Search Results (864)

Planar honeycomb structures, especially biomimetic hexagonal honeycombs, are widely used in energy-absorbing equipment because of their excellent out-of-plane deformation resistance. However, their significant mechanical anisotropy, manifested by the large discrepancy between out-of-plane and in-plane responses, greatly restricts their broader applications. Inspired by spiral-reinforced thin-walled biological tubular systems, such as animal tracheae and plant vessels, this study proposes a biomimetic reinforcement strategy by embedding spiral structures along the thin walls of planar honeycombs. To validate the feasibility of the proposed design, biomimetic honeycomb specimens were fabricated using 3D-printing technology and tested under compression along different loading directions. Furthermore, a numerical model validated against the experiments was developed to reveal the underlying enhancement mechanism. The results demonstrate that the proposed biomimetic honeycomb preserves the favorable out-of-plane performance of the conventional hexagonal honeycomb, while improving the in-plane energy absorption capacity by up to 70%. The biomimetic spiral reinforcements enable more effective load transfer under multidirectional loading, resulting in a more uniform plastic stress distribution over the entire structure and activating a larger deformation region for energy dissipation. The present work provides a bioinspired strategy for developing lightweight energy-absorbing structures for potential applications in aerospace, rail vehicles, marine engineering, and civil structures. Full article

Palm oil waste (POW) is generated during the production of palm oil, and a large quantity of this waste often travels to landfills for disposal. This review aims to provide a comprehensive understanding of the circular economy approach to sustainable engineering and environmental applications of POW, including its generation, disposal concerns, challenges, and prospects. This review provides an overview of the features, composition, and prospective applications of several POWs, including palm oil clinkers (POCs), palm oil fuel ashes (POFAs), palm oil kernel shells (POKSs), and palm oil fibres (POFs). Furthermore, this overview describes the different applications that POW has found, such as sustainable construction materials, renewable energy production, and environmental remediation. Moreover, this review discusses the leaching and risk assessment of POW. The overview also discusses the circular economy implications of using POW. The results showed that while some wastes are reused and recycled, a good quantity are still discarded in environmentally harmful ways. With this overview of a wide circular economy approach to the sustainable use of POW, there will be a rallying call to experts and researchers to identify research gaps that could contribute to the sustainable use of POW. The results of this overview of the sustainable engineering and environmental applications of POW with a circular economy approach indicate that cleaner production technologies and better environmental sustainability of the palm oil industry are feasible through proper waste management, renewable energy generation, resulting in minimal environmental impacts. Furthermore, this analysis will be very useful in providing tools to engineers, environmentalists, and other relevant stakeholders to enable the efficient and sustainable use of POW in the global circular economy. Full article

This paper proposes a novel parametric output-only modal identification method, termed parametric complexity pursuit (PCP), for accurate identification of modal parameters in civil engineering structures. The proposed method extends the complexity pursuit (CP) algorithm through a system representation. Unlike the standard CP approach, which extracts modes individually, the PCP algorithm transforms CP into a parametric formulation that enables global identification of all modal parameters of interest. This parametric framework provides a rigorous theoretical foundation aligned with structural dynamics and explicitly accounts for mutual influences among different modes, thereby enhancing both accuracy and robustness. Numerical experiments on a simulated 3-DOF system under various damping conditions and closely spaced modes demonstrate that PCP maintains consistent identification accuracy across all damping levels, exhibiting true damping-independent performance. In contrast, conventional CP suffers from marked accuracy degradation as damping increases and fails to properly identify the closely spaced modes. Application to the HCT building further confirms the practical effectiveness of PCP, with comparative analysis demonstrating its superiority over existing output-only techniques in modal separation capability and identification accuracy. The proposed PCP method offers a robust and theoretically grounded framework for output-only modal identification, particularly advantageous in challenging scenarios involving high damping and closely spaced modes. Full article

