MULTISCALE FIBER-REINFORCED SUSTAINABLE CONCRETE WITH HIGH-VOLUME FLY ASH AND PORCELAIN AGGREGATE

The construction industry is increasingly focused on sustainable materials that reduce environmental impact while maintaining high mechanical performance. One promising strategy involves incorporating industrial waste materials into concrete, such as high-volume fly ash (FA) as a cement substitute and recycled porcelain aggregate (PA) as fine aggregate. This study investigates a novel sustainable concrete system enhanced with multiscale fibers to overcome the strength limitations typically associated with high replacement levels. The goal is to develop an eco-friendly material capable of meeting structural performance requirements while promoting circular economy principles.

Optimization of Fly Ash and Porcelain Aggregate Replacement

A key objective of the research was to determine the optimal proportions of FA (0–50 %) and PA (0–100 %) required to achieve a target compressive strength of 45 MPa. Through experimental testing and numerical modeling, the study demonstrated that even at maximum replacement levels, the concrete achieved a compressive strength of 44.3 MPa at 28 days. This finding confirms that significant reductions in cement and natural sand consumption are possible without severely compromising structural performance, making the mixture highly sustainable.

Role of Multiscale Fiber Reinforcement

To further enhance performance, the optimized mixture incorporated multiscale fibers consisting of 1 % steel fibers (macro-scale) and 0.1 % graphene nanotubes (nano-scale). This hybrid reinforcement strategy creates a hierarchical strengthening mechanism across different length scales. Steel fibers improve crack resistance and load transfer at the macro level, while graphene nanotubes refine the microstructure and inhibit microcrack propagation at the nanoscale, producing a synergistic improvement in overall material behavior.

Mechanical Performance Enhancement

The addition of multiscale fibers significantly improved key mechanical properties. Compressive strength increased by 13.3 %, while flexural strength and direct tensile strength improved dramatically by 145.8 % and 44.4 %, respectively. These results indicate that fiber reinforcement is particularly effective in enhancing tensile-related properties, which are typically weak in conventional concrete. Such improvements broaden the applicability of sustainable concrete for structural components subjected to bending, tension, and dynamic loads.

Impact Resistance and Energy Absorption

One of the most striking outcomes of the study was the substantial increase in impact resistance. The energy absorption capacity at the initial cracking stage rose from 98.1 J to 5790.8 J after fiber incorporation. This enhancement is attributed to multiscale crack-bridging mechanisms, where fibers arrest crack growth at different stages and scales, thereby delaying failure and dissipating energy. Such behavior is critical for infrastructure exposed to repeated or sudden loading, including pavements, industrial floors, and protective structures.

Microstructural Characteristics and Engineering Applications

Microstructural analyses using ICP-MS, XRD, and STA tests revealed that graphene nanotubes and fly ash contributed to pore refinement, enhanced pozzolanic reactions, and improved bonding within the cement matrix. The combined effects of waste material utilization and multiscale reinforcement produced a dense, durable microstructure with superior mechanical performance. Consequently, this advanced sustainable concrete is well suited for applications requiring high tensile strength and impact resistance, particularly rigid pavements and heavy-duty infrastructure systems.

#EcoFriendlyMaterials
#HighPerformanceConcrete
#RigidPavement
#ImpactResistance
#CompressiveStrength
#FlexuralStrength
#TensileStrength
#ConstructionInnovation
#CircularEconomy
#CivilEngineering
#SustainableInfrastructure
#AdvancedMaterials

BOREHOLE PRESSURE SHEAR TESTER (BPST) FOR IN-SITU EVALUATION OF WEATHERED GEOMATERIALS

Accurate characterization of weathered geomaterials—comprising residual soils and weathered rocks—is essential for ensuring the stability and safety of civil engineering structures. These materials exhibit transitional behavior between soil and rock, making their mechanical properties difficult to assess using conventional techniques. Laboratory testing is often impractical because obtaining undisturbed samples from weathered layers is extremely challenging. Consequently, reliable in-situ testing methods are crucial for capturing true field conditions and improving geotechnical design accuracy.

Limitations of Conventional Field Testing Methods

Traditional field tests are typically developed either for soils or for intact rock masses, leading to significant shortcomings when applied to intermediate geomaterials. Weathered layers possess heterogeneous structures, variable stiffness, and complex failure mechanisms that standard tests cannot fully capture. As a result, existing techniques may produce unreliable estimates of deformation and shear strength, potentially compromising engineering decisions for foundations, slopes, and underground structures.

Development of the Borehole Pressure Shear Tester (BPST)

To address these limitations, this study introduces the Borehole Pressure Shear Tester (BPST), an innovative device that combines the principles of pressuremeter testing and borehole shear testing. The BPST applies controlled horizontal pressure and shear forces directly within a borehole, enabling simultaneous assessment of deformation characteristics and shear strength parameters. This integrated approach provides a more comprehensive understanding of the mechanical behavior of weathered geomaterials compared to single-mode testing methods.

Testing Procedure and Measurement Capabilities

The BPST operates by expanding against the borehole wall while applying tangential shear displacement, replicating realistic stress conditions encountered in situ. This allows direct measurement of deformation modulus and shear resistance under controlled loading paths. By capturing both normal and shear responses in a single test, the device reduces uncertainty associated with extrapolating parameters from separate tests and improves efficiency in field investigations.

Experimental Validation on Residual Soils

Empirical tests conducted on residual soils at simulated high relative densities and varying overburden stresses demonstrated the effectiveness of the BPST. The measured deformation moduli and shear strength parameters showed strong agreement with results obtained from conventional triaxial compression and direct shear tests. This correlation confirms the accuracy and reliability of the device for characterizing intermediate geomaterials under realistic field conditions.

Implications for Geotechnical Design and Infrastructure Safety

The introduction of the BPST represents a significant advancement in geotechnical site investigation. By enabling accurate in-situ evaluation of weathered layers, the device enhances predictive modeling, supports safer foundation design, and reduces uncertainty in stability assessments. Its ability to characterize materials that fall between soil and rock categories makes it particularly valuable for projects involving slopes, tunnels, deep foundations, and infrastructure built on weathered terrain.

