Risk analysis in quality assessment of ready-mixed concrete using fuzzy logic
 
More details
Hide details
1
Politechnika Rzeszowska, Wydział Budownictwa, Inżynierii Środowiska i Architektury, Katedra Geodezji i Geotechniki, Poznańska 2, Rzeszów, 35-084
 
2
Politechnika Rzeszowska, Wydział Budownictwa, Inżynierii Środowiska i Architektury, Katedra Konstrukcji Budowlanych, Poznańska 2, Rzeszów 35-084
 
 
Publication date: 2023-06-07
 
 
Cement Wapno Beton 28(1) 26-39 (2023)
 
KEYWORDS
ABSTRACT
The decision to include the considered batch of concrete in the designed class depends on the satisfaction of the conditions imposed on the strength of each individual result and the average value. The concrete conformity criteria are formulated in EN 206+A1:2016. When considering risk in concrete quality assessment, it can be assumed that there are three levels of result: low, medium, and high risk in quality assessment. Using logical operations on fuzzy sets, inference rules can be constructed to establish relationships between different variables. The paper presents an analysis of the risk of produced concrete carried out for two input parameters. Parameters on the average compressive strength of concrete and online defects obtained during compliance checks. Defects are identified by the probability of their occurrence. The third parameter introduced relates to the consequences of the occurrence of events identified with the obtained defectiveness after the compliance check of the compressive strength of the concrete produced. When verifying the compressive strength of concrete based on a sample size of n = 3, with the result obtained of a mean value of 28 MPa and a defect before and after conformity control defined at the medium defectiveness, the risk regarding the correct assessment of the quality of the produced concrete is medium
REFERENCES (23)
1.
EN 206:2013+A1:2016: Concrete - Requirements, properties, production and conformity.
 
2.
L. Czarnecki (ed.), Beton według normy PN-EN 206-1 - komentarz. Polski Cement, Kraków 2004.
 
3.
D. Breysse, X. Romão, M. Alwash, Z.M. Sbartaï, V.A.M. Luprano, Risk evaluation on concrete strength assessment with NDT technique and conditional coring approach. J. Build. Eng. 32, 101541 (2020). https://doi.org/10.1016/j.jobe....
 
4.
M. Mohamed, D.Q. Tran, Risk-based inspection for concrete pavement construction using fuzzy sets and bayesian networks. Automat. Constr. 128, 103761 (2021). https://doi.org/10.1016/j.autc....
 
5.
L. Sun, W. Gu, Pavement condition assessment using fuzzy logic theory and analytic hierarchy process. J. Transp. Eng. 137, 648 – 655 (2011). https://doi.org/10.1061/(asce)....
 
6.
I. Skrzypczak, Analysis of concrete quality assessment criteria and their impact on the manufacturer’s and recipient’s risk. Oficyna Wydawnicza Politechniki Rzeszowskiej, Rzeszów, 2013 (in Polish).
 
7.
Sz. Woliński, Ryzyko w projektowaniu konstrukcji z betonu, ZNPG, 59, 55-61 (2006).
 
8.
Sz. Woliński, Conformity control of concrete strength based on the risk assessment. ZN PRz, 53, 163-169 (2009).
 
9.
Sz. Woliński, Ocena jakości betonu metodami normowymi i według logiki rozmytej. Dni Betonu 2006, 1121-1131 (2006).
 
10.
Q. Mascarenhas Guedes, L. Gopfert, Study of the risks related to conformity criteria by CEB for important lots of concrete. Mater. Struct. 16, 269-273 (1983). https://doi.org/10.1007/BF0247....
 
11.
S. Tesfamariam, H. Najjaran, Adaptive network-fuzzy inferencing to estimate concrete strength using mix design. NRCC-49681, http://irc.nrc-cnrcqc.ca.
 
12.
B. Möller, M. Beer, Fuzzy Randomness Uncertainty in Civil Engineering and Computational Mechanics. Springer, Berlin, 2004.
 
13.
M. Neshat, A. Adeli, G. Sepidnam, M. Sargolzaei, Comparative study on fuzzy inference system for prediction of concrete compressive strength, Int. J. Phys. Sci. 7(3), 440-456 (2012). https://doi.org/10.5897/IJPS11....
 
14.
L. Zadeh, Fuzzy sets. Inform. Contr., 8, 338-353 (1965). https://doi.org/10.1016/S0019-....
 
15.
L. Czarnecki, H. Justnes, Sustainable & durable concrete. Cem. Wapno Beton. 17(6), 341 – 360 (2012).
 
16.
D. Straub, M.H. Faber, Risk based inspection planning for structural systems. Struct. Saf. 27 335 – 355 (2005). https://doi.org/10.1016/j.stru....
 
17.
S.S. Leu, C.M. Chang, Bayesian-network-based safety risk assessment for steel construction projects. Accid. Anal. Prev. 54 122 – 133 (2013). https://doi.org/10.1016/j.aap.....
 
18.
R. Yager, D. Filev, Fundamentals of fuzzy modeling and control. WN-T, Warszawa, 1995 (in Polish).
 
19.
J. Ozolos, A. Borisov, Fuzzy classification based on pattern projections analysis. Pattern Recog. 34, 763-781 (2001). https://doi.org/10.1016/S0031-....
 
20.
L. Brunarski, Podstawy matematyczne kształtowania kryteriów zgodności wytrzymałości materiałów. Prace naukowe ITB, WITB, Warszawa, 2009 (in Polish).
 
21.
L. Taerwe, Evaluation of compound compliance criteria for concrete strength. Mater. Struct. 21, 13-20 (1988). https://doi.org/10.1007/BF0247....
 
22.
L. Taerwe, The influence of autocorrelation on OC-lines of compliance criteria for concrete strength. Mater. Struct. 20, 418-427 (1987). https://doi.org/10.1007/BF0247....
 
23.
I. Skrzypczak, W. Kokoszka, I. Zięba, A. Leśniak, D. Bajno, Ł. Bednarz, A Proposal of a Method for Ready-Mixed Concrete Quality Assessment Based on Statistical-Fuzzy Approach. Materials 13(24), 5674 (2020). https://doi.org/10.3390/ma1324....
 
ISSN:1425-8129
Journals System - logo
Scroll to top