Building Damage Detection Based on Earthquake Impact and Gradient Boosting Method

Elvan Felix Dwitama (1) , Genrawan Hoendarto (2) , Jimmy Tjen (3)
(1) Widya Dharma Pontianak University, Pontianak, West Kalimantan, Indonesia , Indonesia
(2) Widya Dharma Pontianak University, Pontianak, West Kalimantan, Indonesia , Indonesia
(3) Widya Dharma Pontianak University, Pontianak, West Kalimantan, Indonesia , Indonesia

DOI:

https://doi.org/10.18535/sshj.v9i01.1575

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Issue
Vol. 9 No. 01 (2025)
Published
2025-01-02
Keywords:
    Gradient Boosting, Earthquake Impact, Building Damage Detection.
PDF

Abstract

Damage to buildings often occurs, and one of the causes is natural disaster such as earthquakes. Earthquakes frequently result in significant damage to buildings, causing financial losses due to damage to building facilities and even loss of life. Therefore, it is crucial to assess the damage to buildings to determine the extent of the damage. This research proposed an algorithm for detecting building damage using gradient boosting method. This method is similar to decision tree approach, but the decisions tree re-evaluated, resulting in smaller and more accurate data. For this analysis, the dataset was divided into two parts: training set and testing set. 80% of the dataset was used as training data, while 20% was used as testing data. After thorough data preprocessing, the gradient boosting method achieved an accuracy of 60.86% from large number of datasets compared to other methods, such as decision trees and random forests, the decision tree tends to overfit or underfit, especially with complex data. Meanwhile, the random forest method is generally faster and less prone to overfitting on large datasets. However, Gradient Boosting (GB) can achieve better accuracy, particularly for complex datasets. This result is indicating the effectiveness of the gradient boosting method. Despite the large and complex dataset, where prediction results can sometimes vary, the outcomes demonstrate good performance. Future research should focus on refining datasets and optimizing the parameters used for predicting building damage.