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#ShearStrength

#DeformationModulus

#SiteCharacterization

#EngineeringGeology

#InfrastructureSafety

#FieldTesting

#SoilMechanics

#GeotechnicalInnovation

#SlopeStability

#SubsurfaceEngineering

SOFT ACTOR-CRITIC REINFORCEMENT LEARNING FOR ROBUST ACTIVE STRUCTURAL VIBRATION CONTROL

Active structural control systems are among the most effective technologies for suppressing vibrations in civil engineering structures subjected to dynamic loads such as earthquakes and wind. However, conventional active control methods often suffer from performance degradation due to time delays, measurement noise, and changing operational conditions. To overcome these limitations, data-driven control strategies based on reinforcement learning have emerged as promising alternatives. This study introduces advanced soft actor-critic (SAC)–based control approaches designed to enhance adaptability and robustness in complex real-world environments.

Challenges in Traditional Active Control Systems

Traditional controllers, such as linear quadratic Gaussian (LQG) control, rely on predefined system models and fixed parameters. In practical applications, uncertainties such as sensor noise, communication delays, and environmental variability can significantly reduce their effectiveness. These issues may lead to instability or loss of control performance during critical events. Consequently, there is a growing need for intelligent control strategies capable of learning from real-time data and adapting to evolving structural conditions.

Parameter Real-Time Regulator Strategy

The first proposed SAC-based approach introduces a parameter real-time regulator that dynamically adjusts controller parameters using feedback from the environment. By continuously updating its control policy, this strategy maintains effective vibration suppression even when system characteristics change. The regulator demonstrates strong adaptability, particularly in scenarios where external disturbances or structural properties vary over time, making it suitable for practical structural control applications.

Independent Data-Driven Controller

The second strategy replaces conventional control algorithms entirely with an independent reinforcement learning controller. This SAC-based controller learns optimal control actions directly from environmental interactions without relying on prior system models. As a result, it can handle highly nonlinear behavior and unknown dynamics. Experimental results indicate that this approach significantly improves displacement control compared to traditional methods, though its performance in acceleration mitigation varies depending on operating conditions.

Hybrid Compensator Strategy

The third strategy combines reinforcement learning with traditional control methods through a compensator mechanism. Instead of replacing the baseline controller, the SAC algorithm adjusts the control signal in real time to compensate for uncertainties, delays, and noise. This hybrid approach leverages the stability of classical control and the adaptability of machine learning. Among the tested methods, the compensator demonstrates the most effective reduction in structural responses, achieving the highest improvements in both drift and acceleration control.

Performance Evaluation Under Uncertainty

To assess robustness, the control environment was augmented with random time delays and noise, simulating realistic operating conditions. Seven performance criteria were used to evaluate effectiveness. Results show that all SAC-based strategies maintain stable and efficient control, whereas the traditional LQG controller loses effectiveness under severe disturbances. Strategy C (hybrid compensator) achieves the best overall performance, reducing peak inter-story drift by up to 84.50% and peak acceleration by 63.45%. These findings highlight the strong potential of reinforcement learning for next-generation intelligent structural control systems.

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#DataDrivenControl

#AIinEngineering

#InfrastructureSafety

#ControlSystems

#IntelligentInfrastructure

#StructuralHealth

#RobustControl

#EngineeringInnovation

#SeismicProtection

#FutureEngineering

FOAMED GLASS AGGREGATE AS A LIGHTWEIGHT SUSTAINABLE GEOMATERIAL FOR GEOTECHNICAL INFRASTRUCTURE

Foamed glass aggregate (FGA) is an innovative lightweight geomaterial manufactured from recycled glass through a sinter-foaming process. As sustainability becomes a central priority in civil engineering, FGA has emerged as a promising alternative to conventional granular fills. Its highly porous cellular structure results in extremely low density, excellent thermal insulation, and efficient drainage performance. These characteristics make FGA particularly suitable for applications such as embankments, backfills, retaining structures, and foundation systems where weight reduction and environmental benefits are essential.

Production Mechanisms and Microstructural Formation

The engineering performance of FGA originates from its manufacturing process, in which glass particle size, sintering temperature, and foaming agent dosage interact to create a controlled cellular microstructure. During sintering, gas released from the foaming agent becomes trapped within softened glass particles, forming interconnected pores. The resulting pore size distribution, connectivity, and wall thickness determine the aggregate’s mechanical strength, density, and durability. Understanding these production parameters is crucial for tailoring FGA to specific geotechnical requirements.

Influence of Porosity on Engineering Properties

The intrinsic porosity of FGA governs its macroscopic behavior. High void content produces low unit weight and strong thermal insulation, while pore connectivity enhances drainage capacity. However, excessive porosity may reduce strength and increase compressibility. The study highlights the concept of intra-void ratio as a key parameter controlling deformation resistance, load-bearing capacity, and thermal conductivity. This relationship underscores the need to balance lightweight characteristics with structural performance.

Compaction Behavior and Strength Characteristics

Unlike natural soils, FGA exhibits unique compaction responses due to its rigid cellular particles and low particle crushing resistance. Variations in particle size distribution, specific gravity, and pore structure significantly influence compaction efficiency and resulting strength. The material’s degradation behavior under load is also linked to pore wall integrity and internal structure. These factors determine whether FGA can function effectively as a load-bearing geomaterial in infrastructure projects.

Limitations of Conventional Soil Classification

Traditional soil classification systems and compaction methods were developed for natural granular materials and may not accurately represent FGA behavior. The research emphasizes that applying standard soil mechanics approaches can lead to misleading design assumptions. Instead, a new unified classification framework based on intrinsic structural parameters—such as apparent specific gravity, bulk density, and intra-porosity—is recommended. Such a system would better capture the engineered nature of FGA and support reliable design practices.