Precast concrete (PC) component production scheduling is essential to the efficiency and reliability of industrialized construction. Although intelligent algorithms have been widely applied in this field, the relationships among research evolution, collaboration patterns, and industrial applicability remain insufficiently understood. To address this issue, this study presents a bibliometric review of 1272 publications indexed in the Web of Science Core Collection from 1990 to 2025. CiteSpace was employed to analyze publication trends, collaboration networks, co-citation structures, keyword co-occurrence, and burst terms. On this basis, a technology adaptability evaluation framework was developed to assess the alignment between algorithmic advances and industrial implementation in terms of dynamic adaptability, verification completeness, and technological generation gap. The results indicate that the field has evolved through four broad stages, from early static optimization to multi-objective coordination, digital twin-enabled dynamic scheduling, and emerging human-centric intelligent autonomous systems. The analysis also shows an increasing convergence of operations research, computer science, and civil engineering. However, a gap remains between academic output and industrial application. Specifically, 32% of the retrieved studies focused on genetic algorithms, whereas only 6% reported full-process industrial validation. In addition, Gen 4.0-related studies showed a technological generation gap of 82.5%, indicating that many frontier technologies have not yet reached broad industrial implementation. The collaboration network further reveals a “high-output, low-synergy” pattern, in which major publishing countries contribute substantially to the literature but exhibit limited cross-institutional integration. This study provides a structured overview of the development of PC component production scheduling research and highlights future directions for digital twin integration, human–robot collaboration, and cross-sector validation platforms. Full article

This review focuses on electromagnetic imaging methods widely used in urban geophysics and civil engineering. The rapid growth of the urban population and the increase in the frequency of extreme events related to climate change make novel approaches to the geophysical monitoring of urban areas and civil infrastructures essential in the context of programs for the sustainability and resilience of cities. In this scenario, there is a growing interest in using ground-based electromagnetic methods to investigate strategic infrastructures such as bridges, tunnels, dam embankments, power plants, energy plants and pipelines in a non-invasive way. The development of cost-effective, user-friendly sensor arrays, robust methodologies for tomographic data inversion, and AI-based and machine learning techniques has rapidly transformed these methods. This review critically analyzes the results relating to the application of ground-based electromagnetic methods in infrastructure monitoring and surveillance over the past 20 years by presenting a selection of best practice examples and studies planned to support programs for the resilience and maintenance of engineering infrastructures. The analysis reveals that these methods are highly effective in addressing a broad spectrum of monitoring issues in view of effective maintenance of civil infrastructures. In fact, these methods are essential for detecting the geometry of buried objects (e.g., bars and voids), enabling the early detection of degradation phenomena, and mapping water infiltration processes inside structures, as well as many other challenging applications. Finally, prospectives for development are identified in terms of using soft robot technologies, miniaturized sensors, and AI-based methods to acquire, process and interpret data as well as to design smart operational guidelines for infrastructure management. Full article

The growing complexity, age, and environmental exposure of civil infrastructure assets—bridges, tunnels, buildings, highways, and dams—have necessitated a transition from reactive or preventive maintenance strategies toward predictive, data-driven systems. The integration of IoT and Digital Twin (DT) technologies provides a transformative paradigm for intelligent monitoring, early fault detection, and real-time lifecycle management. This paper explores the technological convergence of IoT sensor networks, edge-cloud analytics, and digital twin platforms for predictive maintenance in civil and structural engineering. The study presents a multi-layered DT–IoT integration framework designed for infrastructure assets, emphasizing interoperability, cybersecurity, and semantic data synchronization. Key research outcomes include enhanced asset availability, reduced maintenance costs, and improved safety margins. The proposed architecture incorporates sensor-level digital shadows, edge inference modules, and cloud-based analytical twins powered by hybrid machine learning and finite element models. Real-world applications and case studies from smart bridges and intelligent building systems demonstrate prediction accuracies exceeding 90% in identifying early structural fatigue indicators. Ultimately, the results underscore the strategic role of DT–IoT convergence in realizing sustainable, resilient, and self-aware civil infrastructure aligned with Industry 5.0 principles. This study provides a roadmap for digital transformation in asset management, integrating standards such as International Organization for Standardization (ISO) 23247 and ISO 19650 to ensure interoperability and lifecycle traceability. The results reinforce that predictive maintenance through DT and IoT integration is not only technically viable but essential for extending infrastructure lifespan, minimizing unplanned downtime, and achieving carbon-efficient asset operation. Full article

