Novel hybrid models of ANFIS and metaheuristic optimizations (SCE and ABC) for prediction of compressive strength of concrete using rebound hammer field test

Abstract

In this study, we developed novel hybrid models namely Adaptive Neuro Fuzzy Inference System (ANFIS) optimized by Shuffled Complex Evolution (SCE) on the one hand and ANFIS with Artificial Bee Colony (ABC) on the other hand. These were used to predict compressive strength (Cs) of concrete relating to thirteen concrete-strength affecting parameters which are easy to determine in the laboratory. Field and laboratory tests data of 108 structural elements of 18 concrete bridges of the Ha Long-Van Don Expressway, Vietnam were considered. The dataset was randomly divided into a 70:30 ratio, for training (70%) and testing (30%) of the hybrid models. Performance of the developed fuzzy metaheuristic models was evaluated using standard statistical metrics: Correlation Coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results showed that both of the novel models depict close agreement between experimental and predicted results. However, the ANFIS-ABC model reflected better convergence of the results and better performance compared to that of ANFIS-SCE in the prediction of the concrete Cs. Thus, the ANFIS-ABC model can be used for the quick and accurate estimation of compressive strength of concrete based on easily determined parameters for the design of civil engineering structures including bridges.

Graphical abstract

Keywords

shuffled complex evolution / artificial bee colony / ANFIS / concrete / compressive strength / Vietnam

References

[1] Onyelowe K C, Iqbal M, Jalal F E, Onyia M E, Onuoha I C. Application of 3-algorithm ANN programming to predict the strength performance of hydrated-lime activated rice husk ash treated soil. Multiscale and Multidisciplinary Modeling, Experiments and Design, 2021, 4(4): 259–274

[2] Najimi M, Ghafoori N, Nikoo M. Modeling chloride penetration in self-consolidating concrete using artificial neural network combined with artificial bee colony algorithm. Journal of Building Engineering, 2019, 22: 216–226

[3] Ghasemi H, Kerfriden P, Bordas S P, Muthu J, Zi G, Rabczuk T J C S. Interfacial shear stress optimization in sandwich beams with polymeric core using non-uniform distribution of reinforcing ingredients. Composite Structures, 2015, 120: 221–230

[4] Ghasemi H, Brighenti R, Zhuang X, Muthu J, Rabczuk T J S, Optimization M. Optimal fiber content and distribution in fiber-reinforced solids using a reliability and NURBS based sequential optimization approach. Structural and Multidisciplinary Optimization, 2015, 51(1): 99–112

[5] Ghasemi H, Brighenti R, Zhuang X, Muthu J, Rabczuk T J C M S. Optimization of fiber distribution in fiber reinforced composite by using NURBS functions. Computational Materials Science, 2014, 83: 463–473

[6] Zhao Y, Moayedi H, Bahiraei M, Foong L K. Employing TLBO and SCE for optimal prediction of the compressive strength of concrete. Smart Structures and Systems, 2020, 26(6): 753–763

[7] Jalal F E, Xu Y, Iqbal M, Javed M F, Jamhiri B. Predictive modeling of swell-strength of expansive soils using artificial intelligence approaches: ANN, ANFIS and GEP. Journal of Environmental Management, 2021, 289: 112420

[8] Alavi A H, Gandomi A H, Nejad H C, Mollahasani A, Rashed A. Design equations for prediction of pressuremeter soil deformation moduli utilizing expression programming systems. Neural Computing & Applications, 2013, 23(6): 1771–1786

[9] Pham B T, Luu C, Van Phong T, Nguyen H D, Van Le H, Tran T Q, Ta H T, Prakash I. Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam. Journal of Hydrology (Amsterdam), 2021, 592: 125815

[10] Anitescu C, Atroshchenko E, Alajlan N, Rabczuk T. Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials & Continua, 2019, 59(1): 345–359

[11] GopalakrishnanKCeylanH. Application of Shuffled Complex Evolution Optimization Approach to Concrete Pavement Backanalysis. Iowa State University Digital Repository, 2010

[12] GuoHZhuangX RabczukT. A deep collocation method for the bending analysis of Kirchhoff plate. 2021, arXiv:2102.02617

[13] Samaniego E, Anitescu C, Goswami S, Nguyen-Thanh V M, Guo H, Hamdia K, Zhuang X, Rabczuk T. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications. Computer Methods in Applied Mechanics and Engineering, 2020, 362: 112790

[14] Zhuang X, Guo H, Alajlan N, Zhu H, Rabczuk T. Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning. European Journal of Mechanics. A, Solids, 2021, 87: 104225

[15] Tapao A, Cheerarot R. Optimal parameters and performance of artificial bee colony algorithm for minimum cost design of reinforced concrete frames. Engineering Structures, 2017, 151: 802–820

