Optimasi Desain Penampang Struktur Rangka Batang Baja Berbasis Reliabilitas Menggunakan Symbiotic Organisms Search dan Artificial Neural Network


  • Willy Husada Petra Christian University
  • Doddy Prayogo Petra Christian University
  • Christoffel Felio Thamrin Petra Christian University
  • Ronald Herdjijono Petra Christian University




artificial neural network, metaheuristic, reliability-based design optimization, steel truss structure, symbiotic organisms search


Safety and economic factors are the two main consideration in designing a structure. The structural engineer always try to find the optimal structure design with minimum cost that satisfy the safety requirement. This safety requirement can be expressed as structural reliability that associated to a certain failure probability threshold. An integrated Reliability-based Design Optimization (RBDO) framework usually employed to minimize the cost objective function subjected to the failure probability limit. Failure probability mostly computed by using a time-consuming Monte Carlo Simulation (MCS) method. This study develops two hybrid RBDO framework, SOS-ANN and PSO-ANN, which combine the metaheuristic method, Symbiotic Organisms Search (SOS) and Particle Swarm Optimization (PSO) with a machine learning method, Artificial Neural Network (ANN). The SOS and PSO method are used to solve the discrete optimization problem. The ANN method is adopted to replace the MCS method in predicting the reliability of every solution using binary classification. A practical RBDO case of steel truss structure is used to demonstrate the performance of both SOS-ANN and PSO-ANN method in finding the optimal structural design. The results show that the SOS-ANN method outperforms the PSO-ANN method in terms of solution quality, computational efficiency and consistency.

Author Biographies

Willy Husada, Petra Christian University

Civil Engineering Department, Lecturer

Doddy Prayogo, Petra Christian University

Civil Engineering Department, Lecturer

Christoffel Felio Thamrin, Petra Christian University

Civil Engineering Department, Student

Ronald Herdjijono, Petra Christian University

Civil Engineering Department, Student


I.-T. Yang and W. Husada, “Improving Classification Accuracy for Single-loop Reliability-based Design Optimization,†Lect. Notes Eng. Comput. Sci., vol. 2228, pp. 1036–1040, 2017.

J. B. Cardoso, J. R. de Almeida, J. M. Dias, and P. G. Coelho, “Structural reliability analysis using Monte Carlo simulation and neural networks,†Adv. Eng. Softw., vol. 39, no. 6, pp. 505–513, 2008, doi: 10.1016/j.advengsoft.2007.03.015.

W. S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,†Bull. Math. Biophys., vol. 5, no. 4, pp. 115–133, 1943.

N. Zavrtanik, J. Prosen, M. Tušar, and G. Turk, “The use of artificial neural networks for modeling air void content in aggregate mixture,†Autom. Constr., vol. 63, pp. 155–161, 2016, doi: 10.1016/j.autcon.2015.12.009.

Y. Hong, A. W. A. Hammad, A. Akbarnezhad, and M. Arashpour, “A neural network approach to predicting the net costs associated with BIM adoption,†Autom. Constr., vol. 119, no. November 2019, p. 103306, 2020, doi: 10.1016/j.autcon.2020.103306.

D. Prayogo, W. F. Tjong, R. Gunawan, S. K. Ali, and S. Sugianto, “Optimasi Ukuran Penampang Rangka Batang Baja berdasarkan SNI 1729:2015 dengan Metode Metaheuristik Symbiotic Organisms Search,†J. Tek. Sipil, vol. 25, no. 1, p. 41, 2018, doi: 10.5614/jts.2018.25.1.6.

J. Kennedy and R. Eberhart, “Particle Swarm Optimization,†in Proceedings of IEEE International Conference on Neural Networks, 1995, pp. 1942–1948.

M. Marinaki, Y. Marinakis, and G. E. Stavroulakis, “Vibration control of beams with piezoelectric sensors and actuators using particle swarm optimization,†Expert Syst. Appl., vol. 38, no. 6, pp. 6872–6883, 2011, doi: 10.1016/j.eswa.2010.12.037.

I. Montalvo, J. Izquierdo, R. Pérez, and M. M. Tung, “Particle Swarm Optimization applied to the design of water supply systems,†Comput. Math. with Appl., vol. 56, no. 3, pp. 769–776, 2008, doi: 10.1016/j.camwa.2008.02.006.

M. Y. Cheng and D. Prayogo, “Symbiotic Organisms Search: A new metaheuristic optimization algorithm,†Comput. Struct., vol. 139, pp. 98–112, 2014, doi: 10.1016/j.compstruc.2014.03.007.

D. Prayogo, J. C. Sutanto, H. E. Suryo, and S. Eric, “A Comparative Study on Bio-Inspired Algorithms in Layout Optimization of Construction Site Facilities,†Civ. Eng. Dimens., vol. 20, no. 2, p. 102, 2018, doi: 10.9744/ced.20.2.102-110.

D. Prayogo and C. T. Kusuma, “Optimization of resource leveling problem under multiple objective criteria using a symbiotic organisms search,†Civ. Eng. Dimens., vol. 21, no. 1, pp. 43–49, 2019, doi: 10.9744/ced.21.1.43-49.

G. G. Tejani, N. Pholdee, S. Bureerat, and D. Prayogo, “Multiobjective adaptive symbiotic organisms search for truss optimization problems,†Knowledge-Based Syst., vol. 161, pp. 398–414, 2018, doi: 10.1016/j.knosys.2018.08.005.

Badan Standarisasi Nasional, Spesifikasi untuk bangunan gedung baja struktural (SNI 1729:2015). 2015.

R. Gunawan, “Tabel Profil Konstruksi Baja.†Kanisius, 1988.

H. Adeli and S.-L. Hung, Machine Learning: Neural Networks, Genetic Algorithms, and Fuzzy Systems. John Wiley & Sons, Inc., 1995.

I. T. Yang and Y. H. Hsieh, “Reliability-based design optimization with discrete design variables and non-smooth performance functions: AB-PSO algorithm,†Autom. Constr., vol. 20, no. 5, pp. 610–619, 2011, doi: 10.1016/j.autcon.2010.12.003.




How to Cite

Husada, W., Prayogo, D., Thamrin, C. F., & Herdjijono, R. (2021). Optimasi Desain Penampang Struktur Rangka Batang Baja Berbasis Reliabilitas Menggunakan Symbiotic Organisms Search dan Artificial Neural Network. Rekayasa Sipil, 15(3), pp.214–221. https://doi.org/10.21776/ub.rekayasasipil.2021.015.03.8