PENGAWASAN KESEHATAN STRUKTUR & JARINGAN SYARAF TIRUAN: SISTEM FIXED-ROLLED BEAM

Masykur Kimsan, Ica Capriyati Husen, Wa Ode Siti Nur Alam

Abstract


Kerusakan, bermula dari yang kecil, pada struktur bangunan sipil seringkali menyebabkan kegagalan. Oleh karena itu, sangat penting untuk melakukan pengawasan terhadap munculnya kerusakan pada struktur. Kerusakan yang terjadi pada struktur ditunjukkan dengan pengurangan pada kekakuan struktur. Strategi dasar yang diterapkan pada penelitian ini adalah melatih jaringan syaraf untuk mengklasifikasi kondisi struktur yang ditentukan, apakah struktur tersebut mengalami pengurangan kekakuan 30% atau 90%. Gagasan ini diterapkan pada balok jepit-roll. Perpindahan dan tegangan pada struktur digunakan sebagai parameter input untuk mengidentifikasi kerusakan dengan menggunakan jaringan syaraf tiruan propagasi balik. Perpindahan dan tegangan dihitung dengan menggunakan metode elemen hingga. Hasilnya menunjukkan bahwa sistem mampu mengklasifikasi kondisi kesehatan struktur secara tepat dengan akurasi sistem sebesar 92,69%, walaupun sistem diuji dengan data baru.

Kata kunci; Balok Jepit-Roll, Jaringan Syaraf Tiruan, Kondisi Struktur, Akurasi Sistem, Propagasi Balik

Full Text:

PDF

References


J. T. Andersen, “Studies in damage detection using flexibility method,” University of Stavanger, Norway, 2012.

C. Rainieri, G. Fabbrocino, and E. Cosenza, “Structural health monitoring systems as a tool for seismic protection,” in Proceedings of the 14th world conference on earthquake engineering, Beijing, China, 2008, pp. 12–17.

M. Mishra, P. B. Lourenço, and G. V. Ramana, “Structural health monitoring of civil engineering structures by using the internet of things: A review,” J. Build. Eng., vol. 48, p. 103954, 2022.

M. A. Hannan, K. Hassan, and K. P. Jern, “A review on sensors and systems in structural health monitoring: Current issues and challenges,” Smart Struct. Syst. An Int. J., vol. 22, no. 5, pp. 509–525, 2018.

Z. Ma, J. Choi, and H. Sohn, “Structural displacement sensing techniques for civil infrastructure: A review,” J. Infrastruct. Intell. Resil., vol. 2, no. 3, p. 100041, 2023.

S. J. S. Hakim and H. A. Razak, “Structural damage detection of steel bridge girder using artificial neural networks and finite element models,” Steel Compos. Struct, vol. 14, no. 4, pp. 367–377, 2013.

D. Maity and A. Saha, “Damage assessment in structure from changes in static parameter using neural networks,” Sadhana, vol. 29, pp. 315–327, 2004.

S.-Y. Lee, “Displacement Detection of Structures using a Micro-genetic Algorithm,” Int. J. Adv. Sci. Technol., vol. 56, pp. 111–118, 2013.

A. M. Ajofoyinbo and D. O. Olowokere, “Fuzzy control model for structural health monitoring of civil infrastructure systems,” J. Control Sci. Eng., vol. 1, pp. 9–20, 2015.

P. R. Baviskar and V. B. Tungikar, “Multiple cracks assessment using natural frequency measurement and prediction of crack properties by artificial neural network,” Int. J. Adv. Sci. Technol., vol. 54, pp. 23–38, 2013.

S. Chakraverty, T. Marwala, P. Gupta, and T. Tettey, “Response prediction of structural system subject to earthquake motions using artificial neural network,” arXiv Prepr. arXiv0705.2235, 2007.

H. K. Vinayak, A. Kumar, P. Agarwal, and S. K. Thakkar, “NN based damage detection from modal parameter changes,” 2008.

S. J. S. Hakim and H. A. Razak, “Frequency response function-based structural damage identification using artificial neural networks-a review,” Res. J. Appl. Sci. Eng. Technol., vol. 7, no. 9, pp. 1750–1764, 2014.

V. V Nguyen et al., “Damage identification of a concrete arch beam based on frequency response functions and artificial neural networks,” Electron. J. Struct. Eng., vol. 14, no. 1, pp. 75–84, 2015.

C. Y. Kao and S.-L. Hung, “Detection of structural damage via free vibration responses generated by approximating artificial neural networks,” Comput. & Struct., vol. 81, no. 28–29, pp. 2631–2644, 2003.

C. Zang and M. Imregun, “Structural damage detection using artificial neural networks and measured FRF data reduced via principal component projection,” J. Sound Vib., vol. 242, no. 5, pp. 813–827, 2001.

N. Bakhary, “Vibration–Based Damage Detection Of Slab Structure Using Artificial Neural Network,” J. Teknol., pp. 17â--30, 2006.

M.-C. Popescu, V. E. Balas, L. Perescu-Popescu, and N. Mastorakis, “Multilayer perceptron and neural networks,” WSEAS Trans. Circuits Syst., vol. 8, no. 7, pp. 579–588, 2009.

A. Khatir, R. Capozucca, S. Khatir, and E. Magagnini, “Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial neural network,” Front. Struct. Civ. Eng., vol. 16, no. 8, pp. 976–989, 2022, doi: 10.1007/s11709-022-0840-2.

J. O. Gidiagba, L. Tartibu, and M. O. Okwu, “Crack Detection on a Structural Beam: A Simplified Analytical Method Based on Artificial Neural Network Model,” in 2022 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), 2022, pp. 1–7, doi: 10.1109/icABCD54961.2022.9856177.

K.-V. Yuen and H.-F. Lam, “On the complexity of artificial neural networks for smart structures monitoring,” Eng. Struct., vol. 28, no. 7, pp. 977–984, 2006, doi: https://doi.org/10.1016/j.engstruct.2005.11.002.

M. Stoffel, R. Gulakala, F. Bamer, and B. Markert, “Artificial neural networks in structural dynamics: A new modular radial basis function approach vs. convolutional and feedforward topologies,” Comput. Methods Appl. Mech. Eng., vol. 364, p. 112989, 2020, doi: https://doi.org/10.1016/j.cma.2020.112989.

D. Wu and G. G. Wang, “Causal artificial neural network and its applications in engineering design,” Eng. Appl. Artif. Intell., vol. 97, p. 104089, 2021, doi: https://doi.org/10.1016/j.engappai.2020.104089.

L. Zhang and L. Caracoglia, “Structural Fragility Analysis of Tall Buildings and Towers via Artificial Neural Network Surrogate Modeling,” pp. 40–42, 2021.

S. Kusumadewi, “Membangun Jaringan Syaraf Tiruan Menggunakan MATLAB & EXCEL LINK,” Yogyakarta Graha Ilmu, 2004.

C. Gonzalez-Perez and J. Valdes-Gonzalez, “Identification of structural damage in a vehicular bridge using artificial neural networks,” Struct. Heal. Monit., vol. 10, no. 1, pp. 33–48, 2011


Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 semanTIK

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


Editor's Address :

Informatics Engineering Department of Halu Oleo University, Engineering Faculty Building 3rd Floor
H.E.A. Mokodompit Street, Bumi Tridharma Green Campus, Halu Oleo University

Telp. (0401) 3196237
Fax. (0401) 3195287
Website:http://ojs.uho.ac.id/index.php/semantik/index
E-mail: semantik.informatika@uho.ac.id