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Agus Wibowo
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Civil Engineering Science and Technology (CEST)
Published by Universitas Sains Dan Teknologi Komputer
ISSN : 30896908     EISSN : 30896894     DOI : 10.51903
Core Subject : Engineering,
Civil Engineering, Building, Construction & Architecture Engineering Mechanical Engineering
Aim The Civil Engineering Science and Technology (CEST) journal aims to serve as a high-quality scientific publication platform dedicated to the field of civil engineering. The journal focuses on the scientific and technological aspects that contribute to the advancement of methods, materials, and innovations in civil engineering. CEST facilitates the dissemination of cutting-edge research findings that enhance both theoretical understanding and practical applications in the civil engineering industry. By providing a forum for academics, researchers, and practitioners, the journal encourages interdisciplinary collaboration and fosters global advancements in civil engineering science and technology. Scope CEST covers a broad range of topics within civil engineering, including but not limited to: Soil and Rock Mechanics – Studies on geotechnical properties, foundation engineering, slope stability, and soil-structure interactions. Structures and Materials – Research on structural analysis, construction materials, seismic engineering, and innovative building techniques. Hydraulic and Hydrology – Water resources management, fluid mechanics, river engineering, and coastal protection systems. Transportation and Infrastructure – Roadway and railway engineering, traffic management, urban mobility, and smart transportation systems. Environment and Resource Management – Sustainable construction, waste management, climate resilience, and eco-friendly engineering solutions. Information and Computing Technology in Civil Engineering – Applications of AI, BIM (Building Information Modeling), GIS, and digital twin technology in civil engineering. Innovation and Technological Development in Civil Engineering – Emerging trends, automation, robotics, and new methodologies for construction and infrastructure development. Case Studies and Practical Applications – Real-world civil engineering projects, lessons learned, and best practices in various construction domains. By covering these diverse areas, CEST aims to bridge the gap between research and industry, fostering technological innovation and sustainable development in civil engineering.
Arjuna Subject : Teknik (semua kategori) - Teknik Sipil dan Struktur
Articles 6 Documents
 1 
Implementation of Internet of Things (IoT) Technology in Construction Monitoring 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/h3wqm765

Abstract

The construction industry requires advanced monitoring systems to ensure infrastructure safety and sustainability. This study develops a real-time structural health monitoring system integrated with the Internet of Things (IoT) and deep learning-based analytics to enhance structural safety during and after construction. The proposed system incorporates multiple smart sensors and employs a Long Short-Term Memory (LSTM) model to detect early structural deformations and predict potential failures. The experimental results demonstrate that the IoT-based monitoring system significantly improves accuracy in tracking humidity (92.4%), temperature (94.8%), pressure (94.1%), and vibration (97.2%) compared to conventional manual inspections. A comparative analysis with global implementations in Singapore and Japan highlights the efficiency of edge computing integration in reducing latency and improving data reliability. The findings underscore the importance of integrating deep learning with IoT to enhance predictive maintenance in the construction industry. This research contributes to the development of a more accurate, real-time, and scalable monitoring system for ensuring infrastructure resilience and sustainability.
The Use of Drones for Surveying and 3D Modeling in Construction Projectsn Suwandi; Angga Setyadi Tommy
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/ccspb273

Abstract

This study investigates the integration of drone mapping technology with Building Information Modeling (BIM) to enhance accuracy and efficiency in construction projects. By utilizing LiDAR-equipped drones, the research demonstrates significant improvements in topographic mapping, reducing error rates to less than 2% compared to 10% with conventional surveying methods. Additionally, the integration of real-time drone data into BIM optimizes design precision and streamlines project coordination. The study's findings highlight a substantial reduction in survey time and an increase in decision-making accuracy. Despite the benefits, challenges such as high initial investment and workforce training remain obstacles to widespread adoption. This research provides insights into overcoming these barriers and presents a framework for future advancements in drone-BIM integration for construction 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

Abstract

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

Abstract

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
AI-Driven Digital Twin for Predictive Maintenance in Urban Infrastructure: Enhancing Structural Resilience and Sustainability Dita Diana; Putri Anindita; Anggi Angga Mukti
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: 10.51903/3c72e647

Abstract

The increasing complexity of urban infrastructure necessitates more efficient and proactive maintenance strategies. Traditional maintenance approaches often rely on reactive measures, leading to increased costs, unplanned downtime, and potential structural failures. The emergence of Artificial Intelligence (AI)-driven Digital Twin technology offers a promising solution by enabling predictive maintenance through real-time monitoring and advanced analytics. This study aimed to evaluate the effectiveness of AI-driven Digital Twin systems in enhancing predictive maintenance for urban infrastructure. A qualitative case study methodology was employed, analyzing multiple infrastructure projects that integrated Digital Twin technology. Data were collected from project reports, real-time sensor outputs, and expert interviews. The predictive capabilities of machine learning models, including Decision Trees, Support Vector Machines (SVM), and Deep Learning networks, were assessed based on their precision, recall, and F1-score. The results demonstrated that Deep Learning models achieved the highest fault detection accuracy, with an F1-score of 92.5%, outperforming other models. The adoption of Digital Twin systems resulted in a 30% reduction in maintenance costs and a 40% decrease in infrastructure downtime. Additionally, AI-driven predictive maintenance improved fault detection efficiency, reducing the average detection time from 15 days to 3 days. These findings highlight the potential of AI-enhanced Digital Twins in optimizing urban infrastructure resilience, cost efficiency, and sustainability. This study underscores the importance of integrating AI and Digital Twin technologies in predictive maintenance strategies. Future research should focus on addressing implementation challenges, including data security, interoperability, and computational costs, to facilitate broader adoption in smart city development
Performance Evaluation of Recycled Concrete Aggregates in Seismic-Resistant Structural Design Said Maulana Ibrahim; Anel Prasyas
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: 10.51903/9d6ba216

Abstract

The increasing demand for construction materials has led to excessive exploitation of natural aggregates, raising concerns about environmental sustainability and carbon emissions. Recycled Concrete Aggregate (RCA) has been introduced as a potential alternative to natural aggregates, but its application in seismic-resistant structures remains a challenge due to its lower mechanical properties. This study aims to evaluate the structural performance of RCA-based concrete in seismic applications by analyzing its compressive strength, tensile strength, elastic modulus, and cyclic loading resistance. An experimental approach was employed, where concrete samples with RCA proportions of 0%, 25%, 50%, 75%, and 100% were tested under standardized laboratory conditions. The results indicate that RCA can be used up to 50% without significant loss of compressive strength, which remained above 30 MPa. However, at RCA proportions above 50%, compressive strength decreased by up to 30%, and the elastic modulus dropped from 30.2 GPa (0% RCA) to 20.8 GPa (100% RCA). Cyclic loading tests further revealed a reduction in energy dissipation capacity, from 85 kJ at 0% RCA to 55 kJ at 100% RCA, and an increase in residual deformation. These findings highlight the need for mix optimization in high-RCA concrete, such as incorporating supplementary materials like fly ash, nano-silica, or fiber reinforcement to enhance mechanical performance. This study contributes to the sustainable development of construction materials by providing insights into the feasibility and limitations of RCA in seismic-resistant structures

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