Tensile strength prediction of rock material using non-destructive tests: a comparative intelligent study

2021 ◽  
pp. 100652
Author(s):  
Maryam Parsajoo ◽  
Danial Jahed Armaghani ◽  
Ahmed Salih Mohammed ◽  
Mahdy Khari ◽  
Soheil Jahandari
Holzforschung ◽  
2011 ◽  
Vol 65 (3) ◽  
Author(s):  
Tobias Biechele ◽  
Ying Hei Chui ◽  
Meng Gong

Abstract Non-destructive evaluation (NDE) methods are common for grading structural lumber with static bending as the traditional NDE method for strength. More recently, longitudinal and transverse vibration techniques have also been proposed for grading lumber. In this study, unjointed and finger-jointed sawn lumber has been evaluated by these traditional and relatively new NDE methods. In total, 188 pieces of 38 mm×89 mm black spruce lumber were tested. Of these, 40 were unjointed, 47 had 2–3 finger joints, and 101 had 5–7 finger joints. The main objective was to evaluate the reliability of the various NDE techniques in predicting the bending stiffness and tensile strength of finger-jointed lumber with different number of finger joints. Results show that all NDE methods provide stiffness values of unjointed and finger-jointed lumber that correlate well with laboratory measured static bending stiffness with R2 values ranging from 0.76 to 0.97. Moreover, lumber with finger joints has lower bending stiffness than unjointed lumber. Based on the correlation coefficients, there is no evidence that finger joints affect the precision of the strength prediction by NDE methods.


2020 ◽  
Vol 27 ◽  
pp. 1218-1223
Author(s):  
Sagar Chokshi ◽  
Piyush Gohil ◽  
Amul Lalakiya ◽  
Parth Patel ◽  
Amit Parmar

2017 ◽  
Vol 487 ◽  
pp. 143-157 ◽  
Author(s):  
Manik Bansal ◽  
I.V. Singh ◽  
B.K. Mishra ◽  
Kamal Sharma ◽  
I.A. Khan

1994 ◽  
Vol 29 (22) ◽  
pp. 6033-6040 ◽  
Author(s):  
L. Molliex ◽  
J. -P. Favre ◽  
A. Vassel ◽  
M. Rabinovitch

Author(s):  
M. M. Matlin ◽  
V. A. Kazankin ◽  
E. N. Kazankina ◽  
E. V. Kapinosova

The paper describes a technique for non-destructive determination of the tensile strength of a metal in shearing based on elastic-plastic introduction of an indenter into a test material.


Author(s):  
Akira Shimamoto ◽  
Keitaro Yamashita ◽  
Hirofumi Inoue ◽  
Sung-mo Yang ◽  
Masahiro Iwata ◽  
...  

Destructive tests are generally applied for evaluating fixed strength of spot welding nuggets on zinc plating steel, which is swidely used as the primary automobile structural material. Destructive tests, however, are expensive and time consuming. This paper discusses a non-destructive method for evaluating of welded joints fixed strength by utilizing surface electric resistance. A nugget tester has developed by authors for this purpose. The non-destructive method focuses on surface electric resistance decreasing rate; α, and effect of the corona bond. Nugget diameter is estimated by RQuota calculated from variation of resistance and constant representing the area of the corona bond. Since maximum tensile strength is correlated with the nugget diameter, it is inferred from the estimated nugget diameter.


2020 ◽  
Vol 862 ◽  
pp. 72-77
Author(s):  
José Alberto Guzmán Torres ◽  
Francisco Javier Domínguez Mota ◽  
Elia Mercedes Alonso-Guzmán ◽  
Wilfrido Martínez-Molina ◽  
José Gerardo Tinoco Ruiz ◽  
...  

The inclusion of additions to concrete blends helps to improve performance in certain conditions. The analysis of two concrete blends was performed, a blend with the addition of a natural organic polymer and a control blend to make predictive models and find a correlation. Tree tests were performed: Electrical resistivity (Er) test, Tensile strength (Ft) and Carbonation resistance. One of the most popular non-destructive tests on concrete is , due to the simplicity of measuring readings on concrete elements. It is a non-destructive test that determines the interconnectivity that exists in the concrete cementitious matrix by determining the quality of the concrete. The blend with the addition showed improved performance in all the tests. Data science techniques were used to generate artificial data, the Machine Learning technique (ML) is based on Tree regression (Tr) with satisfactory accuracy to assess the reliability.


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