Implications for Sustainable Infrastructure Design

By integrating principles from materials science, chemistry, and geotechnical engineering, this study positions FGA as a multifunctional engineered aggregate capable of balancing weight reduction, strength, and durability. Its use of recycled glass contributes to circular economy goals while improving infrastructure resilience. As research advances, FGA has the potential to become a cornerstone material for next-generation sustainable construction, offering environmentally responsible solutions for transportation, foundation, and earthwork applications.

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#CivilEngineering
#EngineeredAggregates
#InfrastructureDesign
#SoilMechanics
#PorousMaterials
#EcoFriendlyMaterials
#FoundationEngineering
#EmbankmentDesign
#ResilientInfrastructure
#FutureConstruction

NON-DESTRUCTIVE ESTIMATION OF UNSATURATED HYDRAULIC CONDUCTIVITY USING TIME-LAPSE GEOPHYSICAL METHODS

Accurate estimation of soil unsaturated hydraulic conductivity is essential for understanding vadose zone flow, groundwater recharge, contaminant transport, and broader subsurface hydrological processes. Conventional point-scale measurements are typically invasive, labor-intensive, and limited in spatial and temporal coverage, making them inadequate for capturing natural field heterogeneity. To address these limitations, non-destructive geophysical techniques offer a promising alternative for continuous, large-scale monitoring of soil moisture dynamics and hydraulic behavior.

Integrated Geophysical Framework

This study introduces an innovative framework that integrates time-lapse ground penetrating radar (GPR) and electrical resistivity tomography (ERT) with an improved instantaneous profile (IIP) inversion approach. By combining electromagnetic and electrical measurements, the method captures changes in soil moisture and pore-water distribution over time. This integrated approach enables the indirect estimation of hydraulic conductivity without disturbing the soil structure, providing high-resolution spatial and temporal insights into subsurface flow processes.

Validation with Laboratory-Based Models

To ensure reliability, the conductivity estimates obtained from geophysical data were validated against laboratory-derived soil water characteristic curve (SWCC) models, specifically the van Genuchten–Mualem (VGM) and Childs–Collis-George (CCG) formulations. These widely accepted models relate soil water retention properties to hydraulic conductivity. The comparison demonstrates that the proposed method can produce results consistent with established theoretical predictions, reinforcing its suitability for practical hydrological applications.

Constitutive Relationships with Electrical Properties

The research further developed parsimonious logarithmic constitutive models linking hydraulic conductivity to relative permittivity and bulk electrical conductivity for two soil types. These relationships leverage measurable geophysical properties to infer hydraulic behavior, enabling rapid estimation across large areas. By translating electrical and dielectric measurements into hydraulic parameters, the approach bridges geophysics and soil physics, facilitating non-invasive subsurface characterization.

Model Performance and Uncertainty Analysis

Predictive performance was evaluated using root mean square error (RMSE) and coefficient of variation (CV) metrics. The models achieved an overall RMSE of 0.32 mm/min, indicating strong agreement with laboratory references. Uncertainty analysis revealed low variability in intermediate moisture conditions, with CV values around 2.5–5.4%, while higher variability (7.1–8.6%) occurred near saturation. This pattern reflects increased complexity in fully saturated soils due to thin-film flow and surface conduction effects.

Implications and Future Research Directions

The findings demonstrate that time-lapse geophysical monitoring can provide spatially explicit, non-destructive estimates of unsaturated hydraulic conductivity, particularly in drained to partially saturated conditions. However, model confidence decreases near saturation, highlighting the need for further refinement. Future work should focus on field-scale validation, joint inversion of multiple geophysical datasets, and improved modeling of saturation effects to enhance transferability across diverse soil environments. This approach holds significant potential for advancing hydrological modeling, agricultural management, and environmental monitoring.

#VanGenuchtenModel

#EnvironmentalEngineering

#SoilPhysics

#GroundwaterResearch

#GeotechnicalEngineering

#FieldHydrology

#EarthScienceResearch

#WaterResources

#CivilEngineering

 

BRIDGE DIGITAL TWINS AND THE ROLE OF LOAD TESTING IN LIFECYCLE MANAGEMENT

Bridge digital twins represent a transformative approach in bridge engineering, enabling the creation of virtual replicas that mirror the physical structure throughout its lifecycle. Originating from advancements in other industries, digital twin technology integrates real-world data with computational models to support monitoring, analysis, and decision-making. In bridge applications, digital twins promise enhanced safety, predictive maintenance, and optimized asset management, making them a critical component of next-generation infrastructure systems.

Concept of Digital Twins in Bridge Engineering

A bridge digital twin combines geometric information, material properties, sensor data, and operational conditions into a unified virtual model. By linking physical bridges with Building Information Modeling (BIM) and Finite Element (FE) models, engineers can simulate structural behavior under varying loads and environmental influences. This integration enables continuous assessment of performance, allowing infrastructure owners to move from reactive maintenance toward proactive management strategies.

Load Testing as the Birth of the Digital Twin

The study emphasizes that bridge load testing marks the “birth” of the digital twin. During this phase, controlled loads are applied to the actual bridge to measure structural responses such as deflection, strain, and vibration. This process provides high-quality empirical data that cannot be obtained at later stages with the same reliability. Consequently, load testing offers a unique opportunity to calibrate digital models so that they accurately represent real structural behavior.

Updating BIM and Finite Element Models

Accurate digital twins depend on well-calibrated BIM and FE models. Load test data enables engineers to validate assumptions regarding stiffness, boundary conditions, and material properties. By updating these models with measured responses, discrepancies between theoretical predictions and actual performance can be minimized. This calibration ensures that the digital twin remains a trustworthy tool for structural analysis, safety evaluation, and performance forecasting.

Application During the Operational Phase

Once established, the digital twin supports the bridge throughout its service life. During operation, it can be continuously updated with monitoring data to detect anomalies, assess damage, and evaluate the effects of aging or environmental changes. This capability allows engineers to predict future performance, schedule maintenance efficiently, and extend service life while maintaining safety standards. Thus, the digital twin becomes an active management system rather than a static model.

Case Study of a Post-Tensioned Concrete Bridge

The concept is demonstrated through modeling and load testing of a real post-tensioned concrete bridge. Post-tensioning introduces complex stress distributions and structural behavior, making accurate modeling particularly important. The case study illustrates how field measurements obtained during testing can refine computational models and establish a reliable digital twin. This example confirms the feasibility and practical value of integrating testing, modeling, and digital technologies in modern bridge engineering.

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#InfrastructureInnovation

#StructuralAnalysis

#EngineeringSimulation

#SmartBridges

#AssetManagement

#StructuralSafety

#InfrastructureLifecycle

#FutureOfConstruction

 

ANISOTROPIC SHEAR BEHAVIOR OF BAMBOO SCRIMBER FOR STRUCTURAL APPLICATIONS

 Bamboo scrimber (BS) is a high-performance engineered bamboo material gaining recognition as a sustainable alternative to conventional structural materials. Produced through the densification and resin impregnation of bamboo fibers, BS exhibits excellent strength, durability, and resource efficiency. Despite its promising mechanical properties, the anisotropic nature of bamboo—stemming from its fibrous structure—leads to direction-dependent behavior that is not yet fully understood, particularly under shear loading. This knowledge gap presents a critical challenge for the reliable structural design of BS components subjected to shear forces.

Experimental Investigation of Shear Properties

To address this challenge, the study conducted an extensive experimental program involving 250 shear tests performed under five distinct loading orientations. This comprehensive testing approach enabled the systematic evaluation of how fiber alignment and loading direction influence shear performance. By capturing a wide range of conditions, the experiments provide a robust dataset for characterizing the mechanical response of BS, ensuring that the findings are representative of practical structural applications.

Failure Modes and Directional Response

The tests revealed multiple failure modes, each strongly dependent on the loading orientation relative to the bamboo fiber direction. Observed mechanisms included fiber pull-out, matrix cracking, interfacial debonding, and shear sliding along weak planes. These distinct failure patterns demonstrate that BS does not exhibit uniform behavior but instead responds differently depending on the direction of applied load. Understanding these mechanisms is essential for predicting structural performance and preventing brittle or premature failures in real-world applications.

Quantification of Anisotropic Shear Strength and Modulus

Analysis of the experimental data showed pronounced anisotropy in both shear strength and shear modulus. Average shear strengths ranged from 12.93 to 37.63 MPa, while shear modulus values varied between 213.6 and 606.3 MPa across orientations. These wide ranges confirm that BS possesses highly direction-dependent mechanical properties. Such variability underscores the necessity of incorporating orientation-specific parameters into structural design calculations to ensure safety and efficiency.

Statistical Modeling and Design Values

To translate experimental findings into practical engineering parameters, the study employed both Normal and Weibull statistical models to derive directional design values. The resulting characteristic shear strengths ranged from 6.33 to 32.58 MPa, reflecting conservative estimates suitable for structural design. The use of probabilistic models enhances reliability by accounting for material variability and uncertainty, supporting performance-based design methodologies for engineered bamboo structures.

Comparative Performance and Structural Design Implications

When compared with traditional construction materials such as concrete, timber, and laminated bamboo, BS demonstrates superior specific shear strength, highlighting its potential for lightweight yet high-capacity structural elements. Based on these findings, the study proposes design recommendations that explicitly consider directional shear behavior, enabling engineers to utilize BS effectively in shear-critical components. These insights contribute to the advancement of sustainable construction and support the development of standardized guidelines for engineered bamboo structures.

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#StructuralEngineering
#PerformanceBasedDesign
#EcoFriendlyMaterials
#BambooEngineering
#HighStrengthMaterials
#RenewableConstruction
#MechanicalProperties
#AdvancedBuildingMaterials
#LightweightStructures
#CivilEngineeringResearch
#SustainableInfrastructure

TRIBOELECTRIC-FUNCTIONALIZED APCY CEMENT: SMART CORE–SHEATH YARN-BASED CEMENTITIOUS COMPOSITES FOR ENERGY-AUTONOMOUS INFRASTRUCTURE

Advanced cementitious composites are rapidly evolving beyond traditional load-bearing functions to incorporate multifunctional smart capabilities such as self-sensing and ambient energy harvesting. These next-generation materials are designed to support intelligent infrastructure systems capable of structural monitoring, data transmission, and autonomous energy supply. This study introduces triboelectric-functionalized cementitious composites reinforced with smart core–sheath braided yarns (APCY), aiming to enable large-scale deployment of energy-autonomous and self-powered civil infrastructure.

Fabrication of Smart Core–Sheath Yarns via Braiding Technology

The APCY yarns are manufactured using advanced braiding technology to produce a core–sheath configuration optimized for mechanical durability and functional performance. A surface roughening modification applied to the guide holes significantly enhances yarn hairiness, thereby increasing effective surface contact area for triboelectric interactions. Importantly, this modification does not compromise spinning speed or production efficiency. As a result, the treated yarn achieves a 123% increase in triboelectric output voltage compared to untreated counterparts, demonstrating a substantial improvement in energy harvesting capability.

Integration into Cement Matrices and Composite Formation

The fabricated APCY yarns are embedded into cement matrices to form APCY cement composites with integrated sensing and energy functionalities. The conductive and triboelectric properties of the yarn enable mechanical-to-electrical energy conversion when subjected to external stimuli such as traffic loads, vibrations, or structural deformation. The composite structure maintains mechanical integrity while introducing multifunctionality, creating a synergistic material system suitable for smart civil infrastructure applications.

Dual Functionality: Self-Sensing and Ambient Energy Harvesting

APCY cement demonstrates dual functionality by combining structural self-sensing with ambient energy harvesting. The triboelectric mechanism allows real-time detection of strain, impact, and vibration events, enabling applications in earthquake self-sensing buildings and bridge monitoring systems. Simultaneously, harvested mechanical energy can power low-energy devices such as wireless sensors or smart streetlights. This integrated capability reduces dependence on external power sources and supports the development of energy-autonomous infrastructure networks.

Intelligent Pavement System with Deep Learning Integration

As a self-powered pavement system, APCY cement integrates deep-learning algorithms and wireless data transmission modules to achieve advanced traffic monitoring capabilities. The system demonstrates 100% accuracy in vehicle identification, speed detection, and personal recognition under experimental conditions. By coupling energy harvesting with intelligent data processing, the composite material enables real-time analytics and smart transportation management without relying on external electrical infrastructure.

Large-Scale Deployment Potential and Smart City Applications

The yarn-based cement composite exhibits strong scalability and adaptability for smart city infrastructure, including intelligent buildings, architectural systems, bridge monitoring platforms, and sports facilities. Its ability to provide both sensing and energy autonomy positions APCY cement as a transformative material for future urban environments. The combination of braiding-based fabrication, triboelectric enhancement, and AI-driven analytics establishes a foundation for sustainable, intelligent, and resilient infrastructure systems.

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#IntelligentPavement

#DeepLearningInInfrastructure

#WirelessMonitoring

#EarthquakeSensing

#SmartCities

#StructuralHealthMonitoring

#NextGenInfrastructure

#CivilEngineeringInnovation

#MultifunctionalMaterials

#SustainableInfrastructure

#AIEnabledConstruction

 

SLAM-CENTRIC FRAMEWORK FOR PRECISE AND PLATFORM-AGNOSTIC ROBOT-AIDED INFRASTRUCTURE INSPECTION

Robot-aided inspection has emerged as a promising solution for enhancing safety, efficiency, and objectivity in infrastructure assessment. However, existing approaches often suffer from inconsistent mapping accuracy, unreliable defect measurements, and platform-specific system designs that limit scalability. This study investigates whether a SLAM-centric (Simultaneous Localization and Mapping) framework can overcome these limitations and enable precise, repeatable, and platform-agnostic visual inspections across diverse infrastructure environments.

Integrated Lidar–Camera–Inertial SLAM Architecture

The proposed framework integrates lidar, camera, and inertial measurement unit (IMU) data within a unified SLAM pipeline to ensure robust localization and mapping under real-world conditions. Multi-sensor fusion enhances pose estimation accuracy and resilience to environmental challenges such as lighting variation, occlusions, and geometric complexity. By centering the inspection workflow around high-fidelity SLAM, the system establishes a reliable spatial reference for defect mapping and measurement, independent of the robotic platform employed.

Offline Trajectory Refinement and Inspection Map Generation

To further improve mapping precision, the framework incorporates offline trajectory refinement, reducing drift and cumulative localization errors commonly observed in real-time SLAM systems. Refined trajectories enable the generation of dense and geometrically consistent inspection maps. These maps serve as a unified spatial representation where inspection data can be consistently overlaid, facilitating repeatable assessments and longitudinal monitoring of infrastructure assets.

Automated Defect Extraction and 3D Ray-Tracing Projection

Visual defect detection is performed through image-based analysis, extracting cracks, spalls, and surface anomalies from captured imagery. A 3D ray-tracing technique projects detected defects into the unified inspection map, ensuring accurate spatial localization and dimensional quantification. This method allows precise measurement of defect size, orientation, and position within the 3D structure, significantly improving reliability compared to traditional qualitative or manual inspection methods.

Validation in Real-World Scenarios

Experimental validation in real-world environments demonstrates that the SLAM-centric framework produces accurate defect localization, consistent dimensional measurements, and high-density inspection maps. The platform-agnostic design ensures adaptability across different robotic systems, including ground vehicles, aerial drones, and climbing robots. The repeatability and robustness of the approach confirm its suitability for practical infrastructure inspection applications.

Implications for Long-Term Monitoring and Automation

By providing an end-to-end solution for robot-aided inspection, the proposed framework enables faster, safer, and more objective infrastructure assessments. The release of datasets and open software tools establishes a foundation for future research in long-term defect monitoring, inspection automation, and predictive maintenance. This SLAM-centric paradigm represents a significant step toward intelligent, data-driven infrastructure management systems.

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#InspectionAutomation

#SmartInfrastructure

#CivilEngineeringTechnology

#DroneInspection

#RoboticsInConstruction

#InfrastructureSafety

#AIforEngineering

#PredictiveMaintenance

#DigitalTwin

 

 

ADVANCING SUSTAINABLE WATER MANAGEMENT THROUGH CIVIL ENGINEERING INNOVATION

Sustainable water management has become a fundamental pillar of global environmental sustainability and resource conservation. Escalating water demand—driven by climate change, rapid urbanization, and population growth—has intensified pressure on existing water infrastructure systems. Civil engineering plays a decisive role in designing, upgrading, and managing water supply, wastewater, and stormwater systems to ensure long-term resilience and sustainability. This study provides a rigorous evaluation of how innovative engineering practices and emerging technologies are transforming water management toward more sustainable and equitable paradigms.

Sustainable Water Supply Systems and Technological Innovations

Modern water supply systems increasingly integrate advanced treatment technologies, smart monitoring networks, and decentralized distribution models to enhance efficiency and reduce resource losses. Innovations such as membrane filtration, smart metering, leak detection systems, and renewable energy integration are improving water quality and reducing operational footprints. These advancements not only enhance system reliability but also support water conservation strategies, energy efficiency, and long-term infrastructure resilience under climate variability.

Transformative Approaches in Wastewater Treatment

Wastewater treatment is evolving from a disposal-oriented process to a resource recovery platform. Advanced biological treatment processes, nutrient recovery technologies, and energy-positive treatment plants exemplify the transition toward circular water economies. Civil engineers are at the forefront of designing systems that recover water, energy, and valuable by-products, thereby reducing environmental discharge impacts while contributing to sustainable resource cycles. Such innovations significantly align wastewater management with broader sustainability objectives.

Sustainable Stormwater Management and Urban Resilience

Stormwater management has shifted from traditional drainage-based approaches to nature-based and low-impact development strategies. Green infrastructure solutions—such as permeable pavements, bioswales, retention ponds, and green roofs—mitigate flooding risks while enhancing groundwater recharge and urban biodiversity. These approaches strengthen climate adaptation capacity and reduce pollutant loads entering natural water bodies. The integration of ecological design principles within civil engineering practices is critical for achieving resilient and environmentally harmonious urban systems.

Barriers to Implementation and Strategic Solutions

Despite technological progress, widespread adoption of sustainable water management solutions faces financial, regulatory, technological, and societal challenges. High capital investment costs, outdated policies, limited technical expertise, and public acceptance issues can hinder implementation. This study identifies strategic pathways to overcome these barriers, including public–private partnerships, performance-based regulations, capacity-building initiatives, policy reform, and community engagement. Addressing these constraints is essential to accelerating the transition toward sustainable water governance frameworks.

Integrating Theory, Practice, and Policy for Future Sustainability

By combining theoretical sustainability frameworks with empirical case studies, this research underscores the necessity of interdisciplinary collaboration between academia, industry, and policymakers. Civil engineering innovation must be supported by evidence-based policy refinement and practical implementation strategies to ensure scalable impact. The study aims to inspire continued academic inquiry, technological development, and policy evolution, fostering a sustainable, efficient, and equitable water resource management paradigm that meets present and future global demands.

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#InfrastructureInnovation

#EnvironmentalEngineering

#SmartWater

#CircularEconomy

#SustainableCities

#ResilientInfrastructure

#WaterConservation

#EngineeringForSustainability

#PolicyAndInfrastructure

#ClimateAdaptation

#GlobalSustainability

 

FLUORINATED SILANE–MODIFIED POLYSILAZANE SUPERHYDROPHOBIC COATING WITH ENHANCED MECHANICAL AND ENVIRONMENTAL DURABILITY

Superhydrophobic coatings have gained increasing attention in structural engineering due to their ability to provide water repellency, anti-fouling performance, and surface protection against environmental degradation. However, achieving both high hydrophobicity and long-term durability remains a significant challenge. This study presents a novel fluorinated silane-modified organic polysilazane coating system designed to deliver superior water repellency, mechanical robustness, and environmental stability through a scalable and cost-effective fabrication approach.

Synthesis of Fluorinated Silane Coupling Agent

A fluorinated silane coupling agent was synthesized via hydrosilylation between 2-(perfluorohexyl)ethyl methacrylate and trimethoxysilane. The incorporation of perfluoroalkyl functional groups provides low surface energy, which is essential for achieving superhydrophobic behavior. This synthesized coupling agent was subsequently integrated into an organic polysilazane matrix, enhancing interfacial bonding and improving compatibility between the polymeric network and inorganic fillers.

Construction of Hierarchical Micro–Nano Surface Structure

To further amplify hydrophobic performance, micro–nano SiO₂ particles were introduced to form a hierarchical rough surface structure. The dual-scale roughness, combined with the low surface energy fluorinated silane component, enables the formation of a Cassie–Baxter wetting state. A simple spraying technique was employed to fabricate the coating, allowing practical application on diverse substrates including glass, metal, and concrete, thereby demonstrating strong versatility for structural applications.

Superhydrophobic Performance Evaluation

The optimized coating achieved a water contact angle (WCA) of 156.3° and a sliding angle of 5.6°, confirming its superhydrophobic characteristics. The high contact angle indicates excellent water repellency, while the low sliding angle reflects minimal adhesion between water droplets and the coated surface. These properties enable efficient water shedding and reduced moisture accumulation, which are critical for corrosion resistance and durability in structural environments.

Mechanical Robustness and Environmental Stability

Mechanical durability was assessed through repeated tape-peeling tests and sandpaper abrasion under controlled loading conditions. Even after 12 tape-peeling cycles or 360 cm abrasion under a 50 g load, the coating maintained a WCA above 150°, demonstrating strong adhesion and wear resistance under the tested conditions. Furthermore, exposure to alkaline solutions, saline environments, and UV radiation did not significantly compromise hydrophobic performance, indicating good chemical and photostability for outdoor structural use.

Self-Cleaning Capability and Structural Applications

The coating exhibited effective self-cleaning behavior against pigments and common liquids, allowing contaminants to be easily removed by rolling water droplets. This functionality reduces maintenance requirements and enhances surface longevity. Overall, the developed fluorinated silane–polysilazane superhydrophobic coating provides a simple, low-cost, and scalable strategy for protective applications in civil infrastructure, offering promising potential for waterproofing, anti-corrosion, and self-cleaning structural surfaces.

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#CorrosionProtection
#ConcreteProtection
#AdvancedMaterials
#CivilEngineeringMaterials
#HydrophobicCoatings
#SurfaceModification
#DurableCoatings
#SustainableConstruction
#Nanotechnology
#StructuralProtection
#EngineeringInnovation

NOVEL SELF-POWERED SENSOR (NSPS) FOR INTELLIGENT STRUCTURAL HEALTH MONITORING OF CIVIL INFRASTRUCTURE

 

Structural Health Monitoring (SHM) plays a critical role in ensuring the safety, durability, and serviceability of civil infrastructure such as bridges, buildings, and transportation systems. Conventional monitoring systems often depend on external power supplies and wired data transmission, limiting their scalability and long-term reliability. This study introduces a Novel Self-Powered Sensor (NSPS) specifically designed for civil structures, integrating self-energy harvesting, low-power wireless communication, and intelligent sensing capabilities. The proposed system addresses the limitations of traditional SHM by enabling sustainable, long-term, and autonomous infrastructure monitoring.

