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Acoustic emission technique to identify stress corrosion cracking damage
V. Soltangharaei,J.W. Hill,Li Ai,R. Anay,B. Greer,M. Bayat,P. Ziehl 국제구조공학회 2020 Structural Engineering and Mechanics, An Int'l Jou Vol.75 No.6
In this paper, acoustic emission (AE) and pattern recognition are utilized to identify the AE signal signatures caused by propagation of stress corrosion cracking (SCC) in a 304 stainless steel plate. The surface of the plate is under almost uniform tensile stress at a notch. A corrosive environment is provided by exposing the notch to a solution of 1% Potassium Tetrathionate by weight. The Global b-value indicated an occurrence of the first visible crack and damage stages during the SCC. Furthermore, a method based on linear regression has been developed for damage identification using AE data.
Analytical approach on nonlinear vibration of dry cask storage systems
M. Bayat,V. Soltangharaei,P. Ziehl 국제구조공학회 2020 Structural Engineering and Mechanics, An Int'l Jou Vol.75 No.2
In this paper, a novel analytical method, Max-Min Approach (MMA), has been presented and applied to consider the nonlinear vibration of dry cask storage systems. The nonlinear governing equation of the structure has been developed using the shell theory. The MMA results are compared with numerical solutions derived by Runge-Kutta’s Method (RKM). The results indicate a satisfying agreement between MMA and numerical solutions. Parametric studies have been conducted on the nonlinear frequency of dry casks. The phase-plan of the problem is also presented and discussed. The proposed approach can potentially ca be extended to highly nonlinear problems.
Bayesian demand model based seismic vulnerability assessment of a concrete girder bridge
Bayat, M.,Kia, M.,Soltangharaei, V.,Ahmadi, H.R.,Ziehl, P. Techno-Press 2020 Advances in concrete construction Vol.9 No.4
In the present study, by employing fragility analysis, the seismic vulnerability of a concrete girder bridge, one of the most common existing structural bridge systems, has been performed. To this end, drift demand model as a fundamental ingredient of any probabilistic decision-making analyses is initially developed in terms of the two most common intensity measures, i.e., PGA and Sa (T<sub>1</sub>). Developing a probabilistic demand model requires a reliable database that is established in this paper by performing incremental dynamic analysis (IDA) under a set of 20 ground motion records. Next, by employing Bayesian statistical inference drift demand models are developed based on pre-collapse data obtained from IDA. Then, the accuracy and reasonability of the developed models are investigated by plotting diagnosis graphs. This graphical analysis demonstrates probabilistic demand model developed in terms of PGA is more reliable. Afterward, fragility curves according to PGA based-demand model are developed.