The increasing availability of onboard sensors and digital monitoring platforms has enabled the continuous acquisition of operational and health-related data in aircraft systems. In parallel, advances in Big Data analytics and Artificial Intelligence (AI) have driven significant progress in Predictive Maintenance (PdM), enabling earlier fault detection and more reliable estimations of Remaining Useful Life (RUL). This systematic literature review examines recent developments in AI-driven PdM and fault detection applied to aircraft over the last years. A total of 20 studies were selected based on predefined inclusion criteria and analyzed with respect to research trends, application domains, algorithmic approaches, and expected outputs. The findings indicate a strong research emphasis on civil aviation supported by accessible operational datasets, whereas military aviation research prioritizes fleet readiness and mission continuity, often with limited data transparency. Deep learning approaches, particularly hybrid models combining convolutional and recurrent architectures, dominate recent prognostic methodologies, while optimization and Model-Based Systems Engineering (MBSE) frameworks support decision-making integration. Despite these advancements, the transition from experimental models to operational deployment remains constrained by data heterogeneity, model explainability requirements, and regulatory certification processes. This review highlights current progress and identifies gaps and research opportunities to accelerate the adoption of robust and scalable PdM solutions in aviation. Full article

In the field of study of fluid–structure interaction (FSI), sloshing dynamics play a crucial role in various engineering applications, from aerospace to civil infrastructure. Finite Volume (FV)-based Computational Fluid Dynamics (CFD) methods for modeling free surface flows like sloshing are computationally expensive, particularly because high-resolution dynamic transient simulations are required. Moreover, FSI effects are usually considered by coupling different solvers for the fluid and the structural domain, respectively, thus adding to the computational burden due to the various steps of data transfer, interpolation, and mesh adaptation needed to obtain accurate results. On the other hand, reduced-order models of sloshing effects are usually obtained by tuning equivalent mechanical models, which often neglect more complex geometries and imperfections. To address this challenge, the use of acoustic finite elements, as an alternative approach for modeling free surface flows interacting with flexible structures, has been proposed previously. Such elements are defined with the sole dynamic pressure as the nodal degree of freedom; therefore, such methods can significantly accelerate simulations to predict sloshing-induced forces and pressure distribution, taking into account the actual geometry of the structure. Due to the reduced computational time, FSI analysis with acoustic elements can serve as a viable tool for control systems and design optimization. Potential applications of this approach include structural analysis of anti-sloshing devices in rocket propellant tanks, control systems for enhanced launch stability, and seismic safety assessment of liquid storage tanks, as well as slosh-induced wall load evaluation in the fuel and water reservoir, transportation, and energy systems. Validation of FSI effects is conducted against results from partitioned two-way coupled fluid–structural simulations. The simplified frequency-prediction model was reliable for practical flexibility ranges. Overall, this work deepens our understanding of how baffle characteristics influence slosh mitigation, offering valuable guidance for anti-sloshing device engineering. Full article

The beneficial use of mining waste aligns with circular economy thinking: saving primary resources can extend their lifetime and maintain availability, reduce the volume of legacy mining waste and its environmental impacts, and develop a resource beneficiation industry that is less energy and water intensive; mining lower grades at larger scale inevitably requires more beneficial reuse. Existing classifications applicable to different types of mine waste were reviewed. These include factors such as the mode of origin during the mining operation, grain size, chemical composition and stability. The result shows that these factors also largely control their civil engineering applications, suitability for end use sectors and potential hazards. Long-term liabilities related to chemical stability were identified as the most difficult challenge. When developing a reuse project, either by the end users or by the mine operator, it is likely that resource screening covering a comprehensive range of factors will be required, as none of the existing schemes individually cover all of the aspects needed to fully assess suitability for beneficial use. In conclusion, there is a need for a systematic and structured approach to classification of mining waste to facilitate reuse as raw materials, such as that presented in our review. Full article

