Search Results (156)

The use of 3D printing holds significant promise to transform the construction industry by enabling automation and customization, although key challenges remain—particularly the control of fresh-state rheology. This study presents a novel formulation that combines potassium-rich biomass fly ash (BFAK) with an air-entraining plasticizer (APA) to optimize the rheological behavior, hydration kinetics, and structural performance of mortars tailored for extrusion-based 3D printing. The results demonstrate that BFAK enhances the yield stress and thixotropy increases, contributing to improved structural stability after extrusion. In parallel, the APA adjusts the viscosity and facilitates material flow through the nozzle. Isothermal calorimetry reveals that BFAK modifies the hydration kinetics, increasing the intensity and delaying the occurrence of the main hydration peak due to the formation of secondary sulfate phases such as Aphthitalite [(K₃Na(SO₄)₂)]. This behavior leads to an extended setting time, which can be modulated by APA to ensure a controlled processing window. Flowability tests show that BFAK reduces the spread diameter, improving cohesion without causing excessive dispersion. Calibration cylinder tests confirm that the formulation with 1.5% APA and 2% BFAK achieves the maximum printable height (35 cm), reflecting superior buildability and load-bearing capacity. These findings underscore the novelty of combining BFAK and APA as a strategy to overcome current rheological limitations in digital construction. The synergistic effect between both additives provides tailored fresh-state properties and structural reliability, advancing the development of a sustainable SMC and printable cementitious materials. Full article

The operational efficiency of the main steam pipelines at thermal power plants is reduced due to several factors, including operating temperature, pressure, service life, and the frequency of process shutdowns, which contribute to the degradation of heat-resistant steels. The study aims to identify the features of changes in the sizes of grains and carbides along their boundaries, as well as mechanical properties (hardness, strength, plasticity and fracture toughness) along the wall thickness of both pipes in the initial state and after operation with block shutdowns. Preliminary electrolytic hydrogenation of specimens (before tensile tests in air) showed even more clearly the negative consequences of operational degradation of steel. The degradation of steel was also assessed using fracture toughness (J_IC). The value of J_IC for operated steel with a smaller number of shutdowns decreased by 32–33%, whereas with a larger number of shutdowns, its decrease in the vicinity of the outer and inner surfaces of the pipe reached 65 and 61%, respectively. Fractographic signs of more intense degradation of steel after a greater number of shutdowns were manifested at the stage of spontaneous fracture of specimens by changing the mechanism from transgranular cleavage to intergranular, which indicated a decrease in the cohesive strength of grain boundaries. Full article

Special police units like Austria’s EKO Cobra are uniquely trained to manage high-risk operations, including terrorism, amok situations, and hostage crises. This study explores how group dynamics contribute to operational safety and resilience, emphasising the interconnection between risk perception, training, and operational practices. Interviews with current and former EKO Cobra members reveal key risk factors, including overconfidence, insufficient training, inadequate equipment, and the challenges of high-stakes scenarios. Using a structured yet thematically flexible interview analysis approach, the study adopts group dynamics theory as its framework and applies a semi-inductive, semi-deductive qualitative methodology. It examines risk categorisation in ad hoc operations, as well as the interplay between risk perception and training, proposing actionable strategies to enhance safety and preparedness through tailored training programmes. The findings underscore the transformative impact of intensive scenario-based and high-stress training, which enhances situational awareness and reinforces team-based responses through cohesion and effective communication. Group dynamics, including cohesion and effective communication, play a pivotal role in mitigating risks and ensuring operational success. Importantly, this research advocates for continuous, adaptive, and specialised training to address evolving challenges. By linking theoretical frameworks with practical and actionable insights, this study proposes a holistic training approach that promotes both resilience and long-term sustainability in police operations. These findings offer valuable guidance to elite units like EKO Cobra for broader policy frameworks by providing insights that make police operations safer and more effective and resilient. Full article

