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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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REAL-TIME ROCK MASS QUALITY PREDICTION USING ONLINE TRANSFER LEARNING IN TBM TUNNELLING

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  Accurate and real-time perception of rock mass quality during Tunnel Boring Machine (TBM) excavation is essential for ensuring operational safety, optimizing support measures, and minimizing construction risks. This challenge becomes even more critical in newly constructed tunnels, where limited prior geological investigations restrict the ability to promptly evaluate surrounding rock conditions. To address this issue, this study proposes a physical feature–shared online transfer learning framework that enables cross-engineering rock mass quality prediction using monitored data from previously completed tunnels. Framework Overview The proposed framework integrates three core components: 1️⃣ Online Data Stream Processor A real-time data acquisition and processing module was developed to handle continuous TBM monitoring data. This processor performs: Real-time data collection Data segmentation Stream-based feature extraction Importantly, it extracts physical shared...
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MULTISCALE FIBER-REINFORCED SUSTAINABLE CONCRETE WITH HIGH-VOLUME FLY ASH AND PORCELAIN AGGREGATE

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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...
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