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

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

References

[1] Düğenci O, Haktanir T, Altun F. Experimental research for the effect of high temperature on the mechanical properties of steel fiber-reinforced concrete. Construction & Building Materials, 2015, 75: 82–88

[2] Choe G, Kim G, Gucunski N, Lee S. 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

[3] Ergün A, Kürklü G M, Serhat B, Mansour M Y. The effect of cement dosage on mechanical properties of concrete exposed to high temperatures. Fire Safety Journal, 2013, 55: 160–167

[4] Handoo S K, Agarwal S, Agarwal S K. Physicochemical, mineralogical, and morphological characteristics of concrete exposed to elevated temperatures. Cement and Concrete Research, 2002, 32(7): 1009–1018

[5] Li M, Qian C, Sun W. Mechanical properties of high-strength concrete after fire. Cement and Concrete Research, 2004, 34(6): 1001–1005

[6] Balázs G L, Lubloy E. Post-heating strength of fiber-reinforced concretes. Fire Safety Journal, 2012, 49: 100–106

[7] Tanyildizi H. Variance analysis of crack characteristics of structural lightweight concrete containing silica fume exposed to high temperature. Construction & Building Materials, 2013, 47: 1154–1159

[8] Hamdia K M, Arafa M, Alqedra M. Structural damage assessment criteria for reinforced concrete buildings by using a Fuzzy Analytic Hierarchy Process. Underground Space, 2018, 3(3): 243–249

[9] Cülfik M S, Özturan T. 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

[10] Venecanin S D. Thermal incompatibility of concrete components and thermal properties of carbonate rocks. ACI Materials Journal, 1990, 87: 602–607

[11] Yüzer N, Aköz F, Öztürk L D. Compressive strength-color change relation in mortars at high temperature. Cement and Concrete Research, 2004, 34(10): 1803–1807

[12] Crook D N, Murray M J. Regain of strength after firing of concrete. Magazine of Concrete Research, 1970, 22(72): 149–154

[13] Petzold A, Rohrs M. Concrete for High Temperatures. Lincoln: Maclaren and Sons Ltd., 1970

[14] Ožbolt J, Bošnjak J, Periškić G, Sharma A. 3D numerical analysis of reinforced concrete beams exposed to elevated temperature. Engineering Structures, 2014, 58: 166–174

[15] Caggiano A, Etse G. Coupled thermo-mechanical interface model for concrete failure analysis under high temperature. Computer Methods in Applied Mechanics and Engineering, 2015, 289: 498–516

[16] Rabczuk T, Zi G, Bordas S, Nguyen-Xuan H. A geometrically non-linear three-dimensional cohesive crack method for reinforced concrete structures. Engineering Fracture Mechanics, 2008, 75(16): 4740–4758

[17] Rabczuk T, Zi G, Bordas S, Nguyen-Xuan H. A simple and robust three-dimensional cracking-particle method without enrichment. Computer Methods in Applied Mechanics and Engineering, 2010, 199(37–40): 2437–2455

[18] Rabczuk T, Belytschko T. A three-dimensional large deformation meshfree method for arbitrary evolving cracks. Computer Methods in Applied Mechanics and Engineering, 2007, 196(29–30): 2777–2799

[19] Rabczuk T, Belytschko T. Application of particle methods to static fracture of reinforced concrete structures. International Journal of Fracture, 2006, 137(1–4): 19–49

[20] Rabczuk T, Bordas S, Zi G. On three-dimensional modelling of crack growth using partition of unity methods. Computers & Structures, 2010, 88(23–24): 1391–1411

[21] Rabczuk T, Belytschko T. Cracking particles: A simplified meshfree method for arbitrary evolving cracks. International Journal for Numerical Methods in Engineering, 2004, 61(13): 2316–2343

[22] Yeginobal A, Sobolev K G, Soboleva S V, Kiyici B. 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] Saemann J G, Washa G W. Variation of mortar and concrete properties with temperature. ACI Journal Proceedings, 1997, 54: 385–395

[24] Gustaferro A H, Selvaggio S L. Fire endurance of simply supported prestressed concrete slabs. Journal-Prestressed Concrete Institute, 1967, 12(1): 37–52

[25] Schneider U. Concrete at high temperatures—A general review. Fire Safety Journal, 1988, 13(1): 55–68

[26] Castillo C, Durrani A J. Effects of transient high temperature on high strength concrete. ACI Materials Journal, 1990, 87: 47–53

[27] Shah S P, Ahmad S H. High Performance Concrete: Properties and Applications. New York: McGraw-Hill, 1994

[28] Poon C S, Azhar S, Anson M, Wong Y L. Performance of metakolin concrete at elevated temperatures. Cement and Concrete Composites, 2003, 25(1): 83–89