This study presents a comparative benchmarking analysis of G20 nations’ agri-food competitiveness across five critical pillars: food security and nutrition, trade and geopolitics, environmental sustainability, fiscal regimes, and entrepreneurship support. Using a structured benchmarking framework with 13 performance indicators sourced from internationally recognized datasets, the research delivers a comprehensive evaluation of national agri-food systems. The analysis reveals significant disparities in transparency, policy coherence, and investment in innovation across member states. Countries such as the United States, Germany, and Australia emerge as leaders, driven by integrated policy frameworks, trade surpluses, and sustainable production practices. Others fall behind due to import dependence, fragmented governance, or weak innovation ecosystems. Canada performs consistently in trade metrics but is hindered by high emissions intensity, infrastructure constraints, and a lack of a cohesive national food strategy. Theoretically, this work contributes to the emerging field of agri-food system diagnostics by operationalizing a cross-pillar benchmarking methodology applicable at the national level. Practically, it offers policymakers a decision-support tool for identifying structural gaps and setting reform priorities. The framework enables governments, trade partners, and multilateral institutions to design targeted interventions aimed at boosting food system resilience, economic competitiveness, and sustainability in an era of rising geopolitical and environmental volatility. Full article

This study investigates the integration of advanced optimization algorithms within energy-intensive infrastructures and industrial plants. In fact, the authors focus on the dynamic interplay between computational intelligence and operational efficiency in wastewater treatment plants (WWTPs). In this context, energy optimization is thought of as a hybrid process that emerges at the intersection of engineered systems, environmental dynamics, and operational constraints. Despite the known energy-intensive nature of WWTPs, where pumps and blowers consume over 60% of total power, current methods lack systematic, real-time adaptability under variable conditions. To address this gap, the study proposes a computational framework that combines hydraulic simulation, manufacturer-based performance mapping, and a Memetic Algorithm (MA) capable of real-time optimization. The methodology synthesizes dynamic flow allocation, auto-tuning mutation, and step-by-step improvement search into a cohesive simulation environment, applied to a representative parallel-pump system. The MA’s dual capacity to explore global configurations and refine local adjustments reflects both static and kinetic aspects of optimization: the former grounded in physical system constraints, the latter shaped by fluctuating operational demands. Experimental results across several stochastic scenarios demonstrate consistent power savings (12.13%) over conventional control strategies. By bridging simulation modeling with optimization under uncertainty, this study contributes to sustainable operations management, offering a replicable, data-driven tool for advancing energy efficiency in infrastructure systems. Full article

The wear failure of vertical shaft planetary mixer blades under complex working conditions directly affects the quality and productivity of concrete. Given that it is time-consuming and labor-intensive to obtain the wear characteristics of mixer blades by experimental methods, this study used numerical simulation to analyze the effects of different factors on the wear characteristics of mixer blades based on the Hertz–Mindlin with JKR cohesive contact model and the Archard wear model. The results of this study show that under the influence of different factors, the blade is subjected to tangential cumulative contact energy and contact force is significantly larger than that in the normal direction, the wear of the blade is judged to be the form of abrasive wear accompanied by impacts, and the wear on the outer middle and lower edge regions of the blade is the most serious. Specifically, for every 5 rpm increase in mixing speed, the blade wear rate increases by 24.14% on average; for every 5° increase in blade angle, the blade wear rate decreases by 2.9% on average; for every 10% increase in the mass ratio of stone aggregate, the blade wear rate increases by 5.95% on average; conical aggregates have the most serious effect on blade wear, while spherical aggregates have the most minor effect. This study provides the theoretical basis and numerical support for understanding the reasons for blade wear loss and enhancing the service life of mixer blades. Full article

Residual particles embedded at the bond coat/substrate (BC/SUB) interface after grit blasting can affect the failure behavior of thermal barrier coatings (TBCs) under thermal cycling. This study employed a 2D finite element model combining the cohesive zone method (CZM) and extended finite element method (XFEM) to analyze the effect of interfacial grit particles. Specifically, the CZM was used to simulate crack propagation at the BC/thermally grown oxide (TGO) interface, while XFEM was applied to model the arbitrary crack propagation within the BC layer. Three models were analyzed: no grit inclusion, 20 μm grit particles, and 50 μm grit particles at the BC/SUB interface. This systematic variation allowed isolating the influence of particle size on the location of crack propagation onset, stress distribution, and crack growth behavior. The results showed that grit particles at the SUB/BC interface had negligible influence on the crack propagation location and rate at the BC/TGO interface, due to their spatial separation. However, their presence significantly altered the radial tensile stress distribution within the BC layer. Larger grit particles induced more intense stress concentrations and promoted earlier and more extensive vertical crack propagation within the BC. However, due to plastic deformation and stress redistribution in the BC, the crack propagation was progressively suppressed in the later stages of thermal cycling. Overall, grit particles primarily promoted vertical crack propagation within the BC layer. Optimizing grit blasting to control grit particle size is crucial for improving the durability of TBCs. Full article

