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All Journal Jurnal Teknik Sipil Journal of Natural Science and Technology Adpertisi: Bidang Sains dan Teknologi Jurnal Media Teknik Sipil Widya Teknik
Frans, Richard
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Transportation

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Aplikasi Metode Vektor Beban Penentu Lokasi Kerusakan pada Struktur Thick Plate Frans, Richard; Arfiadi, Yoyong
Jurnal Teknik Sipil Vol 15, No 4 (2020)
Publisher : Program Studi Teknik Sipil Fakultas Teknik Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (489.648 KB) | DOI: 10.24002/jts.v15i4.3792

Abstract

Salah satu metode yang sering digunakan dalam mendeteksi kerusakan elemen dalam struktur adalah metode vektor beban penentu lokasi kerusakan. Hal ini dikarenakan metode ini cukup mudah untuk digunakan dan memberi hasil yang cukup memuaskan dalam mendeteksi kerusakan struktur. Pada penelitian-penelitian sebelumnya, metode vektor beban penentu lokasi kerusakan ini telah banyak diaplikasikan ke berbagai jenis (tipe) struktur misalnya pada struktur balok, struktur rangka, struktur portal, dan lain sebagainya, Oleh karena itu, pada penelitian ini, metode beban penentu lokasi kerusakan ini diaplikasikan pada struktur thick plate. Terdapat dua skenario pembagian elemen dengan masing-masing tiga skenario kerusakan. Skenario pembagian elemen yaitu struktur thick plate akan dibagi menjadi 4 elemen dan 81 elemen. Pemodelan yang digunakan adalah dengan menggunakan metode elemen hingga dengan mengasumsikan elemen pelat tersebut sebagai elemen eight-noded quadratic quadrilateral plate bending. Tujuan dari penelitian ini adalah untuk mengetahui kemampuan dari metode vektor beban penentu lokasi kerusakan dalam menentukan elemen rusak. Berdasarkan hasil yang didapatkan, metode vektor beban penentu lokasi kerusakan cukup akurat dalam menentukan elemen yang diasumsikan mengalami kerusakan.
Pengembangan Aplikasi Analisis Struktur Rangka Batang Bidang Berbasis Antarmuka Pengguna Grafis Frans, Richard; Tjiwanda, Antonius Agusto; Kalangi, Hendry; Thioritz, Stevy; Mursalim, Mursalim
JNSTA ADPERTISI JOURNAL Vol. 2 No. 2 (2022): Juli 2022
Publisher : Aliansi Dosen Perguruan Tinggi Swasta Indonesia (Adpertisi)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62728/jnsta.v2i2.324

Abstract

This research develops an analysis structure program for plane truss structure using stiffness matrix method and based on graphical user interface. The program is developed using GUIDE feature from MATLAB R2014 software. The features that were developed of this program is to analyze the reaction forces, internal forces and global displacement for plane truss structure. There are 3 (three) types of plane structures were considered. Reaction forces, internal forces and global displacement which obtained from the program were compared to commercial program which is ETABS 2016. Based on the result, there is not significantly different between the program which developed and ETABS 2016 for reaction forces, internal forces or global displacement. Therefore, the program can be an alternative software to analyze plane truss structure
PREDIKSI CURAH HUJAN MENGGUNAKAN JARINGAN SARAF TIRUAN DENGAN METODE BACKPROPAGATION Weng, Shane; Frans, Richard; Prattyni, Vinsensia Paola
Jurnal Media Teknik Sipil Vol. 3 No. 2 (2025): Jurnal MeDia Teknik Sipil, Vol. 3, No. 2, 2025
Publisher : Program Studi Teknik Sipil, Fakultas Teknik, Universitas Atma Jaya Makassar

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Abstract

Rainfall is an essential factor in various sectors such as agriculture, water resource management, and hydrometeorological disaster mitigation. The unpredictable and nonlinear characteristics of rainfall patterns make conventional statistical methods less effective for accurate forecasting. This study aims to develop a rainfall prediction model using an Artificial Neural Network (ANN) with the Backpropagation method implemented in MATLAB, and analyze the model’s accuracy and performance based on historical rainfall data. The constructed network architecture consists of four input neurons, twelve hidden neurons, and one output neuron. The training results show that the monthly rainfall model performs more well. The findings indicate that the Backpropagation-based ANN model effectively predicts monthly rainfall and can be used as a decision-support tool for water resource planning and management in the Makassar–Maros region
Damage Detection Based on Recorded Damaged Natural Frequencies Using Binary Bat Algorithm Frans, Richard; Arfiadi, Yoyong
Widya Teknik Vol. 25 No. 1 (2026): Mei
Publisher : Fakultas Teknik, Universitas Katolik Widya Mandala Surabaya

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Abstract

The health monitoring system cycle for a structure has a significant impact on damage detection. The life cycle of a structure can be improved through the implementation of effective damage detection techniques. This is because the identification, prevention, and repair of damaged structural elements can be conducted with precision and speed. This study suggests a technique for identifying structural components that have been damaged. This approach involves comparing the recorded natural frequency to its actual natural frequency for all potential damage scenarios. The Modal Assurance Criterion (MAC) has been used to compare these two natural frequency values, while a binary value is used to indicate whether an element has been damaged or not. Two types of structures are considered: (1) shear building structures and (2) plane truss structures. Several damage scenarios are used to test the accuracy of the proposed method. According to the results, the binary bat algorithm in conjunction with MAC produces accurate results for the two categories of structures examined under several kinds of damage scenarios, such as single, double, and multiple damage.
Co-Authors Kalangi, Hendry Mursalim Mursalim Prattyni, Vinsensia Paola Thioritz, Stevy Tjiwanda, Antonius Agusto Weng, Shane Yoyong Arfiadi