Heat Pipe Technology for Energy-Efficient Building Systems

Building energy consumption and greenhouse gas emissions pose critical challenges to global sustainability goals. As buildings account for a substantial share of total energy use, enhancing their energy efficiency is a research priority in civil and architectural engineering. Heat pipe technology, known for its high thermal conductivity and passive heat transfer capability, has emerged as a promising solution for improving the performance of building energy systems. This review focuses on recent research progress and applications of heat pipes in building systems.

Heat Pipes in Solar Water Heating Systems

In solar water heating systems, heat pipes are primarily employed to enhance heat collection and transfer efficiency. Research efforts have concentrated on optimizing thermal performance under varying solar radiation and climatic conditions. Advanced heat pipe configurations and working fluid selection have been shown to significantly improve system efficiency, enabling more stable and effective solar energy utilization in residential and commercial buildings.

Applications in HVAC Systems

Heat pipe technology has been widely studied in heating, ventilation, and air-conditioning (HVAC) systems to improve the coefficient of performance (COP) and enhance dehumidification capacity. By enabling efficient heat recovery and latent heat transfer, heat pipes contribute to reduced energy demand and improved indoor thermal comfort. These characteristics make them particularly valuable in energy-intensive HVAC operations.

Integration into Building Envelopes

The incorporation of heat pipes into building envelopes represents an innovative approach to reducing building heat loss and managing solar gains. Embedded and solar-driven heat pipe designs enable passive heat regulation, contributing to lower heating and cooling loads. Research indicates that such integrations can achieve notable energy savings while maintaining structural and architectural compatibility.

Performance Benefits and Technical Challenges

Current studies demonstrate that heat pipes can improve heat collection efficiency, increase COP values, and reduce overall building energy losses. However, several challenges hinder widespread adoption, including low-temperature freezing risks, adaptability to fluctuating thermal loads, interface thermal resistance, high initial costs, and maintenance requirements. Addressing these limitations is essential for reliable long-term performance.

Future Research Directions and Development Strategies

Future research should emphasize optimizing working fluids, refining structural designs, and integrating intelligent control strategies to enhance adaptability and reliability. Particular attention is needed for low-temperature operation, dynamic load adjustment, and full life-cycle performance assessment. Advancing these areas will be crucial for enabling large-scale deployment of heat pipe technology in sustainable building systems.

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Transformer-Based Intelligent Defect Detection in Civil Infrastructure

Detecting structural and functional defects in large-scale civil infrastructure during operational stages is critical for ensuring safety, serviceability, and sustainability. Traditional inspection methods are often labor-intensive, subjective, and inefficient for large-scale systems. With the rapid evolution of artificial intelligence, deep learning has emerged as a powerful tool for intelligent defect detection. Recently, Transformer-based self-attention models have gained prominence as effective alternatives to convolutional neural networks (CNNs), offering superior capability in modeling long-range dependencies and parallel computation. This research-oriented survey systematically explores the integration of Transformer architectures into civil engineering defect detection applications.

Evolution from CNNs to Transformer Models

Conventional CNN-based approaches have demonstrated strong performance in localized feature extraction for defect detection tasks; however, their limited receptive fields restrict the modeling of global structural relationships. Transformers overcome these limitations through self-attention mechanisms that capture global contextual information across large-scale datasets. This paradigm shift has motivated civil engineering researchers to adopt Transformer models for infrastructure monitoring, enabling more comprehensive understanding of spatial and temporal defect patterns.

Transformer Architectures for Engineering Defect Detection

This survey reviews more than 40 Transformer-based engineering defect detection algorithms, highlighting key architectural variants such as Vision Transformers (ViT), hybrid CNN-Transformer models, and hierarchical attention frameworks. These architectures are tailored to handle high-resolution images, sensor data, and multimodal inputs commonly encountered in civil infrastructure monitoring. The adaptability of Transformer models allows effective feature learning across complex structural geometries and diverse defect manifestations.

Application Scenarios in Civil Infrastructure

Transformer-based defect detection methods have been extensively applied to critical civil infrastructure systems, particularly roadways, tunnels, and bridges. In roadway monitoring, Transformers enable accurate detection of cracks, potholes, and surface degradation. Tunnel inspection applications benefit from long-range dependency modeling in low-light and complex environments, while bridge monitoring leverages attention mechanisms to identify structural anomalies across large spans and interconnected components.

Challenges and Limitations

Despite their advantages, Transformer-based approaches face several challenges in civil engineering applications. These include high computational costs, large data requirements, limited labeled datasets, and difficulties in real-time deployment. Additionally, domain adaptation across different infrastructure types and environmental conditions remains a significant research challenge. Addressing these issues is essential for practical, scalable adoption in real-world infrastructure systems.

Future Research Directions

Future development of Transformer-based intelligent detection systems should focus on lightweight architectures, self-supervised learning, multimodal data fusion, and domain-specific model optimization. Integrating Transformers with digital twins, edge computing, and real-time monitoring systems holds promise for next-generation smart infrastructure management. This survey provides foundational insights and reference strategies to guide researchers in advancing intelligent defect detection within civil engineering.

