Scipedia
Not a member yet
33380 research outputs found
Sort by
Combining Copula Theory and Machine Learning for Prediction of Ground Vibrations Induced by Tunnel Blasting
No full textTo effectively predict and control the peak particle velocity (PPV) induced by tunnel blasting, this study investigates the Qinhuangdao Jiaoshan Tunnel as a case study. This study first proposes an Improved Particle Swarm Optimization (IPSO) algorithm through theoretical derivation. Building upon IPSO, a further enhanced algorithm, termed CIPSO, is developed by integrating a dependency model derived from Copula theory. The CIPSO algorithm is then employed to optimize a Support Vector Regression (SVR) model, establishing the final CIPSO-SVR prediction framework. Copula theory was employed to quantify the correlation between PPV and surface cumulative settlement (S). A regularization term incorporating Kullback-Leibler (KL) divergence was then embedded into the SVR objective function. The Hyperparameters of the CIPSO-SVR model were optimized using fixed-step rolling cross-validation. The model’s predictive performance was rigorously compared against CIPSO-optimized Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models, as well as against SVR, CNN, and LSTM models optimized by the Grey Wolf Optimizer (GWO) and Moth-Flame Optimization (MFO) algorithms. The results show that the CIPSO-SVR model achieves superior accuracy and robustness on the test set (R2= 0.9569) in predicting PPV compared to the alternative models. Crucially, the model effectively captures the inherent nonlinear relationships of complex engineering problems, even with small-sample data.OPEN ACCESS Received: 18/08/2025 Accepted: 17/10/2025 Published: 23/01/202
Reliability Modeling Using Weibull Distribution in Partially Accelerated Life Tests with Unified Hybrid Censoring: White Organic LEDs and Micro-Droplet Applications
No full textAccelerated life testing has become essential in modern reliability assessment, particularly for high-reliability products where traditional testing is often impractical due to time constraints. This study introduces a comprehensive framework for reliability analysis using Weibull-based partially accelerated life tests under a unified hybrid censoring scheme. Assuming lifetimes follow a Weibull distribution, we provide both classical and Bayesian estimation procedures to estimate core quantities, shape, scale, and the acceleration factor, alongside reliability metrics at normal operating conditions. The inferential framework encompasses point estimation along with uncertainty quantification, using both approximate confidence intervals and Bayesian credible intervals derived from Markov Chain Monte Carlo methods. A comprehensive Monte Carlo simulation study evaluates the performance of various methods in terms of mean squared error, interval coverage, and average interval width across a range of censoring patterns. The results provide actionable insights and practical recommendations for selecting appropriate methods and designing future studies under different censoring scenarios. The proposed methodology is further illustrated through two real-world case studies: the reliability of white organic light-emitting diodes and the lifetime of micro-droplets in ambient environments. These examples highlight the method’s flexibility and practical relevance in both engineering and biomedical reliability applications.OPEN ACCESS Received: 31/07/2025 Accepted: 10/09/2025 Published: 23/01/202
A data-driven model of waste gasi pyrolysis: One tailored approach for an experimental facility from the Czech Republic
No full textThe increasing demand for sustainable energy production necessitates the development of innovative technologies for converting municipal waste into valuable energy offering a viable alternative to fossil fuels. This study presents aflexible, portable, and expandable waste-to-energy concept that integrates gasification and pyrolysis processes production of combustible gases and liquid fuels. Particular emphasis is placed on the use of transparent and interpretable modelling approaches to support system optimization and future scalability. The proposed methodology is demonstrated on two experimental systems currently operated at CEET Explorer, VSB– Technical University of Ostrava, Czech Republic: (i) A primary gasification facility equipped with a plasma torch, reactor, hydrogen separator and tank, fuel cells, and renewable grid connections; and (ii) a secondary pyrolysis unit designed to maximize pyrolysis oil production. Both systems are modelled and simulated using in-house software developed in Python, employin
Mechanical behavior analysis at low temperature of flax/epoxy laminates
No full textThe growing environmental consciousness of recent years has driven the composite materials industry to increasingly adopt natural fibers as a sustainable alternative to synthetic ones, aiming to reduce the ecological footprint of manufacturing processes. Replacing conventional synthetic fibers with natural counterparts, such as flax, offers a significant decrease in energy consumption during laminate production, enhancing overall process sustainability. This study explores the mechanical performance of a flax fiber-reinforced epoxy laminate with a twill weave configuration. The material was tested under tensile and in-plane shear loading at both room temperature and subzero conditions (–40 °C and –70 °C). The laminate demonstrated a nonlinear stress–strain response, characterized by three distinct regions, suggesting that a trilinear model may be appropriate for its numerical simulation. The influence of temperature on the mechanical behavior was assessed in both the longitudinal and transverse tensile directions, as well as in shear. Results reveal that lower temperatures lead to increased stiffness and strength, although differences between –40 °C and –70 °C were not substantial
Compensation of thermal and mechanical effects of a Lamb-Wave based SHM system in a composite aerostructure.