System Architecture and Core Technologies

The NSPS integrates three major technological components: environmental energy harvesting, ultra-low-power wireless data transmission, and intelligent sensing modules. The energy harvesting unit captures ambient environmental energy—such as vibration, solar, or thermal energy—and converts it into usable electrical power. The low-power wireless transmission system enables large-scale deployment across infrastructure networks without extensive cabling. Intelligent sensing algorithms process structural performance data efficiently, ensuring accurate detection of stress, deformation, and environmental variations under complex operational conditions.

Energy Harvesting Optimization and Power Management

A central innovation of the NSPS lies in its self-powered functionality. By fine-tuning the sensor design, optimal energy conversion efficiency is achieved from the harvesting unit, ensuring continuous operation even under variable environmental conditions. Advanced power management strategies regulate energy storage, consumption, and transmission cycles to maintain stable performance. This optimization enables the sensor to operate over extended periods without battery replacement, significantly reducing maintenance costs and enhancing the sustainability of monitoring systems.

Wireless Communication and Large-Scale Deployment

The NSPS employs large-scale, low-power wireless data transmission protocols that facilitate real-time structural performance monitoring across extensive infrastructure networks. This approach reduces installation complexity and allows flexible sensor placement in remote or hard-to-access areas. Compared to conventional wired systems, the wireless architecture improves coverage, scalability, and data accessibility, supporting integrated monitoring platforms for smart infrastructure management.

Bridge Case Study and Monitoring Strategy Development

To validate the practicality and effectiveness of the NSPS, a case study is conducted on an operational bridge structure. A monitoring scheme is developed based on the structural principles and load-bearing characteristics of the bridge. The sensor deployment strategy considers key stress zones, dynamic load responses, and environmental exposure conditions. Field testing demonstrates the system’s reliability in real-world scenarios, confirming its ability to continuously collect and transmit high-quality data while maintaining energy autonomy.

Sustainability, Performance Evaluation, and Future Applications

When compared to traditional structural monitoring techniques, the NSPS demonstrates significant improvements in sustainability, operational efficiency, and long-term reliability. The elimination of frequent battery replacement and reduced wiring requirements contribute to lower lifecycle costs and environmental impact. This innovative self-powered monitoring solution lays a strong foundation for future advancements in intelligent transportation systems and smart infrastructure. Further research may focus on multi-energy harvesting integration, AI-based damage prediction, and large-scale implementation across diverse civil engineering applications.

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#InfrastructureSafety
#SmartBridges
#StructuralMonitoring
#IoTSensors
#EngineeringResearch
#DigitalInfrastructure
#ResilientStructures
#TransportationEngineering
#SHMTechnology
#GreenEngineering

NANO-ENGINEERED NA-BENTONITE THIN FILM MEMBRANES FOR SUSTAINABLE WATERPROOFING OF CIVIL STRUCTURES

Waterproofing remains a critical challenge in the durability and service life of concrete infrastructure, particularly in environments exposed to moisture ingress and shrinkage-induced cracking. Conventional bentonite-based systems, while effective, often require high material consumption and may exhibit limitations in performance consistency. This study explores the development of nano-Na-bentonite derived from Egyptian bentonitic clay through solvothermal (NBS) and precipitation (NBP) synthesis routes. By leveraging nano-scale engineering, the research aims to enhance swelling behavior, hydrophobicity, and crack-sealing efficiency, ultimately offering a sustainable and high-performance alternative for waterproofing civil structures.

Material Preparation and Nano-Modification Techniques

The starting Egyptian bentonite was subjected to activation and purification processes to ensure the removal of impurities and optimization of montmorillonite content prior to nano-modification. Two synthesis approaches—solvothermal (NBS) and precipitation (NBP)—were employed to achieve nano-scale refinement. These processes facilitated controlled crystallite formation, yielding particle sizes of approximately 10 nm for NBS and 50 nm for NBP. The comparative evaluation of these methods provides insights into how synthesis pathways influence structural refinement, morphology, and functional performance in waterproofing membranes.

Structural, Chemical, and Thermal Characterization

Comprehensive characterization techniques were utilized to investigate the physicochemical properties of NBS and NBP. X-ray diffraction (XRD) confirmed the preservation of montmorillonite as the dominant mineral phase, while X-ray fluorescence (XRF) validated the retention of essential chemical constituents. Fourier transform infrared spectroscopy (FTIR) identified characteristic functional groups associated with clay minerals, and TGA/DTA analyses demonstrated thermal stability and moisture-related mass changes. The nano-size effect was evident in the enhanced swelling capacities of 16.3 g/mm for NBS and 12 g/mm for NBP, significantly exceeding that of purified bentonite, thereby substantiating the influence of nano-engineering on performance enhancement.

Morphological Features and Swelling Behavior

Scanning electron microscopy (SEM) revealed that both NBS and NBP exhibit continuous wire-like morphologies with spherical and platelet nanostructures, contributing to improved surface area and interaction with water molecules. Micro-scale swelling measurements demonstrated remarkable volumetric expansion upon hydration, facilitating the formation of an impermeable gel layer. The reduced crystallite size in NBS particularly enhanced swelling kinetics, while NBP exhibited more uniform crack-sealing behavior. These morphological and swelling characteristics play a pivotal role in preventing water penetration and improving long-term waterproofing efficiency.

Hydrophobicity and Water Resistance Performance

Prototype thin film membranes fabricated from NBS and NBP were evaluated using water droplet contact angle analysis. Both membranes demonstrated excellent hydrophobic behavior, with contact angles ranging from 90° to 105° and low spreading coefficients between –81 and –86 mN/m. Notably, the membranes withstood 250–300 water droplets (1–1.2 mm height) without penetration, forming a stable protective gel layer on the surface. These findings confirm the enhanced water-repellent characteristics imparted by nano-scale modification and underscore their suitability for high-performance waterproofing systems.

Crack Morphology Analysis and Application Potential

Post-shrinkage crack morphology was assessed using USB digital microscopy to determine the crack-sealing efficiency of the membranes. The NBP membrane exhibited significantly reduced crack widths (0.09–0.18 mm) compared to NBS (0.22–0.58 mm), indicating superior crack mitigation capability. This suggests that precipitation-synthesized nano-bentonite may offer enhanced structural compatibility and sealing efficiency in concrete substrates. Overall, the performance of NBS and NBP thin film membranes highlights their potential as sustainable, low-consumption alternatives to conventional bentonite systems. Future investigations should focus on long-term durability, environmental exposure testing, and large-scale field implementation to validate their practical applicability in civil infrastructure.

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#ThinFilmMembrane

#HydrophobicCoatings

#SwellingClay

#StructuralEngineering

#BuildingMaterials

#InfrastructureInnovation

#MaterialCharacterization

#GreenConstruction

#AdvancedMaterials

#CrackSealingTechnology

#GeotechnicalEngineering

#SustainableInfrastructure

Neural Backstepping Output-Constrained Control for Flexible Civil Aircraft Overload Tracking

Ride quality and flight safety are critical performance indicators for flexible civil aircraft, particularly under atmospheric disturbances such as gusts and turbulence. Normal overload tracking must satisfy stringent comfort and safety standards defined by ISO 2631-1 and MIL-F-9490D. This paper proposes a neural backstepping output-constrained control strategy to ensure accurate overload tracking while strictly respecting ride quality constraints.

Normal Overload Constraints and Control Objectives

The normal overload constraint arises from the need to limit excessive accelerations that degrade passenger comfort and structural integrity. To explicitly address these constraints, the control design incorporates an integral barrier Lyapunov function (IBLF), which guarantees that the overload response remains within a predefined safe interval throughout system operation.

Neural Backstepping Control Framework

A backstepping-based control architecture is developed for the flexible aircraft model, enabling systematic handling of nonlinear dynamics and output constraints. The IBLF-based control laws ensure constraint satisfaction while maintaining stable overload tracking performance, even in the presence of flexible-body effects inherent in civil aircraft structures.

Handling Model Uncertainty and External Disturbances

To address modeling uncertainties and unknown external disturbances, neural networks are embedded within the control framework to approximate uncertain nonlinearities. In parallel, a disturbance observer is employed to estimate and compensate for external disturbances. A composite learning strategy is further introduced to enhance neural network learning efficiency and convergence accuracy.

Stability and Constraint Satisfaction Analysis

Lyapunov stability theory is used to rigorously prove the uniformly ultimate boundedness of all closed-loop system signals. The analysis also confirms that the normal overload remains strictly within the prescribed bounds imposed by ride quality requirements, ensuring both theoretical soundness and practical reliability of the proposed controller.

Simulation and Hardware-in-the-Loop Validation

Numerical simulations and hardware-in-the-loop (HIL) experiments were conducted under typical discrete gusts and atmospheric turbulence conditions. Results demonstrate that the proposed controller significantly improves ride quality, effectively suppresses overload fluctuations, and consistently maintains the normal overload within the predefined safety interval, confirming its suitability for real-world civil aircraft applications.

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#AerospaceEngineering

#FlexibleAircraft

#NonlinearControl

#LyapunovStability

#DisturbanceObserver

#CompositeLearning

#HILSimulation

#AircraftDynamics

#ControlSystems

#AviationSafety

#IntelligentControl

#EngineeringResearch

#AdvancedFlightControl

 

Intelligent Infrastructure Crack Detection Using MSEDBO-Optimized Deep Learning

 

Infrastructure surface crack detection is a vital task in structural health monitoring, directly influencing the safety, durability, and serviceability of civil engineering assets. Although deep learning methods have achieved notable success in automated crack detection, their performance is often constrained by inefficient hyperparameter tuning, susceptibility to local optima, and suboptimal feature extraction. This study addresses these limitations by proposing an intelligent optimization-driven crack detection framework.

Limitations of Conventional Deep Learning-Based Crack Detection

Traditional deep learning models rely heavily on manual or heuristic-based hyperparameter selection, which can lead to unstable training outcomes and reduced generalization performance. Moreover, commonly used optimization techniques may become trapped in local optima, resulting in inaccurate crack localization and increased false positive rates, particularly when dealing with complex backgrounds and diverse infrastructure materials.

Multi-Strategy Enhanced Dung Beetle Optimizer (MSEDBO)

The proposed framework integrates a Multi-Strategy Enhanced Dung Beetle Optimizer (MSEDBO) to systematically optimize critical parameters within the crack detection pipeline. MSEDBO incorporates Latin Hypercube Sampling with elite population initialization, an improved sigmoid-based nonlinear control factor, sine–cosine algorithm integration, and multi-population mutation strategies. These enhancements collectively strengthen global exploration and local exploitation capabilities.

Integration with Deep Learning Models

By embedding MSEDBO into deep learning-based crack detection models, the framework enables adaptive optimization of network parameters and feature extraction processes. This synergy improves convergence behavior, enhances robustness against local optima, and ensures efficient learning across varying crack patterns and surface conditions in civil infrastructure.

Experimental Validation and Benchmark Datasets

The proposed approach was validated using multiple benchmark datasets, including CrackTree200, CFD, GAPs, and SDNET2018, covering a wide range of materials such as concrete pavements, asphalt roads, and bridge surfaces. Comparative experiments demonstrate that the MSEDBO-optimized framework consistently outperforms conventional optimization algorithms and baseline deep learning models.

Performance Gains and Practical Implications

Results show significant improvements, including an 8.7% increase in detection accuracy, a 12.3% improvement in precision, and a 15.6% reduction in false positive rates. The framework maintains computational efficiency while effectively avoiding local optima, making it well suited for real-world deployment. This research advances intelligent infrastructure monitoring by providing a robust optimization strategy to enhance the reliability and accuracy of automated crack detection systems.

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#AIinCivilEngineering

#AutomatedInspection

#ConcreteCracks

#RoadSurfaceMonitoring

#BridgeInspection

#MachineLearning

#EngineeringOptimization

#DigitalInfrastructure

#SustainableInfrastructure

#CivilEngineeringResearch