Data driven models for compressive strength prediction of concrete at high temperatures

doi:10.1007/s11709-019-0593-8

Front. Struct. Civ. Eng.

2020, Vol. 14

Issue (2) : 311-321 https://doi.org/10.1007/s11709-019-0593-8

RESEARCH ARTICLE

Data driven models for compressive strength prediction of concrete at high temperatures

Mahmood AKBARI(

), Vahid JAFARI DELIGANI

Department of Civil Engineering, Faculty of Engineering, University of Kashan, Kashan 8731753153, Iran

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Abstract

The use of data driven models has been shown to be useful for simulating complex engineering processes, when the only information available consists of the data of the process. In this study, four data-driven models, namely multiple linear regression, artificial neural network, adaptive neural fuzzy inference system, and K nearest neighbor models based on collection of 207 laboratory tests, are investigated for compressive strength prediction of concrete at high temperature. In addition for each model, two different sets of input variables are examined: a complete set and a parsimonious set of involved variables. The results obtained are compared with each other and also to the equations of NIST Technical Note standard and demonstrate the suitability of using the data driven models to predict the compressive strength at high temperature. In addition, the results show employing the parsimonious set of input variables is sufficient for the data driven models to make satisfactory results.

Keywords data driven model compressive strength concrete high temperature

Corresponding Author(s): Mahmood AKBARI

Just Accepted Date: 04 November 2019 Online First Date: 02 March 2020 Issue Date: 08 May 2020

Cite this article:

Mahmood AKBARI,Vahid JAFARI DELIGANI. Data driven models for compressive strength prediction of concrete at high temperatures[J]. Front. Struct. Civ. Eng., 2020, 14(2): 311-321.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-019-0593-8
https://academic.hep.com.cn/fsce/EN/Y2020/V14/I2/311

Fig.1 The general structure of the multi-layer feed-forward Neural Network [⁵²].

Fig.2 Two schematic structures of the input-output of the models.

Tab.1 Range of input/output variables

Tab.2 Details of ANN model

Tab.3 Details of ANFIS model

Tab.4 Details of KNN model

Tab.5 Comparison of results obtained from different models with experimentally data observed for the set 1 of the input variables

Tab.6 Comparison of results obtained from different models with experimentally data observed for the set 2 of the input variables

Fig.3 Scatter plots for MLR model and NIST equations for set 2 over the testing data set.

Fig.4 Scatter plot for ANN model for set 1 over the testing data set.

Fig.5 Scatter plot for ANFIS model for set 2 over the testing data set.

