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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 20 Documents
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AI-Driven Digital Twin for Predictive Maintenance in Urban Infrastructure: Enhancing Structural Resilience and Sustainability Diana, Dita; Anindita, Putri; Mukti, Anggi Angga
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 Ibrahim, Said Maulana; Prasyas, Anel
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
Integrating Predictive AI Models to Bridge Energy Efficiency Gaps in Smart Building Design Sugiarto, Sugiarto; Christopher, Liam; Grace, Amelia
Civil Engineering Science and Technology Vol. 1 No. 2 (2025): October | CEST (Civil Engineering Science and Technology)
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/2fwp7m63

Abstract

Energy efficiency has become one of the most important aspects in smart building design, especially considering the gap that has increasingly been noted between simulated energy performance and actual consumption. Even though digital design tools like BIM have enhanced design capabilities, there is still a big gap in energy performance, usually rooted in the static nature of traditional simulations. This research tries to respond to this challenge by proposing a conceptual framework linking predictive AI models with BIM for enhanced accuracy in early design stage forecasting. Other than a few studies that revolved around optimization in the post-occupancy phase, this study applies a conceptual-simulative methodology by using a synthetic BIM model of a medium-sized office building. Machine learning algorithms, such as random forest and gradient boosting, were trained on parameterized design data for predicting EUI. Strong predictive consistency was identified with an R² of 0.89 between the predicted and simulated EUI and a conceptual reduction of the performance gap of about 18%. The model also shows robust logical correspondence to the concepts of energy efficiency within a wide range of design scenarios. This research concludes that predictive AI can significantly improve energy performance forecasting in smart building design and provides a proactive data-driven approach toward overcoming the energy efficiency gap in support of more sustainable architectural practices without immediate physical field testing.
Integrating Climate-Resilient Design And Life Cycle Costing In Green Building Projects: A Simulation-Based Assessment In Tropical Urban Areas Arifin, Samsul; Setiyadi, Angga; Purwanto, Purwanto; Sugiarto, Sugiarto
Civil Engineering Science and Technology Vol. 1 No. 2 (2025): October | CEST (Civil Engineering Science and Technology)
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/3cq4x038

Abstract

Tropical urban areas are increasingly exposed to the compounded impacts of climate change, including rising temperatures, high humidity, and increased rainfall, which pose challenges to the long-term performance, durability, and cost-efficiency of green buildings. This study integrates climate-resilient building design strategies with Life Cycle Costing (LCC) to evaluate both the technical performance and long-term economic feasibility of green building projects in tropical urban environments. A simulation-based building performance assessment was conducted to model key microclimatic variables, solar radiation, thermal loads, and precipitation, and their impacts on building envelope performance, passive cooling strategies, and water management systems. Simulation outputs were incorporated into an LCC framework to compare multiple design scenarios over a 30-year operational lifecycle. The results indicate that climate-resilient design alternatives reduce annual building energy demand by approximately 15–25% and lower total life-cycle costs by 10–18% compared to baseline green-building configurations, despite an initial capital cost increase of 5–12%. These findings demonstrate that investments in climate-adaptive strategies enhance long-term cost efficiency, operational stability, and resilience to extreme climate conditions in tropical cities. This study provides a coherent simulation-based framework that links environmental performance analysis with life-cycle economic evaluation, offering practical decision-support insights for architects, engineers, developers, and policymakers. By quantitatively revealing trade-offs between initial investment and long-term benefits, the research addresses a critical gap in current green building assessment practices and supports the development of financially viable and climate-resilient urban building solutions.
Valorization Of Industrial Ash Waste As Eco-Friendly Binder For Pavement Applications: Experimental Study On Strength And Permeability Properties Alexis, Ruben; Huereca, Camacho
Civil Engineering Science and Technology Vol. 1 No. 2 (2025): October | 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/fd9z5952