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Connectivity-Based Seismic Design Strategy for Bridge Networks Through Fragility Correlation Control

 

Seismic resilience of bridge networks is a critical concern for transportation infrastructure in earthquake-prone regions. Traditional seismic design approaches often focus on individual bridges without adequately accounting for network-level connectivity and interdependencies. This research context emphasizes a connectivity-based seismic design strategy that explicitly considers the correlation of seismic fragility among individual bridges. By integrating network theory with seismic risk assessment, the approach aims to enhance overall system performance, reduce cascading failures, and ensure post-earthquake functionality of critical transportation routes.

Bridge Network Connectivity and System-Level Performance

Bridge networks function as interconnected systems where the failure of a single critical bridge can significantly disrupt regional mobility. This topic focuses on how network connectivity—such as node importance, redundancy, and traffic flow dependency—affects seismic performance. Understanding connectivity metrics enables designers to identify critical bridges whose damage disproportionately impacts network efficiency, thereby prioritizing them in seismic design and retrofit strategies.

Fragility Modeling of Individual Bridges

Accurate seismic fragility models are fundamental to assessing damage probabilities under varying earthquake intensities. This section addresses the development and application of bridge-specific fragility curves, considering structural typology, material properties, aging effects, and soil–structure interaction. These models serve as the foundation for evaluating both independent and correlated damage states across the network.

Correlation of Seismic Fragility Among Bridges

Bridges within a network are often exposed to similar seismic hazards and share common design practices, leading to correlated damage patterns. This topic explores the sources and modeling of fragility correlation, including spatial proximity, ground motion similarity, and construction uniformity. Controlling or strategically managing these correlations is essential to preventing simultaneous failures that can severely compromise network resilience.

Connectivity-Based Design and Control Strategies

This section discusses design strategies that integrate connectivity analysis with fragility correlation control. Approaches such as targeted strengthening, diversification of structural systems, and adaptive design standards are highlighted as means to reduce correlated failures. By intentionally varying fragility characteristics among critical bridges, the network can achieve improved robustness and controlled damage distribution during seismic events.

Implications for Seismic Resilience and Infrastructure Planning

The final topic examines the broader implications of connectivity-based seismic design for infrastructure resilience and policy-making. Incorporating fragility correlation control into planning supports more reliable emergency response, faster recovery, and sustainable investment decisions. This research context underscores the shift from component-level safety to holistic, network-oriented seismic design frameworks for modern bridge systems.

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Spectral Analysis of Surface Waves (SASW) in Civil Engineering: A Systematic Review of Applications and Methodologies

 

The Spectral Analysis of Surface Waves (SASW) method is a non-destructive geophysical seismic testing technique extensively used in civil engineering for subsurface characterization. By analyzing the dispersive properties of surface waves, SASW enables the evaluation of soil and material stiffness profiles with depth. This systematic review synthesizes peer-reviewed studies retrieved from Web of Science and Scopus, following the PRISMA framework, to assess the scope, reliability, and evolving role of SASW in modern engineering practice.

Methodological Framework and Literature Selection

A rigorous identification, screening, and eligibility assessment process was conducted in accordance with PRISMA guidelines. From an initial pool of publications, 37 high-quality studies were selected for detailed analysis. The reviewed literature reflects methodological advancements in data acquisition, signal processing, and inversion techniques, highlighting the robustness of SASW as a research and practical tool for subsurface investigations.

Geotechnical Site Characterization Applications

SASW has been widely applied in geotechnical engineering to determine shear wave velocity profiles, stratification, and dynamic soil properties. These applications support site classification, ground improvement assessment, and foundation design. The reviewed studies demonstrate that SASW offers reliable subsurface insights while minimizing disturbance, making it suitable for urban and environmentally sensitive sites.

Foundation and Structural Engineering Assessment

In foundation and structural analysis, SASW is used to evaluate stiffness degradation, detect anomalies beneath existing structures, and assess soil–structure interaction effects. The method’s ability to investigate subsurface conditions without excavation enhances its value in the assessment of aging infrastructure, retrofitting strategies, and performance monitoring under operational loads.

Seismic Analysis and Microzonation Studies

SASW plays a significant role in seismic hazard analysis and urban microzonation by providing essential dynamic soil parameters. The reviewed research highlights its effectiveness in estimating site response, resonance characteristics, and seismic vulnerability. These contributions are critical for earthquake-resilient design and regional risk mitigation planning.

Infrastructure and Specialized Engineering Applications

Beyond traditional geotechnical uses, SASW has been applied to pavement evaluation, railway track assessment, and specialized cases such as landfill characterization and historical structure analysis. These applications underscore the versatility of SASW in supporting sustainable infrastructure development, condition assessment, and maintenance planning across diverse civil engineering domains.

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