No full textStructural Health Monitoring has become one of the next challenges in the aeronautical industry in order to know the real-time status of structures and optimize their maintenance. The use of Lamb Waves for damage detection is proven; however, its effectiveness is affected by the boundary conditions in which the structure is located. To counteract this effect, it is essential to model its behavior in different environmental and operating conditions to ensure the correct detection of variations in the structure during its service life. This work presents the characterization of the Lamb wave behavior for a composite UAV supporting surface. By applying different load and temperature conditions to the structure, different conditions of the structure during flight will be simulated. The results obtained for the newly fabricated structure will be compared with the results obtained for the same structure damaged by impact. And finally, the main effects produced on the signal (arrival time, amplitude...) will be analyzed; and how they can simulate the presence of a damage that has not occurred (false positive) or camouflage the presence of existing damage (false negative)
An Integrated IVHFS and DEMATEL-ANP Framework for Competitive Intelligence Evaluation in Smart Factories
No full textIn the era of big data, the ability to evaluate high-quality and actionable competitive intelligence (CI) has become essential for smart factories to support data-driven decision-making and maintain technological and operational advantages. However, the highly dynamic and complex nature of the smart manufacturing environment introduces considerable uncertainty, hesitation, and interdependencies among evaluation indicators, posing significant challenges to traditional decision-making frameworks. To address these issues, this study proposes an integrated framework that combines interval-valued hesitant fuzzy sets (IVHFS) with the decision-making trial and evaluation laboratory-analytic network process (DEMATEL-ANP). IVHFS is employed to capture the ambiguity and hesitation inherent in expert judgments, enabling a more flexible and realistic representation of evaluation inputs. Subsequently, the DEMATEL-ANP approach is used to uncover the causal relationships among CI indicators and to construct a network-based weighting structure that reflects their interdependencies. A case study in a smart factory is conducted to validate the practicality and effectiveness of the proposed framework, and a sensitivity analysis confirmed its stability.OPEN ACCESS Received: 04/08/2025 Accepted: 28/10/2025 Published: 23/01/202
Efficient Analytic Technique for Fractional Fourth-Order Cubic Nonlinear Schrodinger Equation
Full text linkThis paper presents an advanced analytical solution for the fractional fourth-order dispersive cubic nonlinear Schrödinger equation (DNLS), a model significant for engineering applications in optical fiber systems, quantum mechanics, and plasma physics. This work leverages the qhomotopy analysis transform method (q-HATM) to address the challenges in modeling complex, nonlinear wave propagation in engineering and physics applications involving fractional dynamics. By providing highly accurate, convergent solutions, this method allows engineers and scientists to model memory effects and higher-order dispersions more effectively in systems like optical waveguides and plasma waves. The demonstrated accuracy and convergence of q-HATM establish it as a practical tool for researchers aiming to solve complex wave propagation problems, advancing both theoretical understanding and real-world engineering solutions in nonlinear optics, quantum fields, and other areas requiring precise modeling of wave interactions.OPEN ACCESS Received: 25/10/2024 Accepted: 05/03/2025 Published: 20/04/202
