38th Edition of Global Civil Engineering Awards | 28–29 June 2026 #worldresearchawards #civilengineering

 

ADVANCING SUSTAINABLE WATER MANAGEMENT THROUGH CIVIL ENGINEERING INNOVATION

Sustainable water management is a fundamental pillar of global environmental sustainability and resource conservation. With increasing water demand driven by climate change, rapid urbanization, and population growth, the need for innovative and efficient water management strategies has become more urgent than ever. Civil engineering plays a central role in designing, implementing, and optimizing systems that ensure reliable water supply, effective wastewater treatment, and resilient stormwater management. This study critically evaluates how modern engineering approaches contribute to achieving long-term sustainability goals.

Innovations in Water Supply Systems

Recent advancements in water supply systems focus on improving efficiency, reducing losses, and ensuring water quality. Technologies such as smart monitoring systems, leak detection networks, advanced filtration, and desalination are transforming how water is sourced and distributed. Civil engineers are integrating digital tools and data-driven approaches to optimize system performance, minimize wastage, and enhance resilience against climate variability, ensuring sustainable and continuous water availability.

Transformations in Wastewater Treatment

Wastewater treatment has evolved from simple disposal systems to resource recovery platforms. Modern treatment technologies emphasize energy efficiency, nutrient recovery, and water reuse. Biological treatment processes, membrane technologies, and decentralized treatment systems are enabling the recycling of wastewater for agricultural and industrial use. These innovations not only reduce environmental pollution but also contribute to circular economy practices by turning waste into valuable resources.

Sustainable Stormwater Management Strategies

Stormwater management has shifted toward sustainable and nature-based solutions that reduce flooding risks and enhance urban resilience. Green infrastructure approaches, such as permeable pavements, rain gardens, bioswales, and retention systems, help manage runoff while improving groundwater recharge and urban ecosystems. These strategies support climate adaptation and reduce pressure on conventional drainage systems, making cities more resilient to extreme weather events.

Challenges in Implementation and Adoption

Despite technological progress, several barriers hinder the widespread adoption of sustainable water management solutions. Financial constraints, regulatory limitations, technological gaps, and lack of public awareness remain significant challenges. Additionally, integrating new technologies into existing infrastructure requires careful planning and investment. Addressing these challenges requires coordinated efforts among policymakers, engineers, and stakeholders to create supportive frameworks for innovation.

Strategic Pathways for Sustainable Water Management

To overcome these challenges, the study proposes strategic approaches including policy reform, investment in advanced technologies, public–private partnerships, and capacity building. Integrating theoretical frameworks with real-world case studies provides practical insights into effective implementation. By promoting interdisciplinary collaboration and encouraging innovation, civil engineering can lead the transition toward sustainable, efficient, and equitable water resource management systems that meet current and future demands.

#WaterConservation

#EnvironmentalEngineering

#SmartWater

#CircularEconomy

#SustainableCities

#InfrastructureInnovation

#WaterSustainability

#ClimateAdaptation

#EngineeringSolutions

#FutureInfrastructure

#GlobalSustainability

#CivilEngineeringResearch

ERGONOMICS-BASED MANAGEMENT STRATEGIES FOR HIGH-ALTITUDE TUNNEL CONSTRUCTION

High-altitude tunnel construction presents unique physiological and operational challenges due to reduced oxygen availability, extreme environmental conditions, and increased physical strain on workers. These factors significantly elevate construction risks and can negatively impact productivity. This study investigates ergonomics-based construction management strategies aimed at improving worker safety and efficiency by analyzing respiratory metabolism and energy consumption under high-altitude conditions.

Impact of High Altitude on Worker Physiology

Field testing and controlled bicycle power simulation experiments were conducted to evaluate the effects of altitude on respiratory metabolic parameters and energy metabolism rate (EMR). Results indicate that as altitude increases, oxygen intake efficiency decreases, leading to higher physiological stress and energy expenditure. Workers operating in high-altitude environments must therefore exert greater effort to perform the same tasks compared to those at lower elevations.