1	O Düğenci, T Haktanir, F Altun. Experimental research for the effect of high temperature on the mechanical properties of steel fiber-reinforced concrete. Construction & Building Materials, 2015, 75: 82–88 https://doi.org/10.1016/j.conbuildmat.2014.11.005
2	G Choe, G Kim, N Gucunski, S Lee. Evaluation of the mechanical properties of 200 MPa ultra-high-strength concrete at elevated temperatures and residual strength of column. Construction & Building Materials, 2015, 86: 159–168 https://doi.org/10.1016/j.conbuildmat.2015.03.074
3	A Ergün, G M Kürklü, B Serhat, M Y Mansour. The effect of cement dosage on mechanical properties of concrete exposed to high temperatures. Fire Safety Journal, 2013, 55: 160–167 https://doi.org/10.1016/j.firesaf.2012.10.016
4	S K Handoo, S Agarwal, S K Agarwal. Physicochemical, mineralogical, and morphological characteristics of concrete exposed to elevated temperatures. Cement and Concrete Research, 2002, 32(7): 1009–1018 https://doi.org/10.1016/S0008-8846(01)00736-0
5	M Li, C Qian, W Sun. Mechanical properties of high-strength concrete after fire. Cement and Concrete Research, 2004, 34(6): 1001–1005 https://doi.org/10.1016/j.cemconres.2003.11.007
6	G L Balázs, E Lubloy. Post-heating strength of fiber-reinforced concretes. Fire Safety Journal, 2012, 49: 100–106 https://doi.org/10.1016/j.firesaf.2012.01.002
7	H Tanyildizi. Variance analysis of crack characteristics of structural lightweight concrete containing silica fume exposed to high temperature. Construction & Building Materials, 2013, 47: 1154–1159 https://doi.org/10.1016/j.conbuildmat.2013.05.060
8	K M Hamdia, M Arafa, M Alqedra. Structural damage assessment criteria for reinforced concrete buildings by using a Fuzzy Analytic Hierarchy Process. Underground Space, 2018, 3(3): 243–249
9	M S Cülfik, T. Özturan Mechanical properties of normal and high strength concretes subjected to high temperatures and using image analysis to detect bond deteriorations. Construction & Building Materials, 2010, 24(8): 1486–1493 https://doi.org/10.1016/j.conbuildmat.2010.01.020
10	S D Venecanin. Thermal incompatibility of concrete components and thermal properties of carbonate rocks. ACI Materials Journal, 1990, 87: 602–607
11	N Yüzer, F Aköz, L D Öztürk. Compressive strength-color change relation in mortars at high temperature. Cement and Concrete Research, 2004, 34(10): 1803–1807 https://doi.org/10.1016/j.cemconres.2004.01.015
12	D N Crook, M J Murray. Regain of strength after firing of concrete. Magazine of Concrete Research, 1970, 22(72): 149–154 https://doi.org/10.1680/macr.1970.22.72.149
13	A Petzold, M Rohrs. Concrete for High Temperatures. Lincoln: Maclaren and Sons Ltd., 1970
14	J Ožbolt, J Bošnjak, G Periškić, A Sharma. 3D numerical analysis of reinforced concrete beams exposed to elevated temperature. Engineering Structures, 2014, 58: 166–174 https://doi.org/10.1016/j.engstruct.2012.11.030
15	A Caggiano, G Etse. Coupled thermo-mechanical interface model for concrete failure analysis under high temperature. Computer Methods in Applied Mechanics and Engineering, 2015, 289: 498–516 https://doi.org/10.1016/j.cma.2015.02.016
16	T Rabczuk, G Zi, S Bordas, H Nguyen-Xuan. A geometrically non-linear three-dimensional cohesive crack method for reinforced concrete structures. Engineering Fracture Mechanics, 2008, 75(16): 4740–4758 https://doi.org/10.1016/j.engfracmech.2008.06.019
17	T Rabczuk, G Zi, S Bordas, H Nguyen-Xuan. A simple and robust three-dimensional cracking-particle method without enrichment. Computer Methods in Applied Mechanics and Engineering, 2010, 199(37–40): 2437–2455 https://doi.org/10.1016/j.cma.2010.03.031
18	T Rabczuk, T Belytschko. A three-dimensional large deformation meshfree method for arbitrary evolving cracks. Computer Methods in Applied Mechanics and Engineering, 2007, 196(29–30): 2777–2799 https://doi.org/10.1016/j.cma.2006.06.020
19	T Rabczuk, T Belytschko. Application of particle methods to static fracture of reinforced concrete structures. International Journal of Fracture, 2006, 137(1–4): 19–49 https://doi.org/10.1007/s10704-005-3075-z
20	T Rabczuk, S Bordas, G Zi. On three-dimensional modelling of crack growth using partition of unity methods. Computers & Structures, 2010, 88(23–24): 1391–1411 https://doi.org/10.1016/j.compstruc.2008.08.010
