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All Journal IPTEK Journal of Proceedings Series Jurnal Dimensi Pratama Teknik Sipil Rekayasa Sipil Journal of Civil Engineering Civil Engineering Dimension
Willy Husada
Petra Christian University
Civil Engineering, Building, Construction & Architecture Computer Science & IT

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Optimasi Desain Penampang Struktur Rangka Batang Baja Berbasis Reliabilitas Menggunakan Symbiotic Organisms Search dan Artificial Neural Network Willy Husada; Doddy Prayogo; Christoffel Felio Thamrin; Ronald Herdjijono
Rekayasa Sipil Vol 15, No 3 (2021)
Publisher : Department of Civil Engineering, Faculty of Engineering, Universitas Brawijaya

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Abstract

Safety and economic factors are the two main consideration in designing a structure. The structural engineer always try to find the optimal structure design with minimum cost that satisfy the safety requirement. This safety requirement can be expressed as structural reliability that associated to a certain failure probability threshold. An integrated Reliability-based Design Optimization (RBDO) framework usually employed to minimize the cost objective function subjected to the failure probability limit. Failure probability mostly computed by using a time-consuming Monte Carlo Simulation (MCS) method. This study develops two hybrid RBDO framework, SOS-ANN and PSO-ANN, which combine the metaheuristic method, Symbiotic Organisms Search (SOS) and Particle Swarm Optimization (PSO) with a machine learning method, Artificial Neural Network (ANN). The SOS and PSO method are used to solve the discrete optimization problem. The ANN method is adopted to replace the MCS method in predicting the reliability of every solution using binary classification. A practical RBDO case of steel truss structure is used to demonstrate the performance of both SOS-ANN and PSO-ANN method in finding the optimal structural design. The results show that the SOS-ANN method outperforms the PSO-ANN method in terms of solution quality, computational efficiency and consistency.
Comparative Study on Data Mining Methods in Structural Reliability Prediction Willy Husada; I-Tung Yang; Tri Joko Wahyu
IPTEK Journal of Proceedings Series No 1 (2015): 1st International Seminar on Science and Technology (ISST) 2015
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (174.579 KB) | DOI: 10.12962/j23546026.y2015i1.1098

Abstract

The goal of reliability-based design optimization (RBDO) is to find the optimal structure design with minimum cost subjected to maximum failure probability limit. Since failure probability is usually small, it takes a large amount of computation time for accurate estimation in reliability analysis. Surrogate models usually created to replace the time-consuming reliability analysis. In this empirical study, we use several data mining methods with focus on classification and regression tree (CART), artificial neural network (ANN) and support vector machine (SVM) method to create the surrogate models on a empirical benchmark case study. We aim to find the best data mining method in predicting the failure probability which divided into two parts: classification and regression. The main findings of this study is that CART method performed better than ANN and SVM in both classification and regression. Support vector machine (SVM) method is the worst in both cases.
PENENTUAN MODEL PENILAIAN PRIORITAS RISIKO PADA PROYEK GEDUNG BERTINGKAT SAAT PANDEMI DI SURABAYA Ricky Christian Chandra; Aland Kane Ujuto; Doddy Prayogo; Willy Husada
Jurnal Dimensi Pratama Teknik Sipil Vol 11, No 2 (2022): September 2022
Publisher : Jurnal Dimensi Pratama Teknik Sipil

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Abstract

Keadaan alamiah proyek konstruksi yang dinamis dan kompleks menuntun proyek konstruksi menjadi penuh ketidakpastian dan dapat mempengaruhi performa dari perusahaan konstruksi apabila ketidakpastian tidak segera diatasi. Penelitian ini bertujuan untuk mengembangkan model prioritas risiko agar dapat membantu pengambil keputusan mengidentifikasi dan mengelola risiko kritis yang memengaruhi proyek konstruksi gedung bertingkat di Surabaya pada saat pandemi COVID-19 yang telah memunculkan banyak risiko dan ketidakpastian yang baru. Data penelitian diperoleh dengan menyebarkan kuesioner kepada kontraktor, manajemen konstruksi, tim owner/developer, dan akademisi di Surabaya. Metode pendekatan yang digunakan yaitu pendekatan tradisional (analisis deskriptif mean) dan pendekatan berbasis monte carlo simulation yang mendukung prioritas risiko dan bisa mengidentifikasi risiko dengan probabilitas dan dampak yang rendah hingga tinggi. Hasil penelitian menunjukkan hasil data memiliki nilai yang kurang lebih sama dan tidak ada risiko yang terletak di zona eksposur berisiko tinggi. Namun, usulan penentuan model direkomendasikan menggunakan pendekatan metode monte carlo simulation karena memiliki keuntungan yang lebih baik menggunakan simulasi dalam menguji data sehingga hasilnya jauh lebih mendekati keadaan sesungguhnya dan tidak dapat menyebabkan beberapa risiko kritis terabaikan.
Optimization of Counterfort Retaining Wall Structure with Shear Key using Metaheuristic Method Gogot Setyo Budi; Joel Glenn Chandra; Bryan Saputra Ongkowardhana; Willy Husada
Civil Engineering Dimension Vol. 26 No. 2 (2024): SEPTEMBER 2024
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/ced.26.2.151-159