Energy Metabolism Rate Variation with Altitude

The study reveals a significant increase in EMR with rising altitude. Specifically, EMR values increased from 8–11 kJ/(min·m²) at 2500 m to 10–14 kJ/(min·m²) at 4700 m, indicating a substantial rise in energy demand. The altitude range of 3400–3800 m was identified as a critical physiological adaptation zone, where workers begin to experience noticeable metabolic strain. At elevations above 4000 m, task-specific metabolic rates (MET) vary significantly, highlighting the growing influence of individual physiological differences.

Development of Work Duration Control Standards

Based on observed metabolic changes, the study establishes scientifically grounded work duration control standards for high-altitude tunnel construction. These standards are designed to prevent excessive fatigue, reduce health risks, and maintain consistent productivity. By aligning work-rest cycles with physiological limits, construction managers can better protect workers from altitude-related stress and performance decline.

Targeted Oxygen Supply Strategy

A novel “3 regions + 5 oxygen supply measures” strategy was proposed to optimize oxygen delivery in high-altitude tunnel environments. This approach categorizes work zones based on oxygen demand and implements targeted oxygen supply interventions tailored to each region. The strategy ensures efficient oxygen utilization, reduces unnecessary resource consumption, and enhances worker adaptability to altitude conditions.

Performance Improvements and Practical Implications

Implementation of the proposed ergonomics-based management strategies resulted in significant improvements. During a six-week monitoring period, worker efficiency increased by 13.6% to 28.6%, while cases requiring medical treatment due to oxygen deficiency dropped from 10–15 cases per week to zero. These outcomes demonstrate the effectiveness of integrating physiological insights into construction management practices. The findings provide a strong theoretical and practical foundation for improving safety and efficiency in high-altitude tunnel projects and can be extended to other high-altitude engineering applications.

#OccupationalHealth

#SmartConstruction

#ProductivityImprovement

#HumanFactorsEngineering

#EngineeringInnovation

#SustainableConstruction

#WorkplaceSafety

#HighAltitudeWork

#CivilEngineeringResearch

#ConstructionEfficiency

AGMAMBA: A LIGHTWEIGHT ADAPTIVE GUIDED STATE SPACE MODEL FOR PIXEL-LEVEL CRACK SEGMENTATION IN CIVIL INFRASTRUCTURE

Accurate pixel-level segmentation of crack regions is essential for the inspection and maintenance of civil infrastructure such as bridges, pavements, and buildings. Early detection of structural cracks enables timely maintenance actions and reduces the risk of structural failure. However, existing segmentation approaches often struggle to simultaneously capture fine crack textures and suppress complex background noise, which can significantly degrade detection accuracy. Additionally, many high-performing models require large computational resources, limiting their applicability in real-time infrastructure monitoring systems.

Challenges in Existing Crack Segmentation Methods

Current deep learning-based crack detection methods frequently encounter two primary challenges. First, cracks typically exhibit thin, irregular, and discontinuous patterns, making it difficult for models to dynamically capture their texture and morphology. Second, background elements such as shadows, stains, and surface roughness can easily be misidentified as cracks. These challenges often lead to reduced segmentation accuracy or excessive computational complexity when attempting to improve feature extraction.

AGMamba Architecture and Lightweight Design

To overcome these limitations, this study introduces AGMamba, a lightweight adaptive guided state space model designed for efficient crack segmentation. The architecture focuses on capturing crack texture features while minimizing redundant background information. With only 3.40 million parameters and 20.99 GFLOPs, AGMamba achieves a strong balance between computational efficiency and segmentation performance, making it suitable for practical civil infrastructure inspection applications.

Crack Perception Module (CPM)

A key component of the proposed model is the Crack Perception Module (CPM), which integrates two complementary mechanisms:

• Adaptive Guided Scanning Strategy (AGSS2D) – prioritizes crack regions during feature scanning to improve the efficiency of texture extraction.

