International Research Award on Civil and Environmental Engineering

Quantum computing is anticipated to revolutionize problem-solving by addressing computational challenges that are currently intractable for classical computers. While large-scale, fault-tolerant quantum computers are still under development, quantum-inspired computing techniques have already demonstrated practical advantages using classical hardware. Despite this potential, applications of both quantum and quantum-inspired computing within civil engineering remain limited. This study aims to establish a foundational understanding and encourage future exploration in this emerging research domain. Fundamental Principles of Quantum Computing Quantum computing operates on principles such as superposition, entanglement, and quantum interference, enabling parallel exploration of vast solution spaces. These properties make quantum algorithms particularly suited for optimization, simulation, and combinatorial problems that frequently arise in civil engineering, including structural optimiza...

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...

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...

  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...

  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 resea...