Search Results (20,120)

Efficient acquisition of rock mass parameters is a critical step for conducting numerical simulations in tunneling and ensuring the safety of subsequent construction. This paper proposes an intelligent back-analysis method for key rock mass parameters (Young’s modulus, Poisson’s ratio, cohesion, and friction angle) based on excavation-induced deformation data, using a deformation database that incorporates multi-feature values from tunnel excavation. This study employs five machine learning algorithms with single-feature inputs and three deep neural networks (DNNs) with multi-feature inputs, with a particular focus on convolutional neural network (CNN) due to their superior performance in terms of both accuracy and efficiency. The results demonstrate that the CNN model incorporating excavation features achieves excellent performance in parameter back-analysis, with an R² of 0.99 and 97.8% of predictions having errors within 5%. Compared with machine learning models using single-feature inputs, the CNN-based approach improves predictive performance by an average of 13.9%. Furthermore, compared with other DNNs, the CNN consistently outperforms across various evaluation metrics. This study also investigates the CNN’s capability to predict rock mass parameters using deformation data from early-stage excavation. After ten excavation steps, 96.9% of test samples had prediction errors within 5%. Finally, the proposed method was validated using field-monitored deformation data from a real highway tunnel project, confirming the method’s effectiveness and practical applicability. Full article

Electrocatalytic conversion of CO₂ to high-energy-density multicarbon products (C₂₊) offers a sustainable route for renewable energy storage and carbon neutrality. Precisely modulating Cu-based catalysts to enhance C₂₊ selectivity remains challenging due to uncontrollable reduction of Cu^δ+ active sites. Here, an efficient and stable Ge/Cu catalyst was developed for CO₂ reduction to ethanol via Ge modification. A Cu₂O/GeO₂/Cu core–shell composite was constructed by controlling Ge doping. The structure–performance relationship was elucidated through in situ characterization and theoretical calculations. Ge⁴⁺ stabilized Cu¹⁺ active sites and regulated the surface microenvironment via electronic effects. Ge modification simultaneously altered CO intermediate adsorption to promote asymmetric CO–CHO coupling, optimized water structure at the electrode/electrolyte interface, and inhibited over-reduction of Cu^δ+. This multi-scale synergistic effect enabled a significant ethanol Faradaic efficiency enhancement (11–20%) over a wide potential range, demonstrating promising applicability for renewable energy conversion. This study provides a strategy for designing efficient ECR catalysts and offers mechanistic insights into interfacial engineering for C–C coupling in sustainable fuel production. Full article

Prefabricated stations utilizing digital modeling techniques demonstrate significant advantages over traditional cast-in-place methods, including improved dimensional accuracy, reduced environmental impact, and minimized material waste. To maximize these benefits, this study develops a digital twin platform for prefabricated assembled superimposed stations through the integration of Digital Twin Scene–Entity–Relationship–Incident–Control (SERIC) modeling with IoT technology. The platform adopts a “1+5+N” architecture that implements model-data separation, lightweight processing, and model-data association for SERIC model management, while IoT-enabled data acquisition facilitates lifecycle data sharing. By integrating BIM models, engineering data, and IoT sensor inputs, the platform employs multi-source analytics to monitor construction progress, enhance safety surveillance, ensure quality control, and optimize designs. Implementation at Jinan Metro Line 8’s prefabricated underground station confirms the SERIC-IoT digital twin’s efficacy in advancing sustainable, high-quality rail transit development. Results demonstrate the platform’s capacity to improve construction efficiency and operational management, aligning with urban rail objectives prioritizing sustainability and technological innovation. This study establishes that integrating SERIC modeling with IoT in digital twin frameworks offers a robust approach to modernizing prefabricated station construction, with scalable applications for future smart transit infrastructure. Full article