[29] Phan L T. 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] Abrams M S. Compressive Strength of Concrete at Temperatures to 1600F. Detroit: American Concrete Institute (ACI) SP 25, Temperature and Concrete, 1971

[31] Malhotra H L. The effect of temperature on the compressive strength of concrete. Magazine of Concrete Research, 1956, 8(23): 85–94

[32] Morita T, Saito H, Kumagai H. 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] Tsivilis S, Parissakis G. Amathematical-model for the prediction of cement strength. Cement and Concrete Research, 1995, 25(1): 9–14

[40] de Siqueira Tango C E. An extrapolation method for compressive strength prediction of hydraulic cement products. Cement and Concrete Research, 1998, 28(7): 969–983

[41] Anderson D A, Seals R K. Pulse velocity as a predictor of 28- and 90-day strength. ACI Journal Proceedings, 1981, 78: 116–122

[42] Phan L T, McAllister T P, Gross J L, Hurley M J. 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] Duan Z H, Kou S C, Poon C S. Using artificial neural networks for predicting the elastic modulus of recycled aggregate concrete. Construction & Building Materials, 2013, 44: 524–532

[44] Khademi F, Akbari M, Jamal S M. Prediction of concrete compressive strength using ultrasonic pulse velocity test and artificial neural network modeling. Romanian Journal of Materials, 2016, 46: 343–350

[45] Sadrmomtazi A, Sobhani J, Mirgozar M A. Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS. Construction & Building Materials, 2013, 42: 205–216

[46] Tayfur G, Erdem T K, Kırca Ö. Strength prediction of high-strength concrete by fuzzy logic and artificial neural networks. Journal of Materials in Civil Engineering, 2014, 26(11): 04014079

[47] Akbari M, Overloop P J, Afshar A. Clustered k nearest neighbor algorithm for daily inflow forecasting. Journal Water Resources Management, 2011, 25(5): 1341–1357

[48] Akbari M, Afshar A, Mousavi S J. Multiobjective reservoir operation under emergency condition: Abbaspour reservoir case study with nonfunctional spillways. Journal of Flood Risk Management, 2014, 7(4): 374–384

[49] Khademi F, Akbari M, Nikoo M. Displacement determination of concrete reinforcement building using data-driven models. International Journal of Sustainable Built Environment, 2017, 6(2): 400–411

[50] Hamdia K M, Lahmer T, Nguyen-Thoi T, Rabczuk T. Predicting the fracture toughness of PNCs: A stochastic approach based on ANN and ANFIS. Computational Materials Science, 2015, 102: 304–313

[51] Hornik K, Stinchcombe M, White H. Multilayer feed-forward networks are universal approximators. Neural Networks, 1989, 2(5): 359–366

[52] Akbari M, Afshar A. Similarity-based error prediction approach for real-time inflow forecasting. Hydrology Research, 2014, 45(4–5): 589–602

[53] Mamdani E H, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 1975, 7(1): 1–13

[54] Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 1985, 15(1): 116–132

[55] Akbari M, Afshar A, Sadrabadi M R. Fuzzy rule based models modification by new data: Application to flood flow forecasting. Water Resources Management, 2009, 23(12): 2491–2504

[56] Vernieuwe H, Georgieva O, De Baets B, Pauwels V, Verhoest N E C, De Troch F P. Comparison of data-driven Takagi-Sugeno models of rainfall-discharge dynamics. Journal of Hydrology (Amsterdam), 2005, 302(1–4): 173–186

[57] Kang P, Cho S. Locally linear reconstruction for instance-based learning. Pattern Recognition, 2008, 41(11): 3507–3518

[58] Behnood A, Ziari H. 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

[59] Bastami M, Baghbadrani M, Aslani F. Performance of nano-Silica modied high strength concrete at elevated temperatures. Construction & Building Materials, 2014, 68: 402–408

[60] Chen L, Fang Q, Jiang X, Ruan Z, Hong J. Combined effects of high temperature and high strain rate on normal weight concrete. International Journal of Impact Engineering, 2015, 86: 40–56

[61] Xiong Y, Deng S, Wu D. Experimental study on compressive strength recovery effect of fire-damaged high strength concrete after realkalisation treatment. Procedia Engineering, 2016, 135: 476–481

[62] Magda I M. 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

[63] Fu Y F, Wong Y L, Poon C S, Tang C A. Stress-strain behavior of high-strength concrete at elevated temperatures. Magazine of Concrete Research, 2005, 57(9): 535–544

[64] Husem M. The effects of high temperature on compressive and exural strengths of ordinary and high-performance concrete. Fire Safety Journal, 2006, 41(2): 155–163

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature