Civil Engineering Science and Technology
Civil Engineering Science and Technology (CEST) is a scientific journal dedicated to publishing high-quality research in civil engineering. The journal focuses on the scientific and technological aspects that contribute to developing methods, materials, and innovations in civil engineering. CEST covers a wide range of topics, including soil and rock mechanics, structures and materials, hydraulics and hydrology, transportation and infrastructure, environmental and resource management, information and computing technology in civil engineering, technological innovations, as well as case studies and real-world applications in civil engineering projects. The journal is published twice a year, in March and October. CEST serves as a platform for academics, researchers, and professionals to disseminate the latest findings that support the advancement of civil engineering knowledge and practices on a global scale. CEST welcomes original research articles, review papers, and case studies, ensuring that the latest developments in the field contribute to both academic research and practical applications. Journal Index : e-ISSN | p-ISSN | GOOGLE SCHOLAR | GARUDA Journal Attribute: DOI | CROSSREF | ORCID
Universitas Sains dan Teknologi Komputer
Design and Development of an Interactive Virtual Tour for Enhancing Architectural Design Communication in Civil Engineering Projects
This study addresses the limitations of traditional two-dimensional (2D) architectural drawings in effectively communicating spatial design to diverse stakeholders in civil engineering projects. The research aims to design and develop an interactive virtual tour system and to evaluate its effectiveness in improving architectural design communication. A Design and Development Research approach was employed, consisting of analysis, design, development, testing, and evaluation stages. The system was developed using SketchUp, Lumion, and Unity, and evaluated by 24 participants, including 12 professional designers and 12 non-technical clients. Data were collected through observation, questionnaires, and interviews, and analyzed using descriptive statistics and paired-samples t-tests. The results show that the virtual tour significantly outperformed 2D drawings across all evaluation aspects, with usability scores of 4.6 ± 0.28 compared to 3.2 ± 0.45, aesthetics scores of 4.7 ± 0.25 compared to 3.5 ± 0.40, and interactivity scores of 4.6 ± 0.30 compared to 2.9 ± 0.50, all with p-values < 0.01. These findings indicate that the virtual tour enhances spatial understanding, user engagement, and communication clarity. The novelty of this study lies in the explicit integration of realistic three-dimensional rendering, interactive navigation, and user-centered design principles within a Design and Development Research framework, combined with empirical evaluation involving both technical and non-technical stakeholders in a real project context. This study contributes to both theory and practice by demonstrating that interactive virtual tours serve as an effective communication medium, reducing misinterpretation and supporting more informed decision-making in civil engineering design processes.
Copyright (c) 2026 Civil Engineering Science and Technology https://creativecommons.org/licenses/by-sa/4.0
AI-Driven Digital Infrastructure and Infrastructure Innovation Systems: A Conceptual Analysis
The evolution of infrastructure development increasingly relies on integrating digital technologies to support complex, multi-actor innovation systems. Modern infrastructure projects involve governments, construction industries, research institutions, and technology providers, requiring efficient coordination, data integration, and collaborative decision-making mechanisms. This study examines the role of AI-Driven Digital Infrastructure in enhancing coordination and collaboration within Infrastructure Innovation Systems. A conceptual literature analysis was employed to synthesize prior research on AI applications, digital infrastructure, and innovation systems and map relationships between AI capabilities and systemic innovation dynamics. This study adopts a purely conceptual research design based on structured literature synthesis rather than empirical testing. Additionally, illustrative conceptual scenarios demonstrate potential coordination mechanisms that may facilitate information flows, inter-organizational alignment, and collective decision-making. The analysis suggests that AI-Driven Digital Infrastructure may function as a structural enabler contributing to institutional alignment, knowledge integration, and multi-stakeholder interaction within infrastructure ecosystems. The study provides a conceptual framework that links digital intelligence to systemic innovation processes, highlighting the role of AI as a backbone for coordination within complex infrastructure networks. This research contributes to theory by integrating perspectives from digital infrastructure, AI, and Infrastructure Innovation Systems into a unified analytical model. It offers conceptual insights to inform future research on digital infrastructure development.
AI-Driven Digital Twin for Urban Transport Infrastructure Network Operations Optimization
Urban transport infrastructure networks play a critical role in enabling efficient mobility and sustainable urban development in smart-city environments. However, many existing digital twin studies in transportation primarily focus on infrastructure monitoring and sensor-based traffic management. At the same time, limited research has explored the integration of artificial intelligence (AI) and digital twin simulation to optimize network-level transport operations. This study aims to develop a conceptual AI-driven digital twin framework for urban transport infrastructure networks to optimize traffic flow, network capacity, and overall mobility efficiency. The research adopts a simulation-based and experimental approach, combining transport network modeling using graph theory with synthetic traffic datasets that represent nodes such as intersections, terminals, and stations, as well as edges representing urban road corridors. The framework incorporates AI-based optimization models, including reinforcement learning and heuristic optimization techniques, to evaluate alternative operational scenarios such as baseline network operations, AI-assisted traffic flow optimization, and adaptive infrastructure management. Simulation results indicate the potential for improved network operational performance under controlled experimental conditions, including reduced congestion levels and more balanced traffic distribution across the network. These findings are limited to simulated environments and do not represent real-world validation. Consequently, the proposed framework provides a scalable, exploratory analytical approach for assessing transport operational strategies and supporting data-driven decision-making in urban transport infrastructure management and smart city mobility planning within simulation-based contexts.
Simulation-Based Infrastructure Modeling
Case Study on Structural Design Implementation for Architecturally Complex Reinforced Concrete Buildings
The growing need for architecturally distinctive buildings with complex shapes poses a challenge to structural engineers in reinforced concrete construction. This paper examines the effectiveness of structural design implementation in architecturally complex reinforced concrete buildings through early interdisciplinary collaboration and analytical techniques. A single-case study method was adopted, and the 45-story Meru Tower in Jakarta, with a twisted façade and irregular floor plans, was selected as the case study. Data collection was done using semi-structured interviews with eight key participants and document analysis of structural design calculations, finite element analysis results, and BIM coordination reports. The results show that early co-location of structural engineers with architects, assisted by structured BIM procedures, facilitated real-time geometric feasibility checks and avoided costly design conflicts at a later stage. Technical problems related to torsional irregularities and stress concentrations were addressed through response spectrum analysis, nonlinear time-history analysis, outrigger walls, and post-tensioned transfer girders. Quantitative validation confirmed that all performance criteria were met, including a 0.18% drift ratio, a 1.15 torsional ratio, and a maximum stress of 16.2 MPa, with a 9% reduction in concrete volume. This paper presents a three-phase approach derived from the Meru Tower case, offering practical insights for structural design implementation in architecturally complex concrete buildings. However, further validation across multiple cases is required.
Integration of Building Information Modeling (BIM) and Artificial Intelligence for Predicting Construction Project Risks
Building Information Modeling (BIM) has increasingly been adopted in construction projects; however, its potential for quantitative risk prediction remains underexplored. This study aims to examine the influence of BIM-based variables on project risk severity and to develop a predictive model that explains variations in schedule delay and cost overrun. A quantitative approach was employed, using data from 42 completed construction projects that implemented 3D, 4D, and 5D BIM. Four independent variables-Geometric Complexity Index, Scheduling Density Ratio, Cost Intensity Coefficient, and Object Interdependency Level-were extracted from BIM objects and analyzed using multiple linear regression and machine learning models, including Random Forest, Support Vector Machine, and Artificial Neural Network. The results indicate that the regression model explains 68% of the variance in project risk severity, with scheduling density and cost intensity emerging as the most influential predictors. Among the machine learning models, the Artificial Neural Network achieved the highest predictive accuracy, demonstrating superior capability in capturing nonlinear relationships among BIM-derived attributes. These findings confirm that structured BIM-based metrics can serve as reliable indicators for proactive risk assessment. The study contributes to integrating BIM analytics into construction risk management frameworks and highlights the importance of data-driven decision-making in improving project performance.
Flood Modeling Using HEC-RAS Based on Extreme Rainfall Data
Flooding remains one of the most significant hydrological hazards in many river basins, particularly as extreme rainfall events intensify. This study employs a quantitative research design based on hydrological and hydraulic modeling to analyze flood behavior under extreme rainfall scenarios. The research integrates extreme rainfall frequency analysis, rainfall–runoff transformation, and hydraulic simulation using HEC-RAS, developed by the U.S. Army Corps of Engineers. Design rainfall for multiple return periods was analyzed and converted to peak discharge, which was subsequently used as input to a hydraulic simulation to generate water surface elevation profiles, flow velocity distributions, and flood inundation maps. The results indicate a progressive increase in peak discharge and inundation extent with higher return periods, with a more pronounced hydraulic response under extreme recurrence intervals. Flooding at lower return periods remains confined within the river channel, whereas at higher return periods, flows exceed channel capacity and significantly expand inundation areas. These findings highlight the importance of integrated quantitative modeling in supporting flood risk assessment, river capacity planning, and resilient spatial development strategies. The proposed modeling framework provides a comprehensive basis for linking extreme rainfall characteristics to spatial flood hazard mapping in water resources management.
Enhancing Urban Drainage in Coastal Cities: A Simulation-Based Assessment of Nature-Based Solutions for Climate Resilience
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.
Copyright (c) 2025 Civil Engineering Science and Technology https://creativecommons.org/licenses/by-sa/4.0
Developing an Adaptive Resilience Index for Flood-Resistant Urban Drainage Systems
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.
Integrating Predictive AI Models to Bridge Energy Efficiency Gaps in Smart Building Design
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.
Multi-Agent AI Simulation for Evaluating Sustainability of Urban Transportation Infrastructure
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.
Artificial Intelligence in Transportation
Adaptive BIM–IDS Framework for Semantic-Level Data Interoperability in Construction 5.0 Environments
Physical infrastructure further becomes digitalized, but semantic-level interoperability between Building Information Modeling (BIM) and Information Delivery Specification (IDS) is a problem that is preventing one-size-fits-all information exchange. Conceptually, the current study conceives and analyzes an Adaptive BIM–IDS Framework that can extend support toward semantic conjoining within Construction 5.0 environments through ontology-based reasoning, adaptive mapping, and human–machine cooperation. A constructive research and simulation approach was adopted, wherein a Systematic Literature Review was integrated with PRISMA and dummy dataset simulation of BIM–IDS. Ontology-based mapping, SHACL validation, and SPARQL reasoning were adhered to for semantic completeness, logical consistency, and semantic flexibility of the framework. Performance was tested in three replicable tests Context Recognition Accuracy (CRA), Interoperability Consistency Ratio (ICR), and Adaptive Mapping Success Rate (AMSR) that exhibited higher semantic compatibility than a non-semantic control. While results are hypothetical, simulation-based, rather than empirical, the study presents replicable reason pipeline and formalized semantic ontology to support adaptive, ontology-enabled interoperability feasibility. Outcomes guide theoretical foundation of adaptive BIM–IDS integration and drive subsequent empirical application toward semantically harmonized, human-centered Construction 5.0 systems.
Information Delivery Specification (IDS)
Integrating Climate-Resilient Design And Life Cycle Costing In Green Building Projects: A Simulation-Based Assessment In Tropical Urban Areas
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
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.
Integrating Machine Learning for Dynamic Resource Allocation in Civil Engineering Project Scheduling
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.
AI-Driven Optimization of Project Cost and Duration in Infrastructure Development Projects
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.
Performance Evaluation of Recycled Concrete Aggregates in Seismic-Resistant Structural Design
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
Copyright (c) 2025 Civil Engineering Science and Technology
AI-Driven Digital Twin for Predictive Maintenance in Urban Infrastructure: Enhancing Structural Resilience and Sustainability
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
Integration of AI and Digital Twin Technology for Smart Infrastructure Management in Urban Cities
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
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.
The Use of Drones for Surveying and 3D Modeling in Construction Projectsn
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
Implementation of Internet of Things (IoT) Technology in Construction Monitoring
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.