Surrogate-assisted evolutionary algorithms (SAEAs) have emerged as a powerful class of optimization methods that utilize surrogate models to address expensive optimization problems (EOPs), where fitness evaluations (FEs) are expensive or limited. By leveraging previously evaluated solutions to learn predictive models, SAEAs enable efficient search under constrained evaluation budgets. However, the performance of SAEAs heavily depends on the quality and utilization of surrogate models, and balancing the accuracy and generalization ability makes effective model construction and management a key challenge. Therefore, this paper introduces a novel probability selection-based surrogate-assisted evolutionary algorithm (PS-SAEA) to enhance optimization performance under FE-constrained conditions. The PS-SAEA has two novel designs. First, a probabilistic model selection (PMS) strategy is proposed to stochastically select surrogate models, striking a balance between prediction accuracy and generalization by avoiding overfitting commonly caused by greedy selection. Second, a weighted model ensemble (WME) mechanism is developed to integrate selected models, assigning weights based on individual prediction errors to improve the accuracy and reliability of fitness estimation. Extensive experiments on benchmark problems with varying dimensionalities demonstrate that PS-SAEA consistently outperforms several state-of-the-art SAEAs, validating its effectiveness and robustness in dealing with various complex EOPs. Full article

Since it is difficult to obtain the positions of dynamic loads on structures, this paper suggests a new method to identify the locations of dynamic loads step-by-step based on the correlation coefficients of dynamic responses. First, a recognition model for dynamic load position based on a finite-element scheme is established, with the finite-element domain divided into several regions. Second, virtual loads are applied at the central points of these regions, and acceleration responses are calculated at the sensor measurement points. Third, the maximum correlation coefficient between the calculational and measured accelerations is obtained, and the dynamic load is located in the region with the virtual load corresponding to the maximum correlation coefficient. Finally, this region is continuously subdivided with the refined mesh until the dynamic load is pinpointed in a sufficiently small area. Different virtual load construction methods are proposed according to different types of loads. The frequency response function, unresolvable for the actual problem due to the unknown location of the real dynamic load, can be transformed into a solvable form, involving only known points. This transformation simplifies the analytical process, making it more efficient and applicable to analysis of the dynamic behavior of the system. The identification of the dynamic load position in the entire structure is then transformed into a sub-region approach, focusing on the area where the dynamic load acts. Simulations for case studies are conducted to demonstrate that the proposed method can effectively identify positions of single and multiple dynamic loads. The correctness of the theory and simulation model is verified with experiments. Compared to recent methods that use machine learning and neural networks to identify positions of dynamic loads, the approach proposed in this paper avoids the heavy computational cost and time required for data training. Full article

A novel numerical scheme is developed in this work to approximate solutions (APPSs) for nonlinear fractional differential equations (FDEs) governed by Robin boundary conditions (RBCs). The methodology is founded on a spectral collocation method (SCM) that uses a set of basis functions derived from generalized shifted Jacobi (GSJ) polynomials. These basis functions are uniquely formulated to satisfy the homogeneous form of RBCs (HRBCs). Key to this approach is the establishment of operational matrices (OMs) for ordinary derivatives (Ods) and fractional derivatives (Fds) of the constructed polynomials. The application of this framework effectively reduces the given FDE and its RBC to a system of nonlinear algebraic equations that are solvable by standard numerical routines. We provide theoretical assurances of the algorithm’s efficacy by establishing its convergence and conducting an error analysis. Finally, the efficacy of the proposed algorithm is demonstrated through three problems, with our APPSs compared against exact solutions (ExaSs) and existing results by other methods. The results confirm the high accuracy and efficiency of the scheme. Full article

To address the challenges of strong nonlinearity, stringent terminal constraints, and the trade-off between computational efficiency and accuracy in the midcourse guidance trajectory optimization problem of aerodynamically controlled interceptors, this paper proposes a variable-discrete-scale sequential convex programming (SCP) method. Firstly, a dynamic model is established by introducing the range domain to replace the traditional time domain, thereby reducing the approximation error of the planned trajectory. Second, to overcome the critical issues of solution space restriction and trajectory divergence caused by terminal equality constraints, a terminal error-proportional relaxation approach is proposed. Subsequently, an improved second-order cone programming (SOCP) formulation is developed through systematic integration of three key techniques: terminal error-proportional relaxation, variable trust region, and path normalization. Finally, an initial trajectory generation algorithm is proposed, upon which a variable-discrete-scale optimization framework is constructed. This framework incorporates a residual-driven discrete-scale adaptation mechanism, which balances discretization errors and computational load. Numerical simulation results indicate that under large discretization scales, the computation time required by the improved SOCP is only about 5.4% of that of GPOPS-II. For small-discretization-scale optimization, the SCP method with the variable discretization framework demonstrates high efficiency, achieving comparable accuracy to GPOPS-II while reducing the computation time to approximately 7.4% of that required by GPOPS-II. Full article

