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All Journal Paduraksa : Jurnal Teknik Sipil Universitas Warmadewa Green Engineering: Journal of Engineering and Applied Science
Riza Phahlevi Marwanto
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Engineering Environmental Science

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Evaluasi kondisi permukaan jalan metode pavement condition indeks dalam meningkatkan keselamatan pada Ruas Penghubung Weleri-Sukorejo Hasna Aulia Rahma; Riza Phahlevi Marwanto; Yogi Oktopianto
PADURAKSA: Jurnal Teknik Sipil Universitas Warmadewa 166-173
Publisher : Program Studi Teknik Sipil, Fakultas Teknik dan Perencanaan, Universitas Warmadewa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22225/pd.14.1.12436.166-173

Abstract

Road surface degradation poses significant risks to service quality and traffic safety, necessitating systematic evaluation for effective rehabilitation. This study assessed the condition of the Weleri-Sukorejo road segment using the Pavement Condition Index (PCI) method and safety inspections to formulate targeted rehabilitation strategies. The 12 km road was divided into six segments for visual surveys, with PCI calculations following ASTM D6433 guidelines. Results revealed PCI values ranging from 28 (very poor) to 99 (excellent), averaging 64.5 (fair). Critical findings identified severe damage in segments 0+000–2+000 (PCI 35) and 2+100–4+000 (PCI 28), dominated by potholes (29%) and polished aggregate (24%), requiring immediate structural intervention. Moderate degradation in segments 10+100–12+000 (PCI 64) and 4+100–6+000 (PCI 72) highlighted the need for periodic maintenance, while high-PCI segments (6+100–10+000) warranted routine upkeep. Key contributing factors included overloading, substandard materials, and inadequate drainage. Recommendations prioritize structural rehabilitation, material standardization, and preventive maintenance to enhance road safety and longevity. The study contributes by providing a technical evaluation approach based on PCI data to support more accurate, measurable, and safety-oriented road rehabilitation planning on national road segments.
AI driven Circular Waste to Energy Conversion System Using Smart Thermal Monitoring and Emission Optimization for Sustainable Urban Infrastructure Kiki Ahmad Baihaqi; Krisna Widi Nugraha; Rian Ardianto; Rosyid Ridlo Al-Hakim; Riza Phahlevi Marwanto; Erick Fernando
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 2 (2025): April : Green Engineering: International Journal of Engineering and Applied Sci
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i2.289

Abstract

This study explores the integration of Artificial Intelligence (AI) with thermal optimization in Waste-to-Energy (WtE) systems to enhance both energy recovery and emission control. Introduction: The growing need for sustainable urban waste management has highlighted the importance of optimizing WtE systems. AI technologies, including machine learning and deep learning, have shown potential in improving the efficiency of WtE processes, especially in reducing emissions and enhancing energy recovery. Literature Review: Previous research indicates that AI has been successfully applied to various WtE technologies such as pyrolysis, gasification, and incineration, yet the integration of AI specifically for thermal optimization remains underexplored. Most studies focus on predictive models for emission reduction rather than real time thermal optimization. Materials and Method: The study proposes the development of an AI-driven framework that integrates real time data collection from IoT sensors, predictive modeling, and real time control algorithms. The system optimizes key parameters such as combustion temperature and fuel flow to enhance energy recovery and minimize emissions. The method includes data collection from operational WtE plants, followed by model development using machine learning algorithms. Results and Discussion: Initial simulations and pilot testing showed significant improvements in energy efficiency and emission reduction. AI-driven systems outperformed conventional WtE systems by optimizing operational parameters in real time. The study identifies gaps in AI integration for thermal optimization and suggests future research directions, including the integration of AI with smart grids and carbon credit systems for more sustainable WtE operations.
Co-Authors Baihaqi, Kiki Ahmad Erick Fernando Hasna Aulia Rahma Krisna Widi Nugraha Rian Ardianto Rosyid Ridlo Al-Hakim Yogi Oktopianto