Topology optimization is a powerful methodology for designing lightweight, economical, and efficient structures. However, traditional approaches often face challenges such as numerical instabilities and high computational costs, limiting their practical applicability. Recently, radial basis function (RBF)-based and neural network-based methods have emerged as promising alternatives through the reparameterization of the density field. Despite their potential, these methods typically rely on isotropic basis functions or static feature encodings, which limit their ability to capture fine-scale structural details, particularly in high-aspect-ratio features such as slender bar-like members and in geometrically symmetric structural patterns. To address this research gap, this paper introduces a novel Geometric-enhanced Neural Network (GeNN) for topology optimization based on Anisotropic Radial Basis Functions (ARBFs). By embedding ARBFs into the neural network framework, the proposed method provides a more geometrically informed density representation and inherently suppresses checkerboard patterns without additional filtering techniques. The proposed GeNN framework is thoroughly validated on benchmark problems, including several representative symmetric structural layouts, demonstrating improved computational efficiency compared to traditional methods and other neural-network-based topology optimization methods. In addition, the proposed method demonstrates strong scalability across various optimization problems. Notably, GeNN successfully optimized a 256 m-long bridge involving millions of degrees of freedom within ten minutes on a standard personal computer. This advancement demonstrates the practical potential of the proposed method for large-scale civil engineering applications. Full article

The development of lightweight, high-strength structural systems is a persistent pursuit in modern civil engineering. This paper presents an experimental study on a novel hybrid beam concept in which a sawn timber core is fully bonded with an externally applied Carbon Fiber-Reinforced Polymer (CFRP) laminate, fabricated through a controlled hand lay-up process. The design seeks to exploit the complementary characteristics of the two materials: timber provides compressive resistance and serves as a permanent formwork, while the CFRP carries tensile stresses with high efficiency. Fourteen hybrid beams, with variations in the number of longitudinal CFRP layers (one, two or, three), the presence or absence of longitudinal CFRP layers bonded along the top and bottom surfaces, and the presence or absence of circumferential wrapping in the pure bending region, were tested under four-point bending alongside two solid timber control beams. The results demonstrate that circumferential wrapping is a critical design detail. Wrapped beams consistently failed by tensile rupture of the CFRP—the intended failure mode—and exhibited ultimate moments 15–20% higher than their unwrapped counterparts. Beams with two longitudinal CFRP layers offered the most favorable balance between strength enhancement and material efficiency; adding a third layer shifted the failure mode to crushing of the timber core, indicating a core-limited condition. All hybrid beams showed pronounced linear-elastic behavior up to sudden brittle failure, with performance variability attributable to the inherent inhomogeneity of wood and the sensitivity of the hand lay-up process. The study provides quantitative data and mechanistic insights that support the design and application of bonded CFRP–timber hybrid beams as efficient structural members. Full article

Carbon Fiber Reinforced Polymer (CFRP)-strengthened steel plate systems demonstrate remarkable advantages in civil engineering structural rehabilitation, with their overall performance critically reliant on the interfacial bond behavior between CFRP and steel plates. This paper systematically reviews the typical failure modes, key factors influencing interfacial bond performance, and corresponding testing methodologies. Research indicates that interfacial shear stress dominates the failure process. Enhanced strengthening efficacy can be achieved by employing CFRP plates with optimized adhesive layer thickness (recommended 0.5–1.5 mm) and double-sided bonding configurations. Concurrently, substrate surface treatment and environmental factors (temperature–humidity, corrosion, etc.) significantly affect interfacial bond performance. Current research primarily focuses on the single-factor and strength failure performance of standard specimens, lacking a systematic understanding of the long-term durability and failure mechanisms of complex structures under multi-field coupling effects. This review further summarizes the distinctive features and application scenarios of innovative strengthening systems—including prestressed, unbonded, and shape memory alloy composite systems—to provide guidance for engineering selection and standardized design. Full article