[16] Jahangir H, Rezazadeh Eidgahee D. A new and robust hybrid artificial bee colony algorithm—ANN model for FRP-concrete bond strength evaluation. Composite Structures, 2021, 257: 113160

[17] Iqbal M, Onyelowe K C, Jalal F E. Smart computing models of California bearing ratio, unconfined compressive strength, and resistance value of activated ash-modified soft clay soil with adaptive neuro-fuzzy inference system and ensemble random forest regression techniques. Multiscale and Multidisciplinary Modeling, Experiments and Design, 2021, 4(3): 207–225

[18] Naeini M R, Analui B, Gupta H V, Duan Q, Sorooshian S. Three decades of the Shuffled Complex Evolution (SCE-UA) optimization algorithm: Review and applications. Scientia Iranica, 2019, 26(4): 2015–2031

[19] Zheng S, Lyu Z, Foong L K. Early prediction of cooling load in energy-efficient buildings through novel optimizer of shuffled complex evolution. Engineering with Computers, 2020, 1–15

[20] Chen W, Panahi M, Pourghasemi H R. Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. Catena, 2017, 157: 310–324

[21] Termeh S V R, Kornejady A, Pourghasemi H R, Keesstra S. Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Science of the Total Environment, 2018, 615: 438–451

[22] Jaafari A, Zenner E K, Panahi M, Shahabi H. Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability. Agricultural and Forest Meteorology, 2019, 266: 198–207

[23] Hong H, Panahi M, Shirzadi A, Ma T, Liu J, Zhu A X, Chen W, Kougias I, Kazakis N. Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Science of the Total Environment, 2018, 621: 1124–1141

[24] Chen W, Panahi M, Tsangaratos P, Shahabi H, Ilia I, Panahi S, Li S, Jaafari A, Ahmad B B. Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility. Catena, 2019, 172: 212–231

[25] Zangeneh A Z, Mansouri M, Teshnehlab M, Sedigh A K. Training ANFIS system with DE algorithm. In: The Fourth International Workshop on Advanced Computational Intelligence. Wuhan: IEEE, 2011, 308–314

[26] Pourghasemi H R, Razavi-Termeh S V, Kariminejad N, Hong H, Chen W. An assessment of metaheuristic approaches for flood assessment. Journal of Hydrology (Amsterdam), 2020, 582: 124536

[27] Cus F, Balic J, Zuperl U. Hybrid ANFIS-ants system based optimisation of turning parameters. Journal of Achievements in Materials and Manufacturing Engineering, 2009, 36(1): 79–86

[28] Ly H B, Pham B T, Dao D V, Le V M, Le L M, Le T T. Improvement of ANFIS model for prediction of compressive strength of manufactured sand concrete. Applied Sciences (Basel, Switzerland), 2019, 9(18): 3841

[29] Nazari A, Sanjayan J G. Modelling of compressive strength of geopolymer paste, mortar and concrete by optimized support vector machine. Ceramics International, 2015, 41(9): 12164–12177

[30] Kibar H, Ozturk T. Determination of concrete quality with destructive and non-destructive methods. Computers and Concrete, 2015, 15(3): 473–484

[31] Shariati M, Mafipour M S, Haido J H, Yousif S T, Toghroli A, Trung N T, Shariati A. Identification of the most influencing parameters on the properties of corroded concrete beams using an Adaptive Neuro-Fuzzy Inference System (ANFIS). Steel and Composite Structures, 2020, 34(1): 155

[32] ACI318-11. Building Code Requirements for Structural Concrete and Commentary. Farmington Hills, MI: American Concrete Institute, 2012

[33] ShettyMJain A. Concrete Technology (Theory and Practice). S. Chand Publishing, 2019

[34] Abrams D. Design of Concrete Mixture. Structural Materials Research Laboratory, Lewis Institute, 1919,

[35] PunmiaBJain A KJainA K. Basic Civil Engineering. Firewall Media, 2003

[36] ScottJ S. Dictionary of Civil Engineering. Berlin: Springer Science & Business Media, 1993

[37] Haque M B, Tuhin I A, Farid M S S. Effect of aggregate size distribution on concrete compressive strength. SUST Journal of Science and Technology, 2012, 19(5): 35–39

[38] KozulRDarwin D. Effects of Aggregate Type, Size, and Content on Concrete Strength and Fracture Energy. SM Report No. 43. University of Kansas Center for Research, Inc., 1997

[39] Bloem D L, Gaynor R D. Effects of aggregate properties on strength of concrete. International Concrete Abstracts Portal, 1963, 60(10): 1429–1456

