Soft Set Parametric-based Data Clustering for Building Data Set

Iwan Tri Riyadi Yanto, Ahmad Azhari, Rohmat Saedudin, Sely Novita Sari, Mustafa Mat Deris, Norhalina Senan

Abstract


Identifying buildings for safety purposes is critical to anticipate unforeseen scenarios during a disaster. Rapid Visual Screening (RVS) is one of the procedures that can be used to determine a building's hazardous structure. The growing number of buildings necessitates grouping to provide recommendations for improving the analysis or conducting a more extensive review of the same building group. This article investigates the application of fuzzy clustering to the RVS dataset. Numerous strategies are compared, including fuzzy centroid clustering, fuzzy K-partition clustering, and multi-soft set clustering. The technique is applied to the RVS data set from Kulon Progo, Yogyakarta, which has 144 cases for grouping construction. Four clusters are formed from four distinct variables with fewer conditions: Plan Drawing, Floor Plan, Connection, and Stance. The experiment is based on the rank index, the Dunn index, and response time. The results indicate that multi-soft set-based clustering outperforms other baseline approaches. The investigator or government can utilize this information to suggest treating each cluster's "less" variable.

Keywords


Clustering; soft set; multinomial distribution; RVS.

Full Text:

PDF

References


A. Barbaresi, M. Bovo, and D. Torreggiani, "The dual influence of the envelope on the thermal performance of conditioned and unconditioned buildings," Sustain. Cities Soc., vol. 61, p. 102298, 2020.

E. Harirchian, K. Jadhav, K. Mohammad, S. E. A. Hosseini, and T. Lahmer, "A comparative study of MCDM methods integrated with rapid visual seismic vulnerability assessment of existing RC structures," Appl. Sci., vol. 10, no. 18, 2020.

M. M. Kassem, F. Mohamed Nazri, and E. Noroozinejad Farsangi, "The seismic vulnerability assessment methodologies: A state-of-the-art review," Ain Shams Eng. J., vol. 11, no. 4, pp. 849–864, 2020.

A. Darko, A. P. C. Chan, Y. Yang, and M. O. Tetteh, "Building information modeling (BIM)-based modular integrated construction risk management – Critical survey and future needs," Comput. Ind., vol. 123, p. 103327, 2020.

W. Smith, "The role of environment clubs in promoting ecocentrism in secondary schools: student identity and relationship to the earth," J. Environ. Educ., vol. 50, no. 1, pp. 52–71, Jan. 2019.

N. Khan, M. Ahmed, and N. Roy, "Temporal Clustering Based Thermal Condition Monitoring in Building," Sustain. Comput. Informatics Syst., p. 100441, Sep. 2020.

M. K. N. Huang, "A fuzzy k-modes algorithm for clustering categorical data - Fuzzy Systems, IEEE Transactions on," IEEE Trans. Fuzzy Syst., vol. 7, no. 4, pp. 446–452, 1999.

D.-W. Kim, K. H. Lee, and D. Lee, "Fuzzy clustering of categorical data using fuzzy centroids," Pattern Recognit. Lett., vol. 25, no. 11, pp. 1263–1271, Aug. 2004.

M. S. Yang, Y. H. Chiang, C. C. Chen, and C. Y. Lai, "A fuzzy k-partitions model for categorical data and its comparison to the GoM model," Fuzzy Sets Syst., vol. 159, no. 4, pp. 390–405, 2008.

T. Sarmah and S. Das, "Earthquake Vulnerability Assessment for RCC Buildings of Guwahati City using Rapid Visual Screening," Procedia Eng., vol. 212, pp. 214–221, 2018.

T. Sarmah and S. Das, "Earthquake Vulnerability Assessment for RCC Buildings of Guwahati City using Rapid Visual Screening," in Procedia Engineering, 2018, vol. 212, pp. 214–221.

M. M. Akhlaghi, S. Bose, M. E. Mohammadi, B. Moaveni, A. Stavridis, and R. L. Wood, "Post-earthquake damage identification of an RC school building in Nepal using ambient vibration and point cloud data," Eng. Struct., vol. 227, p. 111413, Jan. 2021.

D. Perrone, M. A. Aiello, M. Pecce, and F. Rossi, "Rapid visual screening for seismic evaluation of RC hospital buildings," Structures, vol. 3, pp. 57–70, Aug. 2015.

B. Lizundia et al., "Rapid visual screening of buildings for potential seismic hazards: FEMA 154 and FEMA 155 updates," NCEE 2014 - 10th U.S. Natl. Conf. Earthq. Eng. Front. Earthq. Eng., no. January, 2014.

A. K. Mallick and A. Mukhopadhyay, "Different Schemes for Improving Fuzzy Clustering Through Supervised Learning BT - Computational Intelligence, Communications, and Business Analytics," 2019, pp. 155–164.

J. Arora, K. Khatter, and M. Tushir, "Fuzzy c-Means Clustering Strategies: A Review of Distance Measures BT - Software Engineering," 2019, pp. 153–162.

T. Hadibarata and R. Rubiyatno, "Active learning strategies in environmental engineering course: A case study in curtin university Malaysia," Jurnal Pendidikan IPA Indonesia, vol. 8, no. 4. pp. 456–463, 2019.

A. Irsadi, S. Anggoro, T. R. Soeprobowati, M. Helmi, and A. S. E. Khair, “Shoreline and mangrove analysis along semarang-demak, Indonesia for sustainable environmental management,†Jurnal Pendidikan IPA Indonesia, vol. 8, no. 1. pp. 1–11, 2019.

S. Sari, S. N. Sari, R. Prastowo, R. Junaidi, and A. Machmud, “Rapid Visual Screening of Building for Potential Ground Movement in Kalirejo, Kulonprogo, Yogyakarta,†J. Ilm. Pendidik. Fis. Al-Biruni, vol. 9, no. 1, pp. 51–59, Apr. 2020.

T. Herawan, M. M. Deris, and J. H. Abawajy, "Matrices Representation of Multi Soft-Sets and Its Application," in Computational Science and Its Applications -- ICCSA 2010: International Conference, Fukuoka, Japan, March 23-26, 2010, Proceedings, Part III, D. Taniar, O. Gervasi, B. Murgante, E. Pardede, and B. O. Apduhan, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 201–214.

J. Nakajima and A. Hasegawa, "Subduction of the Philippine Sea plate beneath southwestern Japan: Slab geometry and its relationship to arc magmatism," J. Geophys. Res., vol. 112, no. B8, p. B08306, Aug. 2007.




DOI: http://dx.doi.org/10.18517/ijaseit.12.5.16010

Refbacks

  • There are currently no refbacks.


Published by INSIGHT - Indonesian Society for Knowledge and Human Development