21	T Rabczuk, T Belytschko. Cracking particles: A simplified meshfree method for arbitrary evolving cracks. International Journal for Numerical Methods in Engineering, 2004, 61(13): 2316–2343 https://doi.org/10.1002/nme.1151
22	A Yeginobal, K G Sobolev, S V Soboleva, B Kiyici. Thermal resistance of blast furnace slag high strength concrete cement. In: The First International symposium on mineral admixtures in cement. Istanbul: Turkish Cement Manufacturers Association, 1997, 106–117
23	J G Saemann, G W Washa. Variation of mortar and concrete properties with temperature. ACI Journal Proceedings, 1997, 54: 385–395
24	A H Gustaferro, S L Selvaggio. Fire endurance of simply supported prestressed concrete slabs. Journal-Prestressed Concrete Institute, 1967, 12(1): 37–52 https://doi.org/10.15554/pcij.02011967.37.52
25	U Schneider. Concrete at high temperatures—A general review. Fire Safety Journal, 1988, 13(1): 55–68 https://doi.org/10.1016/0379-7112(88)90033-1
26	C Castillo, A J Durrani. Effects of transient high temperature on high strength concrete. ACI Materials Journal, 1990, 87: 47–53
27	S P Shah, S H Ahmad. High Performance Concrete: Properties and Applications. New York: McGraw-Hill, 1994
28	C S Poon, S Azhar, M Anson, Y L Wong. Performance of metakolin concrete at elevated temperatures. Cement and Concrete Composites, 2003, 25(1): 83–89 https://doi.org/10.1016/S0958-9465(01)00061-0
29	L T Phan. Fire Performance of High Strength Concrete: A Report of the State-of the-Art. Maryland: Building and Fire Research Laboratory, National Institute of Standards and Technology, 1996
30	M S Abrams. Compressive Strength of Concrete at Temperatures to 1600F. Detroit: American Concrete Institute (ACI) SP 25, Temperature and Concrete, 1971
31	H L Malhotra. The effect of temperature on the compressive strength of concrete. Magazine of Concrete Research, 1956, 8(23): 85–94 https://doi.org/10.1680/macr.1956.8.23.85
32	T Morita, H Saito, H Kumagai. Residual mechanical properties of HSC members exposed to high temperature—Part 1: Test on mechanical properties, summaries of annual meeting. In: Summaries of Annual Meeting. London: Architectural Institute of Japan, 1992
33	Euro-International Committee for Concrete. Fire design of concrete structures-in accordance with CEB/FIB Model Code 90. London: Euro-International Committee for Concrete, 1991
34	European Committee for Standardisation. prENV1992-1-2: Eurocode 2: Design of Concrete Structures. Parts 1–2: Structural Fire Design, CEN/TC 250/SC 2. Brusseles: European Committee for Standardisation, 1993
35	European Committee for Standardisation. Eurocode 4: Design of Composite Steel and concrete Structures. Parts 1–2: General Rules-Structural Fire Design, CEN ENV. Brusseles: European Committee for Standardisation, 1994
36	Concrete Association of Finland.High Strength Concrete Supplementary Rules and Fire Design, RakMK B4. Finland: Concrete Association of Finland, 1991
37	ACI 216.1. Standard Method for Determining Fire Resistance of Concrete and Masonry Construction Assemblies (AC-216.1-07/TMS 0216.1-07). Farmington Hills, MI: American Concrete Institute, 2007
38	BS EN 1992-1-2:2004. Eurocode 2: Design of Concrete Structures, General Rules, Structural Fire Design. Brussels: European Committee for Standardization, 2005
39	S Tsivilis, G. A Parissakismathematical-model for the prediction of cement strength. Cement and Concrete Research, 1995, 25(1): 9–14 https://doi.org/10.1016/0008-8846(94)00106-9
40	C E de Siqueira Tango. An extrapolation method for compressive strength prediction of hydraulic cement products. Cement and Concrete Research, 1998, 28(7): 969–983 https://doi.org/10.1016/S0008-8846(98)00074-X
41	D A Anderson, R K Seals. Pulse velocity as a predictor of 28- and 90-day strength. ACI Journal Proceedings, 1981, 78: 116–122
42	L T Phan, T P McAllister, J L Gross, M J Hurley. Best Practice Guidelines for Structural Fire Resistance Design of Concrete and Steel Buildings NIST Technical Note 1681. Gaithersburg, MD: National Institute of Standards and Technology, 2010
43	Z H Duan, S C Kou, C S Poon. Using artificial neural networks for predicting the elastic modulus of recycled aggregate concrete. Construction & Building Materials, 2013, 44: 524–532 https://doi.org/10.1016/j.conbuildmat.2013.02.064
44	F Khademi, M Akbari, S M Jamal. Prediction of concrete compressive strength using ultrasonic pulse velocity test and artificial neural network modeling. Romanian Journal of Materials, 2016, 46: 343–350
