Frisilia, Ni Putu Ika (2019) Sistem Peringatan Dini pada Pekerjaan Temporary Support untuk Pengecoran Beton Berbasis Integrasi Bayesian Belief Network (BBN) dan Building Information Modeling (BIM). Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
ndustri konstruksi merupakan kegiatan yang kompleks dan berisiko, sehingga angka kecelakaan yang terjadi lebih tinggi dibandingkan industri lain. Disisi lain dengan berkembangnya construction 4.0, teknologi konstruksi juga terus berkembang ke arah digitalisasi khususnya Building Information Modeling (BIM). Namun, hubungan antara safety management dengan model informasi berbasis teknologi belum banyak dikembangkan. Penelitian ini bertujuan untuk membuat sistem yang terintegrasi antara proses kegiatan konstruksi yang berisiko/berpotensi pada pekerjaan Temporaray Support (bekisting dan scaffolding) berbasis Bayesian Belief Network (BBN) dengan Building Information Modeling (BIM). Indentifikasi variabel dilakukan dengan literature review, kemudian dilakukan wawancara kepada expert (pelaksana dan QHSE). Didapatkan lima variabel penyebab terjadinya risiko dan empat variabel risiko yang akan terjadi. Kemudian untuk hubungan antar variabel, didapatkan dengan melakukan wawancara kepada expert (pelaksana dan QHSE). Selanjutnya, membuat model Bayesian Belief Network (BBN) sebagai model pengambilan keputusan. Untuk validasi model, 6 bangunan gedung bertingkat di Indonesia dijadikan sebagai implementasi. Hasil simulasi menunjukkan bahwa model dapat menghasilkan kondisi yang sama dengan kondisi nyata dengan ketepatan 91.7%. Kemudian hasil output dari model digunakan untuk integrasi dengan BIM Software. Integrasi dilakukan dengan menghubungkan Model Bayesian Belief Network (BBN) dengan Building Information Modeling (BIM), informasi yang digabungkan meliputi permodelan gedung, jadwal pekerjaan, dan risiko yang didapatkan dari hasil Bayesian Belief Network (BBN. =================================================================================================================================
Construction activities are very complex and risky. Therefore, the number of accidents in the construction industry is higher compared to other industries. On the other hand, the construction industry 4.0, forced construction technology to develop into the world of digitalization, especially Building Information Modeling (BIM). However, the relationship between safety management and technology-based information models has not been widely developed. This study aims to propose a model that integrates construction risks and BIM. Variable identification is done by literature review, then interviews with experts (implementing and QHSE). Obtained five variables that cause risk and four risk variables that will occur. Then for the relationship between variables, obtained by conducting interviews with experts (implementing and QHSE). Next, make the Bayesian Belief Network (BBN) model as a decision making model. For model validation, 6 storey buildings in Indonesia are used as implementation. Simulation results show that the model can produce the same conditions with real conditions with an accuracy of 91.7%. Then the output from the model is used for integration with BIM Software. Integration is carried out by connecting the Bayesian Belief Network (BBN) Model with Building Information Modeling (BIM), information combined using building modeling, work schedules, and risks obtained from the results of the Bayesian Belief Network (BBN). Keyword: construction industry, building, safety management, Bayesian belief network, building information modeling
| Item Type: | Thesis (Masters) |
|---|---|
| Additional Information: | RTS 624.102 87 Fri s-1 2019 |
| Uncontrolled Keywords: | industri konstruksi, bangunan gedung, safety management, bayesian belief network, building information modeling |
| Subjects: | T Technology > TH Building construction > TH438 Construction industry--Management. Project management. |
| Divisions: | Faculty of Civil, Environmental, and Geo Engineering > Civil Engineering > 22101-(S2) Master Theses |
| Depositing User: | NI PUTU IKA FRISILIA |
| Date Deposited: | 19 Dec 2023 03:38 |
| Last Modified: | 19 Dec 2023 03:38 |
| URI: | http://repository.its.ac.id/id/eprint/72840 |
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