Estimation of optimum design of structural systems via machine learning

Abstract

Three different structural engineering designs were investigated to determine optimum design variables, and then to estimate design parameters and the main objective function of designs directly, speedily, and effectively. Two different optimization operations were carried out: One used the harmony search (HS) algorithm, combining different ranges of both HS parameters and iteration with population numbers. The other used an estimation application that was done via artificial neural networks (ANN) to find out the estimated values of parameters. To explore the estimation success of ANN models, different test cases were proposed for the three structural designs. Outcomes of the study suggest that ANN estimation for structures is an effective, successful, and speedy tool to forecast and determine the real optimum results for any design model.

Graphical abstract

Keywords

optimization / metaheuristic algorithms / harmony search / structural designs / machine learning / artificial neural networks

References

[1] Holland J H. Adaptation in Natural and Artificial Systems. Michigan: University of Michigan Press, 1975

[2] Storn R, Price K. Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 1997, 11( 4): 341– 359

[3] Erol O K, Eksin I. A new optimization method: Big bang–big crunch. Advances in Engineering Software, 2006, 37( 2): 106– 111

[4] Simon D. Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 2008, 12( 6): 702– 713

[5] Kennedy J, Eberhart R C. Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks No. IV. Perth: IEEE Conference Publication, 1995, 1942– 1948

[6] Li X. A new intelligent optimization method-artificial fish school algorithm. Dissertation for the Doctoral Degree. Hangzhou: Zhejiang University, 2003

[7] Karaboga D. An Idea Based on Honeybee Swarm for Numerical Optimization, vol. 200. Technical Report TR06. 2005

[8] Yang X S, Deb S. Cuckoo search via Lévy flights. In: 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). Coimbatore: IEEE, 2009, 210– 214

[9] Yang X S. Flower pollination algorithm for global optimization. In: International Conference on Unconventional Computing and Natural Computation. Berlin: Springer, 2012, 240– 249

[10] Mirjalili S, Mirjalili S M, Lewis A. Grey wolf optimizer. Advances in Engineering Software, 2014, 69 : 46– 61

[11] Mirjalili S, Lewis A. The whale optimization algorithm. Advances in Engineering Software, 2016, 95 : 51– 67

[12] Glover F. Future paths for integer programming and links to artificial intelligence. Computers & Operations Research, 1986, 13( 5): 533– 549

[13] Geem Z W, Kim J H, Loganathan G V. A new heuristic optimization algorithm: Harmony search. Simulation, 2001, 76( 2): 60– 68

[14] Kirkpatrick S, Gelatt C D, Vecchi M P. Optimization by simulated annealing. Science, 1983, 220( 4598): 671– 680

[15] Formato R A. Central force optimization: A new nature inspired computational framework for multidimensional search and optimization. Nature Inspired Cooperative Strategies for Optimization (NICSO 2007). Berlin: Springer, 2007, 221– 238

[16] Rashedi E, Nezamabadi-Pour H, Saryazdi S. GSA: A gravitational search algorithm. Information Sciences, 2009, 179( 13): 2232– 2248

[17] Kaveh A, Bakhshpoori T. Water evaporation optimization: A novel physically inspired optimization algorithm. Computers & Structures, 2016, 167 : 69– 85

[18] Quaranta G, Fiore A, Marano G C. Optimum design of prestressed concrete beams using constrained differential evolution algorithm. Structural and Multidisciplinary Optimization, 2014, 49( 3): 441– 453

[19] Ozbasaran H, Yilmaz T. Shape optimization of tapered I-beams with lateral-torsional buckling, deflection and stress constraints. Journal of Constructional Steel Research, 2018, 143 : 119– 130

[20] Yücel M, Bekdaş G, Ni̇gdeli̇ S M. Minimizing the weight of cantilever beam via metaheuristic methods by using different population-iteration combinations. WSEAS Transactions on Computers, 2020, 19 : 69– 77

[21] Rabi’ M N, Yousif S T. Optimum cost design of reinforced concrete columns using genetic algorithms. Al Rafdain Engineering Journal (New York), 2014, 22( 1): 112– 141

[22] de Medeiros G F, Kripka M. Optimization of reinforced concrete columns according to different environmental impact assessment parameters. Engineering Structures, 2014, 59 : 185– 194

[23] Aydogdu I, Akin A. Biogeography based CO₂ and cost optimization of RC cantilever retaining walls. In: 17th International Conference on Structural Engineering. Paris: World Academy of Science, Engineering and Technology, 2015, 1480– 1485

[24] Jasim N A, Al-Yaqoobi A M. Optimum design of tied back retaining wall. Open Journal of Civil Engineering, 2016, 6( 2): 139– 155

[25] Mohammad F A, Ahmed H G. Optimum design of reinforced concrete cantilever retaining walls according to Eurocode 2 (EC2). Athens Journal of Technology & Engineering, 2018, 5( 3): 277– 296

[26] Kaveh A, Hamedani K B, Zaerreza A. A set theoretical shuffled shepherd optimization algorithm for optimal design of cantilever retaining wall structures. Engineering with Computers, 2021, 37( 4): 3265– 3282

[27] Kayabekir A E, Bekdaş G, Nigdeli S M. Metaheuristic Approaches for Optimum Design of Reinforced Concrete Structures: Emerging Research and Opportunities. Hershey, PA: IGI Global, 2020, 161– 182