Due to a complex of factors, visually impaired people are facing difficulties and increased risks during fire emergencies and evacuations from different types of buildings. Even if a lot of studies have been conducted to improve the mobility and autonomy of people with visual impairment during emergency evacuation processes, these offer only partial solutions, especially in the presence of uncertainties characteristic of fire evolution. Aiming for a more comprehensive approach to the safe evacuation of people with visual impairments, this paper proposes a support system that integrates innovative aspects related to the architecture of the application, modeling and simulation methods, and experimental realization. The system is decentralized, capable of anticipating possible fire extensions and determining, in real-time, new corresponding evacuation routes. The overall design complies with the standard norms in emergency situations. Two models, one developed in Stateflow and the other based on Delay Time Petri Nets (DTPN), were constructed to describe the dynamic behavior of the system in the presence of unexpected events that can change the initial recommended evacuation path. To test the functionality and efficiency of the proposed system, the conditions created by potential fire sources were simulated as a part of realistic scenarios. Tests were conducted with visually impaired people. Simulation and prototype testing showed that the presented system can improve evacuation times, achieving a measurable gain compared to scenarios where there is no information regarding fire evolution. Full article

In this paper, heat dissipation through a passive vertical plate fin heat sink via natural convection was numerically investigated. The influence of two nondimensional geometric parameters, fin spacing-to-thickness ratio and fin height-to-spacing ratio, on the heat sink’s thermal performance was evaluated. A mathematical model describing the three-dimensional steady-state problem of buoyancy-driven flow and heat transfer was formulated. The solution was obtained numerically using the finite volume method in Ansys Fluent. The model and numerical procedure were validated by comparing the numerical predictions with measurements acquired on a constructed experimental apparatus. The heat sink thermal performance was assessed based on a series of performance metrics: heat dissipation rate, heat transfer coefficient, overall thermal resistance, and fin efficiency. Fin spacing-to-thickness ratio was varied between 1.86 and 4.8. Heat dissipation rate displayed a clear peak at a value of approximately 2.6, which coincided with a minimum in the overall thermal resistance. The heat transfer coefficient initially grew steadily, but at higher values of fin spacing-to-thickness ratio, it began to stagnate. Fin efficiency consistently decreased across the investigated range. Fin height-to-spacing ratio was varied between 1.11 and 7.78. The heat dissipation rate increased almost linearly across this range, but when the mass-specific heat dissipation rate was considered, it yielded diminishing returns. The heat transfer coefficient likewise exhibited a plateauing trend, while fin efficiency decreased steadily across the investigated range of fin height-to-spacing ratio. The obtained numerical results provide guidelines for geometry selection and can serve as a foundation for further analyses and optimizations of passive heat sinks’ thermal performance. Full article

Street vitality has become a crucial metric for smart city management. Classical theories qualitatively explain that street vitality originates from the dynamic interaction between people and spatial carriers, yet the threshold effect within this process has not been addressed, leaving a gap in urban research. This study selects South China, one of China’s most vibrant and globally influential regions, introduces dissipative structure theory based on classical theories, and constructs a threshold effect hypothesis model for the vitality formation mechanism. Through energy efficiency conversion of data and a slope-based method for identifying balanced time periods, the periods of supply–demand balance in energy efficiency were identified, the threshold effect in vitality formation was captured, and critical thresholds were measured. The results indicate the following: (1) the hypothesis model is valid; (2) the threshold effect is inevitable and periodic, primarily occurring on workdays from 12:00 to 13:00 and 18:00 to 19:00, and on rest days from 08:00 to 09:00 and 18:00 to 19:00; and (3) the activation threshold is quantifiable and exhibits volatility, ranging from 0.40 to 1.56, varying specifically by city, season, day type, and street type. This study advances the translation of street vitality research from theory into practice and provides theoretical support and strategic guidance for smart city management globally, particularly in developing countries. Full article

The transition to sustainable energy systems is essential for attaining global carbon neutrality targets. Demand-side flexibility for carbon mitigation is investigated, and a low-carbon operational strategy tailored for industrial park energy systems is proposed. Demand response (DR) is classified into price-based and alternative categories, with respective models developed utilizing a price elasticity matrix and accounting for electricity-to-heat conversion. Integrated energy system (IES) involvement in the carbon trading market is incorporated through a stepped carbon pricing mechanism to regulate emissions. A mixed-integer linear programming model is constructed to characterize IES operations under ladder-type carbon pricing and DR frameworks. The model is resolved via the off-the-shelf commercial solver, facilitating effective optimization of dispatch over multiple time intervals and complex market interactions. Case study findings indicate that implementing stepped carbon pricing alongside DR strategies yields a 44.45% reduction in carbon emission costs, a 9.85% decrease in actual carbon emissions, and a 10.62% reduction in total system costs. These results offer a viable approach toward sustainable development of IES, achieving coordinated improvements in economic efficiency and low-carbon performance. Full article

