Proceedings of the Third International Conference on Sustainable Innovation 2019 – Technology and Engineering (IcoSITE 2019)

Selection Model of Building Demolition Method Based on Expert System

Authors
Oryza Lhara Sari, Tri Joko Wahyu Adi, Abdul Munif
Corresponding Author
Oryza Lhara Sari
Available Online October 2019.
DOI
https://doi.org/10.2991/icosite-19.2019.12How to use a DOI?
Keywords
demolition, building, impact demolition, expert system, multiclass and multilabel classification
Abstract
Demolition needs special attention because the planning process is complex and has a high risk. In decision making, practitioners face various conditions that influence the choice of demolition methods. This study aims to develop an optimal model for building demolition methods based on the building characteristics. Identification of criteria was conducted in in-depth literature review and interviews with practitioners who have carried out demolition in Indonesia. The five criteria used in this study were structural characteristics, field conditions, costs, previous experience and time. Furthermore, Multiclass and Multi-label Classification were used to make the optimum demolition method decisions. For model validation, 27 story buildings in Indonesia were used as case studies. The simulation results show that the proposed model can make decisions on the selection of demolition methods with an accuracy of 89.3%. In addition to being able to provide an optimum decision on demolition methods, this model can also provide an estimate of the possible impacts of the selected demolition method.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - CONF
AU  - Oryza Lhara Sari
AU  - Tri Joko Wahyu Adi
AU  - Abdul Munif
PY  - 2019/10
DA  - 2019/10
TI  - Selection Model of Building Demolition Method Based on Expert System
BT  - Proceedings of the Third International Conference on Sustainable Innovation 2019 – Technology and Engineering (IcoSITE 2019)
PB  - Atlantis Press
SP  - 58
EP  - 61
SN  - 2352-5401
UR  - https://doi.org/10.2991/icosite-19.2019.12
DO  - https://doi.org/10.2991/icosite-19.2019.12
ID  - LharaSari2019/10
ER  -