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Optimization of Soil Stabilization Techniques Using Nanomaterials for Enhanced Foundation Performance Reja Putra Jaya; Kristianus Tommy Hendryarto; Suwandi
Civil Engineering Science and Technology Vol. 1 No. 1 (2025): March | CEST (Civil Engineering Science and Technology)
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/0h45k090

Soil stabilization is a crucial aspect of geotechnical engineering aimed at enhancing bearing capacity and structural load resistance. Conventional methods, such as cement and lime, are commonly used but contribute to high carbon emissions, necessitating the exploration of more sustainable alternatives. One promising approach is the utilization of nanomaterials in soil stabilization. This study evaluates the effectiveness of nano-silica, nano-clay, and graphene oxide in improving soil properties and identifies the optimal dosage for practical applications. Laboratory experiments were conducted to measure Unconfined Compressive Strength (UCS), permeability, and dry density following nanomaterial treatment. The results demonstrate that graphene oxide (1.5%) yields the highest UCS increase, reaching 330 kPa, compared to 120 kPa in untreated soil. Nano-silica (2.5%) also significantly improves UCS to 315 kPa, while nano-clay (3.0%) exhibits the most effective permeability reduction to 6.2 × 10⁻⁵ cm/s. Statistical analysis using Response Surface Methodology (RSM) confirms that an optimal nanomaterial dosage can effectively enhance soil stability without compromising other physical properties. This study contributes to the advancement of nanotechnology applications in geotechnical engineering, providing an efficient and environmentally friendly alternative to conventional stabilization techniques. The findings offer a foundation for real-world implementation of nanomaterial-based soil stabilization and support the development of more sustainable infrastructure solutions.

Integration of AI and Digital Twin Technology for Smart Infrastructure Management in Urban Cities Angga Setyadi Tommy; Reja Putra Jaya
Civil Engineering Science and Technology Vol. 1 No. 1 (2025): March | CEST (Civil Engineering Science and Technology)
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/t881qw28

The rapid growth of urban populations presents significant challenges in infrastructure management, including increased maintenance costs, energy inefficiencies, and rising risks of structural failures. To address these issues, integrating Artificial Intelligence (AI) and Digital Twin technology has emerged as a promising approach for predictive infrastructure management. This study aims to evaluate the effectiveness of AI and Digital Twin integration in improving urban infrastructure resilience, optimizing maintenance strategies, and enhancing energy efficiency. A case study methodology was employed, utilizing real-time data from IoT sensors and historical maintenance records to develop AI-driven predictive models. The research applied machine learning algorithms, including Decision Tree, Random Forest, and Long Short-Term Memory (LSTM), for failure prediction, combined with Digital Twin simulations to optimize infrastructure management. The results indicate that the AI-based predictive failure model achieved an accuracy of 92%, significantly reducing the risk of infrastructure failure by 70%. Furthermore, the integration of AI and Digital Twin led to a 60% reduction in maintenance costs and a 35% improvement in energy efficiency, particularly in urban lighting and public facility management. These results demonstrate that the adoption of AI and Digital Twin technology can transform conventional infrastructure management by enabling proactive and cost-effective maintenance strategies. This study contributes to the growing body of knowledge on smart city infrastructure by providing empirical evidence on the benefits of AI-driven predictive analytics and Digital Twin simulations in enhancing urban sustainability and operational efficiency

Optimization of Soil Stabilization Techniques Using Nanomaterials for Enhanced Foundation Performance Reja Putra Jaya; Kristianus Tommy Hendryarto; Suwandi
Civil Engineering Science and Technology Vol. 1 No. 1 (2025): March | CEST (Civil Engineering Science and Technology)
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/0h45k090

Soil stabilization is a crucial aspect of geotechnical engineering aimed at enhancing bearing capacity and structural load resistance. Conventional methods, such as cement and lime, are commonly used but contribute to high carbon emissions, necessitating the exploration of more sustainable alternatives. One promising approach is the utilization of nanomaterials in soil stabilization. This study evaluates the effectiveness of nano-silica, nano-clay, and graphene oxide in improving soil properties and identifies the optimal dosage for practical applications. Laboratory experiments were conducted to measure Unconfined Compressive Strength (UCS), permeability, and dry density following nanomaterial treatment. The results demonstrate that graphene oxide (1.5%) yields the highest UCS increase, reaching 330 kPa, compared to 120 kPa in untreated soil. Nano-silica (2.5%) also significantly improves UCS to 315 kPa, while nano-clay (3.0%) exhibits the most effective permeability reduction to 6.2 × 10⁻⁵ cm/s. Statistical analysis using Response Surface Methodology (RSM) confirms that an optimal nanomaterial dosage can effectively enhance soil stability without compromising other physical properties. This study contributes to the advancement of nanotechnology applications in geotechnical engineering, providing an efficient and environmentally friendly alternative to conventional stabilization techniques. The findings offer a foundation for real-world implementation of nanomaterial-based soil stabilization and support the development of more sustainable infrastructure solutions.

Integration of AI and Digital Twin Technology for Smart Infrastructure Management in Urban Cities Angga Setyadi Tommy; Reja Putra Jaya
Civil Engineering Science and Technology Vol. 1 No. 1 (2025): March | CEST (Civil Engineering Science and Technology)
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/t881qw28

The rapid growth of urban populations presents significant challenges in infrastructure management, including increased maintenance costs, energy inefficiencies, and rising risks of structural failures. To address these issues, integrating Artificial Intelligence (AI) and Digital Twin technology has emerged as a promising approach for predictive infrastructure management. This study aims to evaluate the effectiveness of AI and Digital Twin integration in improving urban infrastructure resilience, optimizing maintenance strategies, and enhancing energy efficiency. A case study methodology was employed, utilizing real-time data from IoT sensors and historical maintenance records to develop AI-driven predictive models. The research applied machine learning algorithms, including Decision Tree, Random Forest, and Long Short-Term Memory (LSTM), for failure prediction, combined with Digital Twin simulations to optimize infrastructure management. The results indicate that the AI-based predictive failure model achieved an accuracy of 92%, significantly reducing the risk of infrastructure failure by 70%. Furthermore, the integration of AI and Digital Twin led to a 60% reduction in maintenance costs and a 35% improvement in energy efficiency, particularly in urban lighting and public facility management. These results demonstrate that the adoption of AI and Digital Twin technology can transform conventional infrastructure management by enabling proactive and cost-effective maintenance strategies. This study contributes to the growing body of knowledge on smart city infrastructure by providing empirical evidence on the benefits of AI-driven predictive analytics and Digital Twin simulations in enhancing urban sustainability and operational efficiency

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