Abstract

As the construction industry faces increasing pressure to reduce its environmental footprint, the use of industrial byproducts as alternative construction materials presents a promising strategy for promoting sustainability. This study investigates the potential valorization of industrial ash waste, particularly fly ash and steel slag, as an eco-friendly binder in permeable pavement applications. The main objective is to engineer a sustainable binder mix that aligns with circular economy principles while maintaining structural and hydraulic performance, particularly in tropical climate conditions. A series of experimental tests was conducted on various ash-based binder formulations to evaluate compressive strength, permeability rate, and durability under simulated tropical environmental exposure. Complementary microstructural analyses using scanning electron microscopy (SEM) and X-ray diffraction (XRD) were also performed to explore the internal bonding characteristics and hydration behavior. The results revealed that specific combinations of fly ash and steel slag achieved compressive strength values comparable to conventional cement-based binders, while exhibiting significantly higher water permeability, an essential feature for stormwater management in urban areas. Moreover, the binder demonstrated good resistance to moisture-induced degradation. These findings suggest that industrial ash waste can be effectively transformed into high-performance, low-impact materials for green infrastructure. This research contributes to both material innovation and sustainable engineering practices, offering a viable solution for environmentally responsible pavement design in developing countries with tropical climates.
AI-Driven Optimization of Project Cost and Duration in Infrastructure Development Projects Leite, Marcos; Silva, Beatriz
Civil Engineering Science and Technology Vol. 1 No. 2 (2025): October | 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/yemg8d35

Abstract

The rapid increase in global infrastructure costs is causing delays, and it is difficult for policy-makers and engineers to generate sustainable and consistent outcomes. In this research study, we provide a framework of artificial intelligence to improve cost and schedule in large-scale infrastructure projects, through aggregated holistic data acquisition, gradient boosted prediction models, and particle-swarms multi-objective optimisation. Using historical project data, current sensor IoT data, digital twin simulation, and drone surveys, we develop a quality dataset for validation and training. The models generated are highly predictive in performance compared to traditional scheduling methods, and robust when material prices vary and/ or labour disruptions apply. In addition we conduct scenario testing to confirm that the framework is able to provide realizable recommendations and allow for adaptive scheduling modifications through the use of interactive dashboards. Being able to provide actual costing estimation and adaptive scheduling in real-time provides construction professionals and PMP's the opportunity to better management site performance thereby reducing overruns and simplifying resource allocation, and also very soon capable of responding to site change. The artificial intelligence approach is a promising route to intelligent, data-driven project positioning artificial intelligence as a viable long-term approach towards saving costs and planning timelines strategically, and sustainable construction project scheduling.  
Integrating Machine Learning for Dynamic Resource Allocation in Civil Engineering Project Scheduling Mokoena, Thandiwe; Nkosi, Sipho
Civil Engineering Science and Technology Vol. 1 No. 2 (2025): October | 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/b5d4sw69

Abstract

Civil construction projects globally are growing more complex because of resource constraints, environmental constraints and schedule uncertainties accumulate. The task presented here generates a machine framework based on learning to address these issues by enabling dynamic, interpretable, and sustainable planning the allocation of resources for comprehensive infrastructure scheduling. The method combines gradient enhanced decision trees and time-based convolutional networks driven by immediate sensing and digital replicas technologies for capturing nonlinearity and time-dependent relationships, thereby maintaining the sensitivity of the predictions to altered situational parameters. Rigorous assessments carried out on urban infrastructure across multiple locations. Projects demonstrate that the proposed model enhances planning precision by more than twenty percent and significantly improves average resource usage when compared to conventional optimization techniques. In addition to measurable efficiency benefits, the structure offers human impact via reduced materials waste reduction and energy efficiency enhancement, promoting global sustainability initiatives while guaranteeing safer construction advancement The strategy's reasoning equips project leaders to interpret and apply every recommendation instantly, promoting the development of managerial trust and fostering integration through daily routines. This combination of predictive capability, transparency, and environmental sustainability offers Civil engineering managers equipped with a robust tool to complete projects ahead of schedule and ensure enduring stability. The following sections outline the integrated method, present empirical data, and respond to the broadened strategies, methodologies, and investigatory pathways for upcoming sustainable infrastructure growth.
Multi-Agent AI Simulation for Evaluating Sustainability of Urban Transportation Infrastructure James, Ethan; Martinez, Sofia
Civil Engineering Science and Technology Vol. 1 No. 2 (2025): October | 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/tx30kz14

