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"Proyek ini bertujuan untuk menentukan peringkat tahan api dari dinding baja ringan di bawah kondisi api menggunakan aplikasi kecerdasan buatan. Dua bagian bagian saluran yang diberi lipatan (LCS) dan bagian saluran berongga flange (HFC) grade 500 dan kelas 250 disajikan dalam penelitian ini. LCS adalah jenis konvensional yang digunakan dalam bingkai baja ringan, sementara HFC memperkenalkan memiliki kinerja api yang unggul. Baru-baru ini pemodelan elemen hingga dan uji skala penuh telah digunakan untuk menentukan kinerja api dinding LSF. Meskipun demikian, pemodelan elemen hingga ditemukan memiliki prosedur yang rumit, dan uji skala penuh adalah eksperimen yang memakan waktu. Oleh karena itu, opsi alternatif sebagai pembelajaran mesin diperlukan untuk mengatasi situasi ini. Pendekatan jaringan saraf pembelajaran mesin akan diadopsi untuk melatih data. Masukan akan menjadi data aktual dari FEA dan proyek uji penuh skala sebelumnya. Temperatur dan suhu flensa dan flensa dingin seksi dari suatu bagian diperoleh sebagai input. Kapasitas pengurangan rasio bertindak sebagai output yang akan diprediksi dalam pembelajaran yang diawasi. Pelatihan dan uji coba dilakukan melalui jaringan saraf tiruan dengan menggabungkan parameter yang berbeda seperti fungsi kehilangan, menjaga faktor probabilitas, tingkat pembelajaran, jumlah lapisan, dan neuron. Rasio pengurangan kapasitas yang diperoleh dari pelatihan mesin dapat diplot dan dibandingkan keakuratannya dengan hasil FEA sebelumnya.

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This project aims to determine fire resistance rating of Light Gauge Steel Frame (LSF) walls under fire condition using artificial intelligence application. Two section of lipped channel section (LCS) and hollow flange channel section (HFC) grade 500 and grade 250 is presented in this research. LCS is a conventional section used in LSF framing, while HFC introduced having superior fire performance. Recently finite element modelling and a full-scale test have been employed to determine fire performance of LSF walls. Nonetheless, finite element modelling was found to have a complicated procedure, and the full-scale test was a time-consuming experiment. Therefore, an alternative option as machine learning is necessary to overcome this situation. A neural network approach of machine learning will be adopted to train the data. The input would be the actual data from FEA and full-scale test previous project. Hot flange and cold flange temperature and dimension of a section are obtained as the input. Capacity reduction ratio act as an output that will be predicted in supervised learning. Training and testing trialare done through the artificial neural network by combining different parameters such as loss function, keep probability factor, learning rate, the number of layers, and neurons. Capacity reduction ratio attained from machine training can be plotted and compared its accuracy with previous FEA results."

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