[40] SmailH. Evaluation of the seismic performance and dimensioning of the seismic joint between two reinforced concrete structures. Dissertation for the Doctoral Degree. Tizi Ouzou: Université Mouloud Mammeri Tizi-Ouzou, 2019

[41] Alexander M, Skalny J, Mindess S. Role of aggregates in hardened concrete. Material Science of Concrete III, 1989, 119–146

[42] Cetin A, Carrasquillo R L. High-performance concrete: influence of coarse aggregates on mechanical properties. Materials Journal, 1998, 95(3): 252–261

[43] Ezeldin A S, Aitcin P C. Effect of coarse aggregate on the behavior of normal and high-strength concretes. Cement, Concrete and Aggregates, 1991, 13(2): 121–124

[44] KaplanM. Flexural and compressive strength of concrete as affected by the properties of coarse aggregates. Journal Proceedings. 1959: 1193–1208

[45] Mehta P K, Monteiro P. Concrete: Structures, Properties and Materials. São Paulo: Pini, 1994, 572

[46] WashaG. Concrete Construction Handbook. New York: McGraw-Hill, 1998

[47] De LarrardF. Concrete Mixture Proportioning: A Scientific Approach. London: CRC Press, 1999

[48] SenftSGallegos SMansonD PGonzalesC. Chemical Admixtures for Concrete. London: CRC Press, 1999

[49] Sada S, Ikpeseni S. Evaluation of ANN and ANFIS modeling ability in the prediction of AISI 1050 steel machining performance. Heliyon, 2021, 7(2): e06136

[50] Akan R, Keskin S N. The effect of data size of ANFIS and MLR models on prediction of unconfined compression strength of clayey soils. SN Applied Sciences, 2019, 1(8): 1–11

[51] Saadat M, Bayat M. Prediction of the unconfined compressive strength of stabilised soil by Adaptive Neuro Fuzzy Inference System (ANFIS) and Non-Linear Regression (NLR). Geomechanics and Geoengineering, 2019, 1–12

[52] Islam M R, Jaafar W Z W, Hin L S, Osman N, Hossain A, Mohd N S. Development of an intelligent system based on ANFIS model for predicting soil erosion. Environmental Earth Sciences, 2018, 77(5): 1–15

[53] Luo Z, Luo Z, Qin Y, Wen L, Ma S, Dai Z. Developing new tree expression programing and artificial bee colony technique for prediction and optimization of landslide movement. Engineering with Computers, 2020, 36(3): 1117–1134

[54] KangFLi JXuQ. Structural inverse analysis by hybrid simplex artificial bee colony algorithms. Computers & Structures, 2009, 87(13−14): 861−870

[55] Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 2007, 39(3): 459–471

[56] Bai J, Liu H. Multi-objective artificial bee algorithm based on decomposition by PBI method. Applied Intelligence, 2016, 45(4): 976–991

[57] Duan Q, Gupta V K, Sorooshian S. Shuffled complex evolution approach for effective and efficient global minimization. Journal of Optimization Theory and Applications, 1993, 76(3): 501–521

[58] Muttil N, Liong S Y. Liong S-Y. Superior exploration–exploitation balance in shuffled complex evolution. Journal of Hydraulic Engineering (New York, N.Y.), 2004, 130(12): 1202–1205

[59] Thyer M, Kuczera G, Bates B C. Probabilistic optimization for conceptual rainfall-runoff models: A comparison of the shuffled complex evolution and simulated annealing algorithms. Water Resources Research, 1999, 35(3): 767–773

[60] Vrugt J A, Gupta H V, Bouten W, Sorooshian S. A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resources Research, 2003, 39(8): 1–52

[61] Nguyen Q H, Ly H-B, Ho L S, Al-Ansari N, Le H V, Tran V Q, Prakash I, Pham B T. Influence of data splitting on performance of machine learning models in prediction of shear strength of soil. Mathematical Problems in Engineering, 2021, 1–15

[62] Tao H, Al-Sulttani A O, Salih Ameen A M, Ali Z H, Al-Ansari N, Salih S Q, Mostafa R R. Training and testing data division influence on hybrid machine learning model process: Application of river flow forecasting. Complexity, 2020, 1–22

[63] Prashanth D S, Mehta R V K, Sharma N. Classification of handwritten Devanagari number—An analysis of pattern recognition tool using neural network and CNN. Procedia Computer Science, 2020, 167: 2445–2457

[64] Lalitha M P, Reddy N S, Reddy V V. Optimal DG placement for maximum loss reduction in radial distribution system using ABC algorithm. International Journal of Reviews in Computing, 2010, 3(1): 44–52

[65] Gao X, Cui Y, Hu J, Xu G, Wang Z, Qu J, Wang H. Parameter extraction of solar cell models using improved shuffled complex evolution algorithm. Energy Conversion and Management, 2018, 157: 460–479

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