Corrosion of reinforced concrete structures has become a major problem worldwide, leading to very high repair costs. A dearth of studies has focused on the corrosion damage evolution of reinforced concrete. In this paper, the ultrasonic guided wave (UGW) technique is adopted to monitor the reinforced concrete corrosion damage evolution process. The properties of different guide wave modes were studied by steel rebar dispersion curves of UGWs through numerical calculation. The availability and validity of the UGW testing-reinforced concrete corrosion damage is proved by corrosion experiment. The experiment shows that the first wave peak value could describe the whole process of steel rebar corrosion. As the corrosion damage level increases, the relative variation for the first UGW peak value increases first and then decreases.

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Abstract:The coupling beam damper is a fundamental energy dissipation component in coupling shear wall structures that directly influences the performance of the shear wall. Here, we proposed a two-fold design method that can give better energy dissipation performance and hysteretic behavior to coupling beam dampers. First, we devised four in-plane yielding coupling beam dampers that have different opening types but the same amount of total materials. Then the geometry parameters of each opening type were optimized to yield the maximum hysteretic energy. The search for the optimal parameter set was realized by implementing the Kriging surrogate model which iterates randomly selected input shape parameters and the corresponding hysteretic energy calculated by the infinite element method. By comparing the maximum hysteretic energy in all four opening types, one type that had the highest hysteresis energy was selected as the optimized design. This optimized damper has the advantages of having a simple geometry and a high dissipation energy performance. The proposed method also provided a new framework for the design of in-plane coupling beam dampers.

Strain energy decomposition methods in phase field fracture models separate strain energy that contributes to fracture from that which does not. However, various decomposition methods have been proposed in the literature, and it can be difficult to determine an appropriate method for a given problem. The goal of this work is to facilitate the choice of strain decomposition method by assessing the performance of three existing methods (spectral decomposition of the stress or the strain and deviatoric decomposition of the strain) and one new method (deviatoric decomposition of the stress) with several benchmark problems. In each benchmark problem, we compare the performance of the four methods using both qualitative and quantitative metrics. In the first benchmark, we compare the predicted mechanical behavior of cracked material. We then use four quasi-static benchmark cases: a single edge notched tension test, a single edge notched shear test, a three-point bending test, and a L-shaped panel test. Finally, we use two dynamic benchmark cases: a dynamic tensile fracture test and a dynamic shear fracture test. All four methods perform well in tension, the two spectral methods perform better in compression and with mixed mode (though the stress spectral method performs the best), and all the methods show minor issues in at least one of the shear cases. In general, whether the strain or the stress is decomposed does not have a significant impact on the predicted behavior.

Summary Stress corrosion is a major failure type of prestressed steel strands damage. Currently, no effective monitoring method exists. This paper is an analysis of the acoustic emission (AE) characteristic signal from the stress corrosion damage to prestressed steel strands using the ant colony optimization and self‐organizing feature mapping. First, AE characteristic signals at the different stages of the stress corrosion were obtained through the stress corrosion experiments on prestressed steel strands, which can primarily present the corrosion mechanism and different corrosion sources. Subsequently, the ant colony optimization was applied to analyze the AE characteristic signals of stress corrosion. This resulted in the identification of the four main types of AE sources of stress corrosion on prestressed steel strands. The AE ant colony optimization cluster analysis, based on the principal component analysis technology, can separate the four types of damage sources totally and judge the evolution process of corrosion damage and broken wires signal easily. Finally, the self‐organizing feature mapping neural network technology applied to the pattern recognition of stress corrosion on prestressed steel strands. The AE characteristic parameter distribution of different clusters can be realized.