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International Journal of Research in Vocational Studies (IJRVOCAS)
Published by Yayasan Ghalih Pelopor Pendidikan
ISSN : 27770168     EISSN : 27770141     DOI : https://doi.org/10.53893/ijrvocas.v1i1
Core Subject : Economy, Science, Education, Social, Engineering,
Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other
The International Journal of Research in Vocational Studies (IJRVOCAS) is a double-blind peer-reviewed journal. This journal provides full open access to its content on the principle that making research freely and independently available to the science community and the public supports a greater global exchange of knowledge and the further development of expertise in the field of vocational education and training (VET). IJRVOCAS is since the beginning independent from any non-scientific third-party funding. The establishment of the journal was supported between 2015 and 2016 with grants from the Yayasan Ghalih Pelopor Pendidikan (Ghalih Foundation). All members of IJRVOCAS work on an honorary basis. The journal is hosted by Ghalih Publishing and the publishing house of the Ghalih Academic. Scope IJRVOCAS covers all topics of VET-related research from pre-vocational education (PVE), initial vocational education and training (IVET) and career and technical education (CTE) to workforce education (WE), human resource development (HRD), professional education and training (PET) and continuing vocational education and training (CVET).
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 242 Documents
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Predicting the Compressive Strength of Ultra-high Strength Geopolymer Concrete Using Multiple Linear Regression Marini, Lelly
International Journal of Research in Vocational Studies (IJRVOCAS) Vol. 5 No. 4 (2026): IJRVOCAS - Special Issues - Hybrid International Conference on Construction, Ma
Publisher : Yayasan Ghalih Pelopor Pendidikan (Ghalih Foundation)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53893/ijrvocas.v5i4.483

Abstract

The growing demand for sustainable yet high-performance construction materials has intensified research into alternatives to Ordinary Portland Cement (OPC), whose production accounts for approximately 7–8% of global CO₂ emissions. Geopolymer Concrete (GPC), synthesized through the alkali activation of aluminosilicate-rich industrial by-products, has emerged as a promising low-carbon binder. However, the design of Ultra-High-Performance Geopolymer Concrete (UHGC), typically characterized by compressive strengths exceeding 120 MPa, remains highly complex due to the strong sensitivity of mechanical performance to mix composition, activator chemistry, and reinforcement parameters. This study proposes a transparent, data-driven framework for predicting and optimizing UHGC compressive strength using Multiple Linear Regression (MLR). A comprehensive dataset comprising 72 UHGC mixtures (122.9–168.8 MPa) was compiled, incorporating key variables including precursor ratio, Si/Al ratio, steel fiber volume fraction, superplasticizer content, and water-to-binder ratio. The MLR model demonstrated excellent predictive accuracy and generalization, achieving R² values of 0.944 and 0.921 for training and testing datasets, respectively, with low RMSE (~4.5 MPa). Statistical analysis confirmed the dominance of the Si/Al ratio and water-to-binder ratio as the most influential parameters governing UHGC strength. Experimental validation using nine independently designed UHGC mixtures further confirmed the robustness of the model, yielding a high correlation between predicted and measured strengths (R² = 0.954) with a mean absolute percentage error below 1%. The optimal formulation achieved a compressive strength of 168.8 MPa at a Si/Al ratio of approximately 6.0 with 1.0% steel fiber content. Compared to more complex machine learning models, the proposed MLR approach offers competitive accuracy while retaining full interpretability, enabling rational mix design and informed decision-making. This study demonstrates that interpretable predictive modeling can effectively bridge geopolymer chemistry and UHGC mix optimization, providing a practical and sustainable pathway for the development of next-generation ultra-high-performance construction materials.
Evaluation of Structural Dynamic Parameters of the Old Truss Bridges Using Smartphone-Embedded Sensors Faisal, Muhammad Hanif; Putranto, Alan; Ismah, Julia Nurzata; Rayani, Annisa Dwi
International Journal of Research in Vocational Studies (IJRVOCAS) Vol. 5 No. 4 (2026): IJRVOCAS - Special Issues - Hybrid International Conference on Construction, Ma
Publisher : Yayasan Ghalih Pelopor Pendidikan (Ghalih Foundation)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53893/ijrvocas.v5i4.484

Abstract

Bridges are critical components of transportation infrastructure that experience continuous dynamic loading from traffic and environmental factors, leading to gradual deterioration of structural performance. Conventional Structural Health Monitoring (SHM) systems are often constrained by high cost and operational complexity. This study evaluates the application of smartphone-embedded accelerometers combined with the Ambient Vibration Test (AVT) method to identify the dynamic parameters of old steel truss bridges in Ketapang, West Kalimantan. A non-destructive and cost-effective approach was employed by utilizing daily traffic as a natural excitation source. Several bridges were selected based on service age, visible deterioration, and operational condition. Vibration data were collected using the Resonance Android application, which records acceleration and processes it into frequency spectra. Dominant frequencies and damping ratios were extracted and analyzed to assess the dynamic response of the structures. Field measurements on the Pawan 1 and Pawan 2 bridges revealed that several parameters—such as natural frequency, displacement, and damping ratio—exceeded standard thresholds, indicating potential structural degradation. These findings demonstrate that smartphone-based monitoring can serve as an effective preliminary diagnostic tool, providing valuable insights to support maintenance decisions and guide further detailed structural assessments.

Page 25 of 25 | Total Record : 242

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