[28] Chen X, Liu S, He S. The optimization design of truss based on ant colony optimal algorithm. In: Sixth International Conference on Natural Computation, vol. 2. Yantai: IEEE, 720– 723

[29] Degertekin S O, Hayalioglu M S. Sizing truss structures using teaching-learning-based optimization. Computers & Structures, 2013, 119 : 177– 188

[30] Bekdaş G, Nigdeli S M, Yang X S. Sizing optimization of truss structures using flower pollination algorithm. Applied Soft Computing, 2015, 37 : 322– 331

[31] Mortazavi A, Toğan V, Nuhoğlu A. Weight minimization of truss structures with sizing and layout variables using integrated particle swarm optimizer. Journal of Civil Engineering and Management, 2017, 23( 8): 985– 1001

[32] Salar M, Dizangian B. Sizing optimization of truss structures using ant lion optimizer. In: 2nd International Conference on Civil Engineering, Architecture and Urban Management in Iran. Tehran: Tehran University, 2019

[33] Yücel M, Bekdaş G, Nigdeli S M. Prediction of optimum 3-bar truss model parameters with an ANN model. In: Proceedings of 6th International Conference on Harmony Search, Soft Computing and Applications (ICHSA 2020). Singapore: Springer, 2021, 317–324

[34] Prayogo D, Gaby G, Wijaya B H, Wong F T. Reliability-based design with size and shape optimization of truss structure using symbiotic organisms search. IOP Conference Series: Earth and Environmental Science, 2020, 506 : 012047–

[35] Mortazavi A. Large-scale structural optimization using a fuzzy reinforced swarm intelligence algorithm. Advances in Engineering Software, 2020, 142 : 102790–

[36] Kaveh A, Mohammadi S, Hosseini O K, Keyhani A, Kalatjari V R. Optimum parameters of tuned mass dampers for seismic applications using charged system search. Civil Engineering (Shiraz), 2015, 39( C1): 21– 40

[37] Shi W, Wang L, Lu Z, Zhang Q. Application of an artificial fish swarm algorithm in an optimum tuned mass damper design for a pedestrian bridge. Applied Sciences (Basel, Switzerland), 2018, 8( 2): 175–

[38] Bekdaş G, Nigdeli S M, Yang X S. A novel bat algorithm based optimum tuning of mass dampers for improving the seismic safety of structures. Engineering Structures, 2018, 159 : 89– 98

[39] Yucel M, Bekdaş G, Nigdeli S M, Sevgen S. Estimation of optimum tuned mass damper parameters via machine learning. Journal of Building Engineering, 2019, 26 : 100847–

[40] Soheili S, Zoka H, Abachizadeh M. Tuned mass dampers for the drift reduction of structures with soil effects using ant colony optimization. Advances in Structural Engineering, 2021, 24( 4): 771– 783

[41] Yucel M, Öncü-Davas S, Nigdeli S M, Bekdaş G, Sevgen S. Estimating of analysis results for structures with linear base isolation systems using artificial neural network model. International Journal of Control Systems and Robotics, 2018, 3

[42] Nguyen-Thanh V M, Zhuang X, Rabczuk T. A deep energy method for finite deformation hyperelasticity. European Journal of Mechanics. A, Solids, 2020, 80 : 103874–

[43] 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–

[44] Abueidda D W, Koric S, Sobh N A. Topology optimization of 2D structures with nonlinearities using deep learning. Computers & Structures, 2020, 237 : 106283–

[45] Minh Nguyen-Thanh V, Trong Khiem Nguyen L, Rabczuk T, Zhuang X. A surrogate model for computational homogenization of elastostatics at finite strain using high-dimensional model representation-based neural network. International Journal for Numerical Methods in Engineering, 2020, 121( 21): 4811– 4842

[46] Kaveh A, Eslamlou A D, Javadi S M, Malek N G. Machine learning regression approaches for predicting the ultimate buckling load of variable-stiffness composite cylinders. Acta Mechanica, 2021, 232( 3): 921– 931

[47] Guo H, Zhuang X, Rabczuk T. A deep collocation method for the bending analysis of Kirchhoff plate. Computers, Materials & Continua, 2019, 59( 2): 433– 456

[48] 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–

[49] Koziel S, Yang X S. Computational Optimization, Methods and Algorithms. Berlin: Springer-Verlag, 2011

[50] Sarle W S. Neural networks and statistical models. In: Proceedings of the Nineteenth Annual SAS Users Group International Conference. Cary: SAS Institute, 1994, 1538– 1550

[51] Mathworks MATLAB. Matlab 2018a, Neural Net Fitting, 2018

[52] Fujita Y, Lind K, Williams T J. Computer Applications in the Automation of Shipyard Operation and Ship Design, vol. 2. New York: Elsevier, 1974, 327– 338

[53] Schmit L A Jr, Farshi B. Some approximation concepts for structural synthesis. AIAA Journal, 1974, 12( 5): 692– 699

[54] Amir H M, Hasegawa T. Nonlinear mixed-discrete structural optimization. Journal of Structural Engineering, 1989, 115( 3): 626– 646

[55] ACI 318–14. Building Code Requirements for Reinforced Concrete. Detroit, MI: American Concrete Institute, 1977

RIGHTS & PERMISSIONS

Higher Education Press 2021.