Machine Learning (ML) is increasingly applied across various domains, addressing tasks such as predictive analytics, anomaly detection, and decision-making. Many of these applications share similar underlying tasks, offering potential for systematic reuse. However, existing reuse in ML is often fragmented, small-scale, and ad hoc, focusing on isolated components such as pretrained models or datasets without a cohesive framework. Product Line Engineering (PLE) is a well-established approach for achieving large-scale systematic reuse in traditional engineering. It enables efficient management of core assets like requirements, models, and code across product families. However, traditional PLE is not designed to accommodate ML-specific assets—such as datasets, feature pipelines, and hyperparameters—and is not aligned with the iterative, data-driven workflows of ML systems. To address this gap, we propose Machine Learning Product Line Engineering (ML PLE), a framework that adapts PLE principles for ML systems. In contrast to conventional ML reuse methods such as transfer learning or fine-tuning, our framework introduces a systematic, variability-aware reuse approach that spans the entire lifecycle of ML development, including datasets, pipelines, models, and configuration assets. The proposed framework introduces the key requirements for ML PLE and the lifecycle process tailored to machine-learning-intensive systems. We illustrate the approach using an industrial case study in the context of space systems, where ML PLE is applied for data analytics of satellite missions. Full article

Aiming at the problems of intense subjectivity and high redundancy in the screening of indicators in the stability evaluation of loess slopes, this study proposes an evaluation method integrating the validity and reliability coefficients. Following an initial screening of the indexes based on the engineering geological characteristics of loess slopes and literature research, 22 significant indicators were kept following a qualitative screening process (the principles of uniqueness, purpose, etc.); combined with the improved grey correlation-Delphi model for quantitative screening, the validity coefficient ($\beta =0.0816$) and reliability coefficient ($\rho =0.9609$) were introduced to validate the scientificity and consistency of the indicator system. The results showed that the 10 core indicators, including Cohesion, Internal Friction Angle, Maximum Monthly Rainfall, Rock Mass Structure, and Anthropogenic Engineering Activities, had a significant influence on loess slope stability, and the screening process effectively reduced the subjective bias and information redundancy. The method provides a data-driven theoretical framework for eolian slope risk assessment, which can improve the accuracy of landslide warning and the reliability of engineering protection design, and the engineering applicability of the model can be further optimized by combining the dynamic environmental parameters and multi-source monitoring data. Full article

To investigate the effects of the strain rate and confinement on characteristic stresses and strength criterion in granite under static to quasi-static loading, triaxial compression tests were systematically conducted across strain rates of 10⁻⁶ to 10⁻² s⁻¹ and confining pressures of 0–40 MPa. Stress–strain curves, characteristic stresses, macro-fracture patterns, and dynamic strength criterion were analyzed. The experimental results indicate the following: (1) crack damage stress (σ_cd) and peak stress (σ_p) show strong linear correlations with logarithmic strain rate, while crack initiation stress (σ_ci) exhibits weaker rate dependence; (2) linear regression establishes characteristic stress ratios σ_ci = 0.58σ_p and σ_cd = 0.85σ_p; (3) macroscopic fractures transition from Y-shaped shear patterns under low confinement and strain rate conditions to X-shaped shear failures at higher confinement and strain rate; (4) the Mohr–Coulomb criterion effectively characterizes dynamic strength evolution in granite, with cohesion increasing 22% across tested strain rates while internal friction angle remains stable at around 50°; (5) variations in microcrack activity intensity during rock deformation stages result in the dynamic increase factor for characteristic stresses (CSDIF) of σ_ci being lower than σ_cd and σ_p. More importantly, σ_cd and σ_p exhibit CSDIF reductions as confining pressure increases. This differential behavior is explained by confinement-enhanced shear fracturing dominance during crack propagation stages, combined with the lower strain rate sensitivity of shear versus tensile fracture toughness. Full article

Fire, whether wild or urban, depends on the triad of oxygen, fuel, and heat. Urban fires, although smaller in scale, have devastating impacts, as evidenced by the 2018 wildfire in Mati, Attica (Greece), which claimed 104 lives. The elderly and children are the most vulnerable due to mobility and cognitive limitations. This study applies Grammatical Evolution (GE), a machine learning method that generates interpretable classification rules to predict the consequences of urban fires. Using historical data (casualties, containment time, and meteorological/demographic parameters), GE produces classification rules in human-readable form. The rules achieve over 85% accuracy, revealing critical correlations. For example, high temperatures (>35 °C) combined with irregular building layouts exponentially increase fatality risks, while firefighter response time proves more critical than fire intensity itself. Applications include dynamic evacuation strategies (real-time adaptation), preventive urban planning (fire-resistant materials and green buffer zones), and targeted awareness campaigns for at-risk groups. Unlike “black-box” machine learning techniques, GE offers transparent human-readable rules, enabling firefighters and authorities to make rapid informed decisions. Future advancements could integrate real-time data (IoT sensors and satellites) and extend the methodology to other natural disasters. Protecting urban centers from fires is not only a technological challenge but also a moral imperative to safeguard human lives and societal cohesion. Full article

