheological behaviour. Such a representation allows combination of good accuracy in analytical description of viscoelastic behaviour of real materials and simplicity in solving boundary value problems. The obtained material parameters allow us to identify time-dependent behaviour of BC hydrogel at high stress level with sufficient accuracy

Seismic Fragility Analysis of the Reinforced Concrete Continuous Bridge Piers Based on Machine Learning and Symbolic Regression Fusion Algorithms

In the fragility analysis, researchers mostly chose and constructed seismic intensity measures (IMs) according to past experience and personal preference, resulting in large dispersion between the sample of engineering demand parameter (EDP) and the regression function with IM as the independent variable. This problem needs to be solved urgently. Firstly, the existing 46 types of ground motion intensity measures were taken as a candidate set, and the composite intensity measures (IMs) based on machine learning methods were selected and constructed. Secondly, the modified Park–Ang damage index was taken as EDP, and the symbolic regression method was used to fit the functional relationship between the composite intensity measures (CIMs) and EDP. Finally, the probabilistic seismic demand analysis (PSDA) and seismic fragility analysis were performed by the cloud-stripe method. Taking the pier of a three-span continuous reinforced concrete hollow slab bridge as an example, a nonlinear finite element model was established for vulnerability analysis. And the composite IM was compared with the linear composite IM constructed by Kiani, Lu Dagang, and Liu Tingting. The functions of them were compared. The analysis results indicated that the standard deviation of the composite IM fragility curve proposed in this paper is 60% to 70% smaller than the other composite indicators which verified the efficiency, practicality, proficiency, and sufficiency of the proposed machine learning and symbolic regression fusion algorithms in constructing composite IMs