References

M. Senthilkumar, D. Gnanasundar, B. Mohapatra, A. K. Jain, A. Nagar, and P. K. Parchure, “Earthquake prediction from high frequency groundwater level data: A case study from Gujarat, India,” HydroResearch, vol. 3, pp. 118–123, Jan. 2020, doi: 10.1016/j.hydres.2020.10.004.
[2] M. Motosaka and K. Mitsuji, “Building damage during the 2011 off the Pacific coast of Tohoku Earthquake,” Soils Found., vol. 52, no. 5, pp. 929–944, 2012, doi: 10.1016/j.sandf.2012.11.012.
[3] A. Rao, J. Jung, V. Silva, G. Molinario, and S. H. Yun, “Earthquake building damage detection based on synthetic-aperture-radar imagery and machine learning,” Nat. Hazards Earth Syst. Sci., vol. 23, no. 2, pp. 789–807, Feb. 2023, doi: 10.5194/nhess-23-789-2023.
[4] H. Zheng et al., “Assessment of Building Physical Vulnerability in Earthquake-Debris Flow Disaster Chain,” Int. J. Disaster Risk Sci., vol. 14, no. 4, pp. 666–679, Aug. 2023, doi: 10.1007/s13753-023-00509-7.
[5] P. K. E S, V. N. Thatha, G. Mamidisetti, S. V. Mantena, P. Chintamaneni, and R. Vatambeti, “Hybrid deep learning model with enhanced sunflower optimization for flood and earthquake detection,” Heliyon, vol. 9, no. 10, 2023, doi: 10.1016/j.heliyon.2023.e21172.
[6] Y. Qing et al., “Operational earthquake-induced building damage assessment using CNN-based direct remote sensing change detection on superpixel level,” Int. J. Appl. Earth Obs. Geoinf., vol. 112, 2022, doi: 10.1016/j.jag.2022.102899.
[7] P. Mangalraj, S. P. Duddela, P. Kirubanantham, and S. Iniyan, “Post-earthquake Building Damage Detection Using Deep Learning,” 2022, pp. 123–133. doi: 10.1007/978-981-16-1249-7_13.
[8] F. Smarra, J. Tjen, and A. D’Innocenzo, “Learning methods for structural damage detection via entropy‐based sensors selection,” Int. J. Robust Nonlinear Control, vol. 32, no. 10, pp. 6035–6067, Jul. 2022, doi: 10.1002/rnc.6124.
[9] J. Tjen, G. Hoendarto, and T. Darmanto, “Ensemble of the Distance Correlation-Based and Entropy-Based Sensor Selection for Damage Detection,” in 2022 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), Nov. 2022, pp. 183–189. doi: 10.1109/COMNETSAT56033.2022.9994387.
[10] Y. Nie, Q. Zeng, H. Zhang, and Q. Wang, “Building damage detection based on opce matching algorithm using a single post‐event polsar data,” Remote Sens., vol. 13, no. 6, Mar. 2021, doi: 10.3390/rs13061146.
[11] B. Kalantar, N. Ueda, H. A. H. Al-Najjar, and A. A. Halin, “Assessment of convolutional neural network architectures for earthquake-induced building damage detection based on pre-and post-event orthophoto images,” Remote Sens., vol. 12, no. 21, 2020, doi: 10.3390/rs12213529.
[12] S. Park, S. Jung, J. Lee, and J. Hur, “A Short-Term Forecasting of Wind Power Outputs Based on Gradient Boosting Regression Tree Algorithms,” Energies, vol. 16, no. 3, Feb. 2023, doi: 10.3390/en16031132.
[13] M. H. L. Louk and B. A. Tama, “Revisiting Gradient Boosting-Based Approaches for Learning Imbalanced Data: A Case of Anomaly Detection on Power Grids,” Big Data Cogn. Comput., vol. 6, no. 2, Jun. 2022, doi: 10.3390/bdcc6020041.
[14] S. Touzani, J. Granderson, and S. Fernandes, “Gradient boosting machine for modeling the energy consumption of commercial buildings,” Energy Build., vol. 158, pp. 1533–1543, Jan. 2018, doi: 10.1016/j.enbuild.2017.11.039.
[15] Y. He, H. Chen, D. Liu, and L. Zhang, “A framework of structural damage detection for civil structures using fast fourier transform and deep convolutional neural networks,” Appl. Sci., vol. 11, no. 19, Oct. 2021, doi: 10.3390/app11199345.
[16] G. Marasco et al., “Machine learning approach to the safety assessment of a prestressed concrete railway bridge,” Struct. Infrastruct. Eng., vol. 20, no. 4, pp. 566–580, 2024, doi: 10.1080/15732479.2022.2119581.
[17] G. Ke et al., “LightGBM: A highly efficient gradient boosting decision tree,” in Advances in Neural Information Processing Systems, 2017, vol. 2017-December, pp. 3147–3155.
[18] P. Nie, M. Roccotelli, M. P. Fanti, Z. Ming, and Z. Li, “Prediction of home energy consumption based on gradient boosting regression tree,” Energy Reports, vol. 7, pp. 1246–1255, Nov. 2021, doi: 10.1016/j.egyr.2021.02.006.
[19] Abdullah-All-Tanvir, I. Ali Khandokar, A. K. M. Muzahidul Islam, S. Islam, and S. Shatabda, “A gradient boosting classifier for purchase intention prediction of online shoppers,” Heliyon, vol. 9, no. 4, Apr. 2023, doi: 10.1016/j.heliyon.2023.e15163.
[20] J. Ge, H. Tang, N. Yang, and Y. Hu, “Rapid identification of damaged buildings using incremental learning with transferred data from historical natural disaster cases,” ISPRS J. Photogramm. Remote Sens., vol. 195, pp. 105–128, Jan. 2023, doi: 10.1016/j.isprsjprs.2022.11.010.
[21] S. Ghimire and P. Guéguen, “Host-to-target region testing of machine learning models for seismic damage prediction in buildings,” Nat. Hazards, vol. 120, no. 5, 2024, doi: 10.1007/s11069-023-06394-z.

Authors

Elvan Felix Dwitama
Widya Dharma Pontianak University, Pontianak, West Kalimantan, Indonesia
Genrawan Hoendarto
Widya Dharma Pontianak University, Pontianak, West Kalimantan, Indonesia
Jimmy Tjen
Widya Dharma Pontianak University, Pontianak, West Kalimantan, Indonesia
[1]
“Building Damage Detection Based on Earthquake Impact and Gradient Boosting Method”, Soc. sci. humanities j., vol. 9, no. 01, pp. 6305–6311, Jan. 2025, doi: 10.18535/sshj.v9i01.1575.