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

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

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

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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 hea...
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MULTIFUNCTIONAL AUXETIC METAMATERIALS: DESIGN STRATEGIES AND MULTIPHYSICS COUPLING MECHANISMS

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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 configurat...
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GEOMETRIC FEATURE–BASED IDENTIFICATION OF ROCK MASS DISCONTINUITIES USING ADAPTIVE HIERARCHICAL CLUSTERING

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Discontinuities such as joints, fractures, and bedding planes play a decisive role in controlling the stability and safety of underground engineering structures. Accurate identification and grouping of these discontinuities are essential for tunnel excavation, slope stabilization, and foundation design. Conventional identification methods primarily depend on normal vector estimation combined with directional clustering algorithms. However, these approaches often suffer from reduced accuracy under uneven orientation density, omission of critical discontinuities, and the need for manual parameter tuning. To address these limitations, this study proposes a novel discontinuity identification framework based on geometric feature analysis and adaptive statistical clustering. Limitations of Conventional Directional Clustering Methods Traditional approaches rely heavily on estimating surface normals from point cloud data and grouping them via clustering techniques such as k-means or density-...
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PHYSICS-REGULARIZED DEEP LEARNING FOR INTER-FLOOR NOISE PREDICTION IN MULTI-FAMILY RESIDENTIAL BUILDINGS

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  Inter-floor noise in multi-family residential buildings represents a persistent structural-acoustic problem, particularly in densely populated urban environments. Excessive impact noise transmission not only contributes to occupant discomfort and health deterioration but also triggers legal disputes and regulatory challenges. Traditional acoustic prediction methods often rely on simplified empirical equations, while purely data-driven deep learning models lack physical interpretability and engineering reliability. Notably, prior studies have reported that 34–71% of predictions from conventional AI models violate fundamental acoustic laws. To address these shortcomings, this study proposes a physics-regularized multi-modal deep learning framework that integrates structural mechanics and acoustic principles into predictive modeling. Structural-Acoustic Theoretical Foundations The framework embeds governing physical principles of sound transmission in concrete floor systems direc...
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THE IMPACT OF GENERATIVE AI ON CIVIL ENGINEERING EDUCATION AND PROFESSIONAL PRACTICE

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The rapid advancement of Generative AI (GenAI) is reshaping both educational and professional environments. From automated drafting to intelligent problem-solving support, AI-powered tools are enhancing productivity and enabling personalized learning experiences. However, while GenAI is widely recognized for improving efficiency, its influence on the learning process among civil engineering students and industry professionals remains relatively underexplored. This study bridges that gap by examining how both groups perceive GenAI adoption, focusing on its impact on efficiency, problem-solving, motivation, teamwork, and future potential. Research Methodology The study employed a mixed-method approach: Quantitative analysis: Likert-scale survey responses Qualitative analysis: Thematic evaluation of open-ended feedback Participants included civil engineering students and practicing professionals, allowing a cross-generational and cross-context comparison of perceptions. Key Find...
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