The article presents the results of research and development work conducted as part of the H2GEO project, aimed at creating a comprehensive technology for the processing of post-mining coal waste heaps. The core of the solution is a mobile density separation system based on a pulsating jig, enabling effective recovery of carbonaceous and mineral fractions. Laboratory experiments assessed the impact of key process parameters—such as sieve slot size, pulsation frequency, and enrichment time—on the efficiency and accuracy of separation for different grain size classes. The most favorable results were obtained using a 2.5 mm screen, a pulsation frequency of 60 min⁻¹, and extended enrichment time, which ensured high-quality separation and low ash content in the carbon-bearing product. The findings supported the design of a new industrial separator (jig) equipped with advanced control systems, facilitating the production of homogeneous fractions suitable for further processing into hydrogen, geopolymers, and construction materials. The proposed solution aligns with circular economy principles, promoting waste reuse, environmental hazard mitigation, and the revitalization of degraded post-industrial areas. Full article

4H-Benzo[d][1,3]oxathiin-4-ones and 4H-benzo[d][1,3]dioxin-4-ones, as important classes of sulfur- or oxygen-containing heterocyclic compounds, possess significant application potential in the fields of pharmaceutical chemistry, agriculture, and the food industry due to their distinctive structural characteristics and diverse biological activities. In recent years, efficient synthetic strategies for these compounds have witnessed remarkable progress. This review summarizes significant advancements in the construction of these heterocycles from 2012 to the present. Full article

With accelerating urbanization, subway stations, as high-energy-consumption sectors, face significant challenges in maintaining power supply stability and ensuring power quality. This paper proposed a novel voltage compensation solution utilizing superconducting magnetic energy storage (SMES) to suppress voltage fluctuations in the traction system of a large subway station with multiple lines, which was caused by frequent acceleration and regenerative braking of multiple subway trains. Using the MATLAB/Simulink platform, a model of the traction power system with SMES for a large subway station with multiple lines was constructed. Appropriate control methods and hierarchical control strategies were used to suppress voltage fluctuations in both single-line and multi-line configurations at subway stations. The technical advantages of SMES in rapid response and efficient charging/discharging were explored. Overall, results show SMES with the novel control strategies can effectively suppress voltage fluctuations on both single- and triple-line configurations, validating the feasibility in mitigating voltage fluctuations and enhancing regenerative braking energy utilization. Full article

This study proposes a composite acoustic porous duct metamaterial muffler composed of a perforated tortuous channel and an externally wrapped porous layer, integrating both structural resonance and material damping effects. Theoretical models for the perforated plate, tortuous channel, and porous material were established, and analytical formulas for the total acoustic impedance and transmission loss of the composite structure were derived. Finite element simulations verified the accuracy of the models. A systematic parametric study was then performed on the effects of porous material type, thickness, and width on acoustic performance, showing that polyester fiber achieves the best results at a thickness of 30 mm and a width of 5 mm. Further analysis of periodic distribution modes revealed that axial periodic arrangement significantly enhances the peak noise attenuation, radial periodic arrangement broadens the effective bandwidth, and multi-frequency parallel configurations further expand the operating range. Considering practical duct conditions, a single-layer multi-cell array was constructed, and its modal excitation mechanism was clarified. By employing the Particle Swarm Optimization (PSO) algorithm for multi-parameter optimization, the average transmission loss was improved from 26.493 dB to 29.686 dB, corresponding to an increase of approximately 12.05%. Finally, physical samples were fabricated via 3D printing, and four-sensor impedance tube experiments confirmed good agreement among theoretical, numerical, and experimental results. The composite structure exhibited an average experimental transmission loss of 24.599 dB, outperforming the configuration without porous material. Overall, this work highlights substantial scientific and practical advances in sound energy dissipation mechanisms, structural optimization design, and engineering applicability, providing an effective approach for broadband and high-efficiency duct noise reduction. Full article