45	A Sadrmomtazi, J Sobhani, M A Mirgozar. Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS. Construction & Building Materials, 2013, 42: 205–216 https://doi.org/10.1016/j.conbuildmat.2013.01.016
46	G Tayfur, T K Erdem, Ö Kırca. Strength prediction of high-strength concrete by fuzzy logic and artificial neural networks. Journal of Materials in Civil Engineering, 2014, 26(11): 04014079 https://doi.org/10.1061/(ASCE)MT.1943-5533.0000985
47	M Akbari, P J Overloop, A Afshar. Clustered k nearest neighbor algorithm for daily inflow forecasting. Journal Water Resources Management, 2011, 25(5): 1341–1357 https://doi.org/10.1007/s11269-010-9748-z
48	M Akbari, A Afshar, S J Mousavi. Multiobjective reservoir operation under emergency condition: Abbaspour reservoir case study with nonfunctional spillways. Journal of Flood Risk Management, 2014, 7(4): 374–384 https://doi.org/10.1111/jfr3.12061
49	F Khademi, M Akbari, M Nikoo. Displacement determination of concrete reinforcement building using data-driven models. International Journal of Sustainable Built Environment, 2017, 6(2): 400–411 https://doi.org/10.1016/j.ijsbe.2017.07.002
50	K M Hamdia, T Lahmer, T Nguyen-Thoi, T Rabczuk. Predicting the fracture toughness of PNCs: A stochastic approach based on ANN and ANFIS. Computational Materials Science, 2015, 102: 304–313 https://doi.org/10.1016/j.commatsci.2015.02.045
51	K Hornik, M Stinchcombe, H White. Multilayer feed-forward networks are universal approximators. Neural Networks, 1989, 2(5): 359–366 https://doi.org/10.1016/0893-6080(89)90020-8
52	M Akbari, A Afshar. Similarity-based error prediction approach for real-time inflow forecasting. Hydrology Research, 2014, 45(4–5): 589–602 https://doi.org/10.2166/nh.2013.098
53	E H Mamdani, S Assilian. An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 1975, 7(1): 1–13 https://doi.org/10.1016/S0020-7373(75)80002-2
54	T Takagi, M Sugeno. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 1985, 15(1): 116–132 https://doi.org/10.1109/TSMC.1985.6313399
55	M Akbari, A Afshar, M R Sadrabadi. Fuzzy rule based models modification by new data: Application to flood flow forecasting. Water Resources Management, 2009, 23(12): 2491–2504 https://doi.org/10.1007/s11269-008-9392-z
56	H Vernieuwe, O Georgieva, B De Baets, V Pauwels, N E C Verhoest, F P De Troch. Comparison of data-driven Takagi-Sugeno models of rainfall-discharge dynamics. Journal of Hydrology (Amsterdam), 2005, 302(1–4): 173–186 https://doi.org/10.1016/j.jhydrol.2004.07.001
57	P Kang, S Cho. Locally linear reconstruction for instance-based learning. Pattern Recognition, 2008, 41(11): 3507–3518 https://doi.org/10.1016/j.patcog.2008.04.009
58	A Behnood, H Ziari. Effects of silica fume addition and water to cement ratio on the properties of high-strength concrete after exposure to high temperatures. Cement and Concrete Composites, 2008, 30(2): 106–112 https://doi.org/10.1016/j.cemconcomp.2007.06.003
59	M Bastami, M Baghbadrani, F Aslani. Performance of nano-Silica modied high strength concrete at elevated temperatures. Construction & Building Materials, 2014, 68: 402–408 https://doi.org/10.1016/j.conbuildmat.2014.06.026
60	L Chen, Q Fang, X Jiang, Z Ruan, J Hong. Combined effects of high temperature and high strain rate on normal weight concrete. International Journal of Impact Engineering, 2015, 86: 40–56 https://doi.org/10.1016/j.ijimpeng.2015.07.002
61	Y Xiong, S Deng, D Wu. Experimental study on compressive strength recovery effect of fire-damaged high strength concrete after realkalisation treatment. Procedia Engineering, 2016, 135: 476–481 https://doi.org/10.1016/j.proeng.2016.01.158
62	I M Magda. Effect of elevated temperature on the properties of silica fume and recycled rubber-lled high strength concretes (RHSC). Housing and Building National Research Center, 2015, 13: 1–7 https://doi.org/10.1016/j.hbrcj.2015.03.002
63	Y F Fu, Y L Wong, C S Poon, C A Tang. Stress-strain behavior of high-strength concrete at elevated temperatures. Magazine of Concrete Research, 2005, 57(9): 535–544 https://doi.org/10.1680/macr.2005.57.9.535
64	M Husem. The effects of high temperature on compressive and exural strengths of ordinary and high-performance concrete. Fire Safety Journal, 2006, 41(2): 155–163 https://doi.org/10.1016/j.firesaf.2005.12.002

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