Abstract

Urban transport systems in tropical cities are under increasing strain due to rapid population growth, rising mobility demand, and climate-induced stresses. These conditions create highly complex, dynamic interactions among users, vehicles, infrastructure, and environmental factors that are difficult to capture with conventional modeling approaches. Most existing transport planning frameworks rely on static or single-agent models, limiting their ability to represent real-time feedback and adaptive behavior, particularly in tropical megacities characterized by congestion, climatic volatility, and socio-economic diversity. This paper introduces a multi-agent artificial intelligence (AI) simulation framework designed as an exploratory tool for evaluating the sustainability of urban transportation infrastructure. The proposed framework integrates dynamic systems theory with multi-agent reinforcement learning to simulate interactions among heterogeneous transport agents and infrastructure components. Synthetic data reflecting typical tropical urban conditions are employed to enable controlled experimentation across multiple scenarios, including baseline, optimized infrastructure, and adaptive AI control settings. Simulation results indicate that adaptive AI scenarios outperform baseline configurations, demonstrating 25.6% higher energy efficiency, 31.4% lower congestion, and 21.8% lower emissions in the modeled environment. These outcomes illustrate the potential of the proposed framework to support comparative sustainability evaluation rather than direct real-world performance validation.
Developing an Adaptive Resilience Index for Flood-Resistant Urban Drainage Systems Louise, Emma; Takeda, Hiroshi
Civil Engineering Science and Technology Vol. 1 No. 2 (2025): October | CEST (Civil Engineering Science and Technology)
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/02bn1165

Abstract

Most urban drainage systems worldwide are under unprecedented stress due to extreme weather driven by climate change and rapid urbanization. This creates severe challenges for traditional static design paradigms. While the different resilience indices proposed to date provide useful performance benchmarks, they reflect the system state at a single point in time and do not capture the system’s dynamic capacity to adapt and learn under evolving climatic pressures and urban growth. This paper helps fill this research gap by introducing a new conceptual framework, the Adaptive Resilience Index (ARI). ARI integrates three core dimensions: System Capacity, Climatic Sensitivity, and Adaptive Capacity, offering a potential framework for assessing adaptive resilience in urban drainage systems. The tripartite structure provides a dynamic lens to evaluate system robustness, vulnerability to climatic shifts, and proactive capacity for learning and reorganization. Although the ARI has not yet been empirically tested, its proposed indicators could be operationalized in future studies through surveys, modeling, and institutional assessments, thereby guiding the development of more resilient, adaptive, and sustainable urban drainage infrastructure in a climate-uncertain world.
Enhancing Urban Drainage in Coastal Cities: A Simulation-Based Assessment of Nature-Based Solutions for Climate Resilience Timur, Prihatin; Johnson, Alex; Carter, Emily; Laksana, Sofyan Dwi
Civil Engineering Science and Technology Vol. 1 No. 2 (2025): October | 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/ks8maj14

Abstract

Climate change is now fully expressed through extreme rainfall and sea-level rise, and it is a major threat to coastal cities globally. Additionally, the exhaustion of urbanization makes the situation even more difficult. Conventional drainage systems are overburdened by the rising demand; thus, Nature-Based Solutions offer a way to build systemic resilience which is characterized by the restoration of natural hydrological functions. The main objective of this paper is to analyze the role of integrated NBS in the improvement of hydraulic performance in the drainage of tropical coastal cities. In this regard, we conduct a systematic literature review alongside scenario-based simulations using the Storm Water Management Model (SWMM) which is supplied with synthetic data that reflects a typical tropical coastal city. The findings suggest that a distributed network of bioswales, rain gardens, and permeable pavements may decrease the peak discharge and total runoff volume by 28.8% and 29.0% respectively, these changes involving to a great extent infiltration enhancement and time to peak delay. Hence, this research provides a quantifiable, conceptual basis that is applicable to the field of urban planners and engineers as a means of warranting the trend of NBS as an essential part of the living adaptations in jeopardized coastal urban zones.

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