Structural reliability updating using experimental data

Author(s):  
Lisha Zhu ◽  
Xianzhen Huang ◽  
Cong Yuan ◽  
Zunling Du
Vestnik MGSU ◽  
2015 ◽  
pp. 27-35
Author(s):  
Aleksandr Aleksandrovich Koyankin ◽  
Valeriy Mikhaylovich Mitasov

Precast-monolithic construction is becoming an increasingly popular form of housing. The wide distribution of this type of construction is explained by the possibility to successfully combine the advantages of precast and monolithic construction, at the same time reducing their disadvantages. Though there is a significant lack of data, including experimental data, for objective assessment of the stress-strain state of precast-monolithic floor structures. In order to investigate the structural reliability of the bolt joint of a bearer with a column in a precast-monolithic building a series of experimental investigations were carried out in the laboratory of testing the building structures of the Siberian Federal University.One of the main conclusions is that the bolt joint of a bearer with a column is characterized by sufficient rigidity, crack resistance and bearing capacity. The results of the given work have proved the data obtained in previously conducted investigations on a fragment of a precast-monolithic ceiling.


2020 ◽  
Vol 1 (3) ◽  
pp. 328-344
Author(s):  
Changkyu Kim ◽  
Do-Eun Choe ◽  
Pedro Castro-Borges ◽  
Homero Castaneda

Corrosion of the reinforced concrete (RC) structures has been affecting the major infrastructures in U.S. and in other continents, causing the recent several bridge collapses and incidents. While the theoretical understanding is well-established, the reliable prediction of the corrosion process in the RC structural systems has hardly been successful due to the inherent uncertainties existed in the electrochemical corrosion process and the associated material and environmental conditions. The paper proposes a computational framework to develop evidence-based probabilistic corrosion initiation models for the reinforcing steels in the RC structures, which predicts the corrosion initiation time and quantifies the inherent variances considering various acting parameters. The framework includes: probabilistic modeling with Bayesian updating based on the sets of previously generated experimental data; Bayesian model/parameter selection considering various parameters, such as material properties and environmental conditions; corrosion reliability analyses to predict the probabilities of the corrosion initiation at given time t, structural configurations, and environmental conditions; and sensitivity analyses to measure and to rank the influences of each acting parameter and its uncertainty to the probabilities of the corrosion initiation. Total of 284 sets of experimental data exposed to the coastal atmospheric environments are used for the modeling. The goal of the Bayesian model selection presented in this paper is to obtain the most accurate and unbiased model using the simplest form of expression. The developed example corrosion model is currently limited to the initiation of diffusion-induced corrosion. The model can be updated, improved, or modified upon future available sets of data. The research contributes to the decision making to improve the corrosion reliability, corrosion control, and further the structural reliability of corroding structures.


Author(s):  
A. Gómez ◽  
P. Schabes-Retchkiman ◽  
M. José-Yacamán ◽  
T. Ocaña

The splitting effect that is observed in microdiffraction pat-terns of small metallic particles in the size range 50-500 Å can be understood using the dynamical theory of electron diffraction for the case of a crystal containing a finite wedge. For the experimental data we refer to part I of this work in these proceedings.


Author(s):  
K.B. Reuter ◽  
D.B. Williams ◽  
J.I. Goldstein

In the Fe-Ni system, although ordered FeNi and ordered Ni3Fe are experimentally well established, direct evidence for ordered Fe3Ni is unconvincing. Little experimental data for Fe3Ni exists because diffusion is sluggish at temperatures below 400°C and because alloys containing less than 29 wt% Ni undergo a martensitic transformation at room temperature. Fe-Ni phases in iron meteorites were examined in this study because iron meteorites have cooled at slow rates of about 10°C/106 years, allowing phase transformations below 400°C to occur. One low temperature transformation product, called clear taenite 2 (CT2), was of particular interest because it contains less than 30 wtZ Ni and is not martensitic. Because CT2 is only a few microns in size, the structure and Ni content were determined through electron diffraction and x-ray microanalysis. A Philips EM400T operated at 120 kV, equipped with a Tracor Northern 2000 multichannel analyzer, was used.


Author(s):  
C. C. Ahn ◽  
D. H. Pearson ◽  
P. Rez ◽  
B. Fultz

Previous experimental measurements of the total white line intensities from L2,3 energy loss spectra of 3d transition metals reported a linear dependence of the white line intensity on 3d occupancy. These results are inconsistent, however, with behavior inferred from relativistic one electron Dirac-Fock calculations, which show an initial increase followed by a decrease of total white line intensity across the 3d series. This inconsistency with experimental data is especially puzzling in light of work by Thole, et al., which successfully calculates x-ray absorption spectra of the lanthanide M4,5 white lines by employing a less rigorous Hartree-Fock calculation with relativistic corrections based on the work of Cowan. When restricted to transitions allowed by dipole selection rules, the calculated spectra of the lanthanide M4,5 white lines show a decreasing intensity as a function of Z that was consistent with the available experimental data.Here we report the results of Dirac-Fock calculations of the L2,3 white lines of the 3d and 4d elements, and compare the results to the experimental work of Pearson et al. In a previous study, similar calculations helped to account for the non-statistical behavior of L3/L2 ratios of the 3d metals. We assumed that all metals had a single 4s electron. Because these calculations provide absolute transition probabilities, to compare the calculated white line intensities to the experimental data, we normalized the calculated intensities to the intensity of the continuum above the L3 edges. The continuum intensity was obtained by Hartree-Slater calculations, and the normalization factor for the white line intensities was the integrated intensity in an energy window of fixed width and position above the L3 edge of each element.


2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.


1981 ◽  
Vol 20 (04) ◽  
pp. 207-212 ◽  
Author(s):  
J. Hermans ◽  
B. van Zomeren ◽  
J. W. Raatgever ◽  
P. J. Sterk ◽  
J. D. F. Habbema

By means of a case study the choice between several methods of discriminant analysis is presented. Experimental data of a two-groups problem with one or two variables is analysed. The different methods are compared according to posterior probabilities which can be computed for each subject and which are the basis of discriminant analysis. These posterior probabilities are analysed graphically as well as numerically.


2020 ◽  
Vol 39 (4) ◽  
pp. 5905-5914
Author(s):  
Chen Gong

Most of the research on stressors is in the medical field, and there are few analysis of athletes’ stressors, so it can not provide reference for the analysis of athletes’ stressors. Based on this, this study combines machine learning algorithms to analyze the pressure source of athletes’ stadium. In terms of data collection, it is mainly obtained through questionnaire survey and interview form, and it is used as experimental data after passing the test. In order to improve the performance of the algorithm, this paper combines the known K-Means algorithm with the layering algorithm to form a new improved layered K-Means algorithm. At the same time, this paper analyzes the performance of the improved hierarchical K-Means algorithm through experimental comparison and compares the clustering results. In addition, the analysis system corresponding to the algorithm is constructed based on the actual situation, the algorithm is applied to practice, and the user preference model is constructed. Finally, this article helps athletes find stressors and find ways to reduce stressors through personalized recommendations. The research shows that the algorithm of this study is reliable and has certain practical effects and can provide theoretical reference for subsequent related research.


Sign in / Sign up

Export Citation Format

Share Document