This study investigates rainfall-induced slope instability in fine-grained clayey soils through a probabilistic and sensitivity analysis framework that integrates spatial variability. Moving beyond traditional deterministic methods, Monte Carlo simulations were employed to quantify uncertainty in geotechnical parameters—unit weight, cohesion, and friction angle—modeled as random fields with a 1 m spatial resolution. This approach realistically captures natural soil heterogeneity and its influence on slope behavior during rainfall events. Transient seepage and slope stability analyses were performed using SEEP/W and SLOPE/W, respectively, with the Spencer method ensuring full equilibrium. This study examined how slope height, inclination, rainfall intensity and duration, and soil properties affect the factor of safety (FS). The results showed that higher rainfall intensity and longer durations significantly increase failure risk. For example, under 9 mm/h rainfall for 48 h, slopes taller than 10 m at 45° inclination exhibited failure probabilities over 30%. At 20 m, FS dropped to 0.68 with a 100% probability of failure. Sensitivity analysis confirmed cohesion and friction angle as key stabilizing factors, though their impact diminishes with infiltration. A dataset of 9984 slope scenarios was generated, supporting future machine learning applications for risk assessment and climate-resilient slope design. Full article

This review maps the complex relationship between embodied carbon emissions and energy within the construction sector, aiming to generate insights that facilitate more informed and sustainable decision-making for new construction projects. It addresses the challenges associated with the variability in standards, methodologies, and emission factors used in embodied carbon assessments, which contribute to discrepancies and impede the development of cohesive carbon reduction strategies. The paper identifies key drivers of embodied emissions, with a particular emphasis on energy consumption, and represents the findings in the form of a detailed graph, elucidating the interplay between energy use and embodied emissions and providing actionable insights to enhance sustainability selections. Additionally, a case study of four residential low-rise projects in Abu Dhabi is conducted to analyze the energy-based carbon emissions of construction projects, examine their patterns over the entire construction period, and determine the energy-based carbon emission intensity of projects typically powered by diesel generators. This work expands the existing knowledge base by offering actionable insights into how energy-related decisions can significantly influence embodied carbon outcomes and aims to guide stakeholders in optimizing selections to advance sustainability practices within the construction industry. Full article

Considering the environmental objectives assumed by the European Commission through Cohesion Policy, the main aim of this study is to check the stability of the relationship between economic growth, GHG emissions, and energy consumption, also known as the 3E relationship, which also allows us to recreate EU policy recommendations. The analysis of EU member states using panel data and time series (1990–2023) showed significant shifts in the relationship between the three variables, especially due to the COVID-19 pandemic. Consequently, many countries successfully decoupled emissions from economic growth. Nevertheless, the persistent carbon-intensive energy use patterns in most EU countries remain a major obstacle, emphasizing the need for increased efforts in cleaner energy technologies and sustainable economic transitions. Full article

Landslides are mass movements of rock, soil, or debris under the influence of gravity. These phenomena occur due to the loss of slope stability or imbalance of external loads. The intensity and consequences of landslides depend on various factors such as topography, geological structure, and precipitation regime. This study investigates the characteristics of rainfall-induced landslides in red basaltic soils on the basis of field investigations, geotechnical surveys, and slope stability modeling under anthropogenic triggers. The results indicate a close relationship between soil moisture and shear strength parameters, which significantly influence slope stability. A real-time observation system recorded groundwater level fluctuation in relation to surface runoff and precipitation rates. It is revealed that intense rainfall and low temperatures regulate soil moisture, resulting in a reduction of cohesion and shear strength parameters. These findings enhance the understanding of landslide mechanism in basaltic soil regions, which are highly sensitive to precipitation. The results also highlight that human activities play a significant role in triggering landslides. Therefore, a real-time monitoring system for rainfall, soil moisture, and groundwater is essential for early warning and supports the integration of smart technologies and Internet of Things (IoT) solutions in natural disaster management. Full article