Abstract

This paper presents the optimization work to obtain the most econo­mical of counterfort retaining wall structure with shear key attached at its base using metaheuristic method. The metaheuristic algorithm is a global optimization method that can be used to find the optimum solution of complex problems. In this research, optimization is carried out using the Particle Swarm Optimization (PSO) and Symbiotic Organisms Search (SOS) methods. This research utilizes a retaining wall sitting on stiff clay layer subjected to ten (10) m of granular soil of backfill. The scope of the study is limited to the material cost, that consists of the cost of concrete and reinforcement bars, of the counterfort retaining wall with shear key. The results show that the SOS algorithm resulted a lower cost and relatively faster in obtaining optimum retaining wall design compared to that of the PSO algorithm.
Comparative Study on Artificial Intelligence Methods in Housing Price Prediction Husada, Willy; Reynaldo, Ambrosius Matthew Junius; Hogianto, Josh Felix; Putri, Clarissa Arisanti
Journal of Civil Engineering Vol 40, No 2 (2025)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20861206.v40i2.22747

Abstract

The demand for property, including houses, continues to grow rapidly in Indonesia. The housing price prediction is essential in assisting the stakeholders such as buyers, sellers, and investors to make better decision-making. There are many key factors that influencing the housing prices and it is challenging to identify the most relevant factors. This study provides a comparative analysis of various methods in the housing price prediction that consists of one traditional method, Linear Regression (LR), and three artificial intelligence (AI) methods, including Artificial Neural Network (ANN), Classification and Regression Tree (CART), and Chi-Squared Automatic Interaction Detection (CHAID). The aim is to find the best machine learning method in predicting the housing price in terms of prediction accuracy through the four performance indicators and one combined performance index called the reference index (RI). The main findings of this study is that the AI-based method, the ANN method, has the best accuracy indicated by its highest RI value hence outperforming other methods in predicting the housing prices.
Vehicle Routing Problem Optimization for Rebar Material Distribution using the Symbiotic Organisms Search Method Reynaldo, Ambrosius Matthew Junius; Husada, Willy; Wijaya, Ezra Kenzie; Vaphilio, Denish
Civil Engineering Dimension Vol. 27 No. 2 (2025): SEPTEMBER 2025
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/ced.27.2.203-213

Abstract

The success rate of construction projects depends on subcontractors and material suppliers, especially in ensuring the material delivery to avoid delays and cost overruns. The Vehicle Routing Problem (VRP) addresses transportation management to minimize the distribution costs. This study presents a comparative analysis of three VRP scenarios: the existing case, the Capacitated Vehicle Routing Problem (CVRP), and the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). The Symbiotic Organisms Search (SOS) method is used to solve the VRP of a building materials supplier in Sulawesi, Indonesia in delivering rebar to 19 locations over 12 weeks while considering the vehicle capacity and the time window constraints. The results show that the SOS method effectively handles the rebar distribution problems with these constraints. The CVRP scenario achieves a total cost saving of Rp 23,263,278 (26.15%), while the CVRPTW scenario saves Rp 6,732,942 (7.57%).
Co-Authors Aland Kane Ujuto Bryan Saputra Ongkowardhana Christoffel Felio Thamrin Doddy Prayogo Doddy Prayogo Gogot Setyo Budi Hogianto, Josh Felix I-Tung Yang Joel Glenn Chandra Putri, Clarissa Arisanti Reynaldo, Ambrosius Matthew Junius Reynaldo, Ambrosius Matthew Junius Ricky Christian Chandra Ronald Herdjijono Tri Joko Wahyu Vaphilio, Denish Wijaya, Ezra Kenzie