• Selective Key Clue Modeling (SKCM) – selectively aggregates information from critical crack edges and structural features.

Together, these modules allow the network to focus on meaningful crack patterns while reducing the influence of irrelevant background features.

Frequency-Domain Feature Perception

To further enhance segmentation performance, the model incorporates a High-Low Frequency Feature Perception (HLFP) strategy and a Frequency-Domain Segmentation Head (FDSH). These components analyze differences between high-frequency crack textures and low-frequency background patterns. By leveraging frequency-domain information, the framework effectively suppresses background interference and improves crack boundary detection accuracy.

Experimental Results and Performance Evaluation

Extensive experiments were conducted on four public crack datasets, demonstrating that AGMamba consistently outperforms existing state-of-the-art (SOTA) segmentation models. On the Crack500 dataset, the proposed model achieved an F1 score of 0.7622 and an mIoU of 0.7808, representing improvements of 1.41% and 1.21%, respectively, over previous SOTA methods. These results confirm the model’s ability to achieve high segmentation accuracy while maintaining low computational cost, making it highly suitable for automated infrastructure inspection systems.

Global Civil Engineering Awards

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#BridgeInspection
#PavementMonitoring
#AIinCivilEngineering
#StructuralSafety
#AutomatedInspection
#DigitalInfrastructure
#LightweightAI
#EngineeringVision
#CivilEngineeringResearch
#SmartMaintenance

 

HYBRID PKO-XGBOOST MODEL FOR ACCURATE SHEAR STRENGTH PREDICTION OF CONCRETE-FILLED STEEL TUBE COLUMNS

Concrete-filled steel tubes (CFST) are extensively used in modern civil engineering structures because of their excellent load-bearing capacity, ductility, and seismic resistance. The composite interaction between the steel tube and the concrete core significantly enhances structural performance compared to conventional steel or reinforced concrete members. However, existing design standards, including the American Institute of Steel Construction (AISC) and Eurocode 4 provisions, often provide conservative shear strength predictions because they do not fully capture the complex composite mechanisms governing CFST behavior.

Limitations of Conventional Design Methods

Traditional empirical or semi-empirical design equations rely on simplified assumptions about material interaction and stress distribution. As a result, these models often underestimate the actual shear capacity of CFST columns, leading to conservative structural designs and inefficient material usage. Furthermore, such approaches cannot easily adapt to large experimental datasets or capture nonlinear relationships among structural parameters. This limitation motivates the adoption of data-driven predictive models capable of learning complex interactions within structural systems.

Hybrid Machine Learning Framework

To improve prediction accuracy, this study proposes a hybrid modeling approach that integrates Extreme Gradient Boosting (XGBoost) with the Pied Kingfisher Optimizer (PKO). PKO is a nature-inspired optimization algorithm designed to enhance model performance by efficiently tuning hyperparameters. By combining the strong learning capability of XGBoost with PKO's optimization strategy, the hybrid model achieves improved predictive capability and robustness when estimating the shear strength of CFST columns.

Prediction Interval and Error Mitigation Techniques

Beyond point predictions, the study incorporates quantile regression to generate prediction intervals for ultimate shear force, enabling uncertainty quantification in structural predictions. Additionally, the Asymmetric Squared Error Loss (ASEL) function is introduced to reduce the risk of overestimation errors, which are critical in structural safety evaluations. This approach ensures that the predictive model remains both accurate and conservative where necessary, aligning with engineering safety requirements.

Model Performance and Comparative Analysis

Computational results demonstrate that the PKO-XGBoost model significantly outperforms conventional models. The hybrid framework achieves a Mean Absolute Percentage Error (MAPE) of 4.431% and a coefficient of determination (R²) of 0.9925 on the test dataset. Furthermore, the ASEL-PKO-XGBoost variant effectively reduces overestimation errors to 28.26% while maintaining comparable predictive performance. These results confirm the effectiveness of the proposed framework for accurately predicting CFST shear capacity.

Development of Predictive Equation and Practical Tools

In addition to the machine learning model, a new strength equation was derived using a Genetic Algorithm (GA) combined with existing equation-based models. The resulting equation demonstrates improved accuracy (R² = 0.934) compared with traditional design formulas. To facilitate practical implementation, web-based Graphical User Interfaces (GUIs) were also developed, enabling engineers to perform real-time shear strength predictions efficiently. These tools support practical adoption of advanced predictive methods in structural design and engineering practice.

Global Civil Engineering Awards

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#SmartStructuralDesign

#AIinConstruction

#GeneticAlgorithm

#EngineeringPrediction

#StructuralSafety

#DataDrivenEngineering

#SteelConcreteComposite

#EngineeringInnovation

#AdvancedStructuralModels

#DigitalEngineering

 

MICROWAVE SINTERING OF ENGINEERING SPOIL CERAMSITE: MULTI-PHYSICS MODELING AND TEMPERATURE FIELD OPTIMIZATION

Microwave sintering of engineering spoil into ceramsite presents a sustainable alternative to traditional high-temperature kiln processes. By converting construction and excavation waste into lightweight aggregate, this technology supports resource recycling and environmentally responsible construction practices. However, challenges such as temperature nonuniformity and inefficient energy utilization limit its large-scale industrial application. This study addresses these challenges by developing a comprehensive multi-physics modeling framework to analyze the energy conversion process and temperature evolution during microwave sintering.

Multi-Physics Coupled Modeling Framework

A three-dimensional electromagnetic–thermal–radiation coupled model was developed to simulate the microwave sintering process of ceramsite. The model integrates electromagnetic wave propagation, thermal conduction, and radiative heat transfer mechanisms to capture the complex interactions occurring during heating. Experimental validation confirmed the model’s accuracy in predicting temperature distribution and heating behavior, providing a reliable tool for investigating energy transfer and temperature field development in microwave-assisted sintering systems.

Role of Silicon Carbide Susceptor in Hybrid Heating

The introduction of a silicon carbide (SiC) susceptor significantly improves heating efficiency and temperature uniformity. Due to the relatively low dielectric loss of ceramsite materials, direct microwave absorption is limited. The SiC susceptor acts as an auxiliary heating element, converting microwave energy into thermal energy and transferring heat to surrounding ceramsite particles. This hybrid heating mechanism reduces temperature gradients and enhances overall sintering stability.

Temperature Evolution and Heat Transfer Mechanisms

The temperature field evolves through different dominant heat transfer mechanisms during the sintering process. At lower temperatures (below 800 °C), localized microwave-induced hotspots result in uneven heating patterns. As the temperature increases beyond 1000 °C, radiative heat transfer becomes the primary mechanism, promoting more uniform temperature distribution across the material. This transition highlights the importance of considering both electromagnetic and radiative effects when designing microwave sintering systems.

Influence of Particle Size and Microwave Power

Parametric analysis revealed that ceramsite particle size and microwave power significantly influence heating uniformity and energy efficiency. Smaller particles (1 cm) produce more uniform temperature distributions, while larger particles (3 cm) are susceptible to uneven heating due to electric field intensity variations. Regarding power input, lower microwave power improves temperature uniformity but increases energy consumption, whereas higher power reduces energy usage but worsens temperature gradients. A moderate microwave power of 3 kW was identified as the optimal operating condition for balancing energy efficiency and thermal uniformity.

Scale-Up Strategies and Hotspot Mitigation

To address industrial-scale challenges, the study proposed an enclosed susceptor design with multi-layer ceramsite arrangements. Among tested configurations, a two-layer structure achieved optimal heat exchange, reducing the temperature coefficient of variation (COV-T) by 21.1% compared to a single-layer setup. Additionally, rotational heating of the susceptor was introduced to mitigate hotspot formation. This dynamic heat redistribution mechanism significantly improves temperature uniformity, achieving a COV-T value of 0.014 at 1240 °C. Thermal flux analysis indicates that alternating radiative heat exchange between the rotating susceptor and ceramsite particles is the key mechanism behind the enhanced uniformity.

Global Civil Engineering Awards

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#MaterialsEngineering

#CircularEconomy

#LightweightAggregates

#EnergyEfficiency

#CivilEngineeringResearch

#IndustrialSintering

#GreenMaterials

#ConstructionInnovation

#ThermalModeling

MULTIFUNCTIONAL AUXETIC METAMATERIALS: DESIGN STRATEGIES AND MULTIPHYSICS COUPLING MECHANISMS

Auxetic metamaterials, characterized by their negative Poisson’s ratio, represent a rapidly evolving class of engineered materials with unconventional mechanical behavior. Unlike traditional materials that contract laterally when stretched, auxetic structures expand laterally under tensile loading due to their unique geometric deformation mechanisms. This property enables exceptional strain energy redistribution, resulting in enhanced stiffness, strength, and energy absorption capabilities. In recent years, auxetic metamaterials have expanded beyond purely mechanical applications and are increasingly recognized as multifunctional platforms capable of interacting with thermal, acoustic, electrical, magnetic, and optical fields.

Deformation Mechanisms and Structural Behavior

The exceptional performance of auxetic metamaterials originates from their distinct deformation mechanisms, including rotational units, re-entrant cell structures, and chiral geometries. These structural configurations allow strain energy to concentrate and redistribute efficiently within the material, improving resistance to fracture and impact. Such deformation characteristics contribute to higher mechanical stability, superior toughness, and improved energy dissipation compared to conventional materials. Understanding these deformation principles is fundamental to tailoring auxetic materials for advanced engineering applications.

Design Strategies for Multifunctional Auxetic Structures

Recent advances in auxetic metamaterial design rely on innovative strategies that enhance structural performance and enable multifunctional behavior. Key design approaches include geometric reconfiguration of unit cells, instability engineering to trigger controlled deformation modes, hierarchical structuring to combine properties across multiple scales, and multi-material integration to introduce additional functional capabilities. These strategies enable the development of programmable materials whose mechanical responses can be customized for specific engineering requirements.

Multiphysics Coupling and Functional Capabilities

Auxetic metamaterials demonstrate strong interactions with external physical fields, enabling multiphysics coupling effects. Their structural configuration allows them to regulate thermal expansion, absorb and dissipate mechanical impact energy, and manipulate acoustic and elastic wave propagation across wide frequency ranges. These capabilities create opportunities for adaptive materials that can respond dynamically to environmental stimuli, making auxetic structures suitable for advanced sensing, vibration mitigation, and thermal management applications.

Emerging Applications Across Engineering Fields

The multifunctional characteristics of auxetic metamaterials have opened pathways for innovative applications across multiple engineering disciplines. In biomedical engineering, auxetic structures offer improved compatibility with biological tissues and enhanced implant performance. Aerospace and civil infrastructure benefit from their energy absorption and vibration control properties, while soft robotics utilizes their flexible yet resilient structural behavior for adaptive motion systems. These diverse applications highlight the transformative potential of auxetic metamaterials in next-generation technologies.

Challenges and Future Research Directions

Despite significant progress, several challenges remain before auxetic metamaterials can be widely implemented in practical systems. Scalable manufacturing methods, long-term durability under coupled mechanical and environmental loading, and reliable predictive multiphysics modeling are key areas requiring further research. Future advancements are expected to emerge from the integration of topology optimization, advanced fabrication technologies such as additive manufacturing, and system-level experimental validation. Addressing these challenges will accelerate the transition of multifunctional auxetic metamaterials from laboratory concepts to real-world engineering solutions.

Global Civil Engineering Awards

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#AdditiveManufacturing

#AcousticMetamaterials

#ThermalEngineering

#WavePropagation

#EnergyAbsorption

#MaterialScienceResearch

#BiomedicalEngineering

#AerospaceMaterials

#CivilEngineeringInnovation

#SoftRobotics

#NextGenMaterials