Bolted joints are extensively used in mechanical and civil engineering structures because of their low cost, standardized design, and ease of installation and maintenance. The preload in a bolted connection is critical for ensuring joint stability and service reliability; however, preload degradation commonly occurs under complex operating conditions, particularly in environments involving sustained or cyclic vibration. To tackle this problem, this study proposes a portable, force-controllable percussion system for bolt looseness detection. The system integrates a solenoid-driven automatic percussion device, acoustic signal acquisition, onboard data-processing, and real-time visualization of diagnostic results. By adjusting the driving current of the solenoid, the percussion force can be accurately controlled, ensuring stable and repeatable excitation. Benefiting from its compact structure and low cost, the proposed system is suitable for real-time, on-site inspection of bolt looseness. Furthermore, a novel audio-processing approach based on a Siamese Capsule Network is developed to identify bolt looseness conditions. Compared with existing percussion-based techniques, the proposed method exhibits improved classification performance, especially in recognizing bolt states that are unseen during training. Exploratory experimental results validate the effectiveness of the proposed system and demonstrate its strong potential for practical engineering applications. Full article

As the manufacturing industry continues to advance its digital transformation, intelligent sensing technology has become a key support for achieving precise, efficient and automated quality control. However, current production line monitoring systems predominantly rely on fixed and costly monitoring equipment and sensors, lacking flexible and interactive first-person perspective perception approaches centered on on-site operators. Meanwhile, factory process monitoring often depends solely on visual expression rather than balancing the capabilities of the simulation model and visual state detection, leading to delayed responses to abnormal systems and hindering the adjustment strategy feedback. To address these limitations, this study provides wearable sensing for key workers, enriching the state perception capabilities in industrial scenarios. Furthermore, to achieve dynamic model and real-time visual representation of production line operations, a multi-source information-enhanced Petri nets model is proposed in terms of engineering and user-friendliness. With the solid mathematical basics of the Petri nets and the enriched human–machine data from the product line, this method provides an intuitive, dynamic and accurate reflection of the production system’s real-time operational status, offering a scientific and reliable basis for operational decision-making. The proposed approach has been implemented in a real-world production system for reinforced concrete civil defense doors, and this engineering application can also be extended to many other scenarios. Full article

Tire-Derived Aggregate (TDA) is a recycled fill material made by cutting scrap tires into small pieces that satisfy the gradation requirements in ASTM D 6270. Since its introduction to civil engineering applications, TDA fill and TDA backfill have been successfully implemented in many projects. However, the dynamic behavior of the TDA backfill under significant earthquakes has not been substantially addressed. The present study used nonlinear time-history Finite Element Analysis (FEA) to analyze the dynamic behavior of a retaining wall with TDA backfill captured from the full-scale shake table test. Unlike typical soil failure observed in a similar retaining wall with conventional soil backfill, significant wall sliding occurred because lightweight TDA contributed to reducing the friction resistance of the wall footing. Therefore, the analysis required modeling capability of rigid body motion and impact loading from the separation between the wall stem and the backfill. With adequate friction models and softened contact models, the FEA generated the dynamic motion of the retaining wall that matched well with the measured responses, including the wall sliding. The friction model between the wall footing and soil was most critical in accurately reproducing wall sliding motion. It was determined to use different friction coefficients for the two different earthquakes used in the study in order to simplify the rate dependence of the coefficient. Also, the softened contact model generated more reasonable impact force by allowing overclosure and finite stiffness during impact. The FEA model and modeling technique in the present study can be used for the seismic design of various field-scale retaining walls with TDA backfill. Full article