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Experimental study on failure precursory characteristics and moisture content effect of pre-cracked rocks under graded cyclic loading and unloading

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International Journal of Mining Science and Technology 2025年第2期35卷 249-264页

作者： Wei Zhang Dongxiao Zhang Weiyao Guo Baoliang Zhang School of Architecture and Engineering Liaocheng UniversityLiaocheng 252000China College of Energy and Mining Engineering Shandong University of Science and TechnologyQingdao 266590China

It is important to analyze the damage evolution process of surrounding rock under different water content for the stability of engineering rock *** on digital speckle correlation(DSCM),acoustic emission(AE)and electromagnetic radiation(EMR),uniaxial hierarchical cyclic loading and unloading tests were carried out on sandstones with different fracture numbers under dry,natural and saturated water content,to explore the fracture propagation,failure precursor characteristics and damage response mechanism under the influence of water content *** results show that with the increase of water content,the peak stress and crack initiation stress decrease gradually,and the decreases are 15.28%-21.11%and 17.64%-23.04%,*** peak strain and crack initiation strain increase gradually,and the increases are 19.85%-44.53%and 19.15%-41.94%,*** precracked rock with different water content is mainly characterized by tensile failure at different loading ***,with the increase of water content,the proportion of shear cracks gradually increases,while acoustic emission events gradually decrease,the dissipative energy and energy storage limits of the rock under peak load gradually decrease,and the charge signal increases significantly,which is because the lubrication effect of water reduces the friction coefficient between crack surfaces.

关键词： Damage mechanisms Pre-cracked rocks Crack propagation Water-rock interaction Graded cyclic loading and unloading

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Simultaneous imposition of initial and boundary conditions via decoupled physics-informed neural networks for solving initialboundary value problems

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Applied Mathematics and Mechanics(English Edition) 2025年第4期46卷 763-780页

作者： K.A.LUONG M.A.WAHAB J.H.LEE Deep Learning Architecture Research Center Sejong UniversitySeoul05006Republic of Korea Soete Laboratory Department of Electrical EnergyMetalsMechanical Constructionsand SystemsFaculty of Engineering and ArchitectureGhent UniversityGent9000Belgium College of Engineering Yuan Ze UniversityTaoyuan32026TaiwanChina

Enforcing initial and boundary conditions(I/BCs)poses challenges in physics-informed neural networks(PINNs).Several PINN studies have gained significant achievements in developing techniques for imposing BCs in static problems;however,the simultaneous enforcement of I/BCs in dynamic problems remains *** overcome this limitation,a novel approach called decoupled physics-informed neural network(d PINN)is proposed in this *** d PINN operates based on the core idea of converting a partial differential equation(PDE)to a system of ordinary differential equations(ODEs)via the space-time decoupled *** this end,the latent solution is expressed in the form of a linear combination of approximation functions and coefficients,where approximation functions are admissible and coefficients are unknowns of time that must be ***,the system of ODEs is obtained by implementing the weighted-residual form of the original PDE over the spatial domain.A multi-network structure is used to parameterize the set of coefficient functions,and the loss function of d PINN is established based on minimizing the residuals of the gained *** this scheme,the decoupled formulation leads to the independent handling of I/***,the BCs are automatically satisfied based on suitable selections of admissible ***,the original ICs are replaced by the Galerkin form of the ICs concerning unknown coefficients,and the neural network(NN)outputs are modified to satisfy the gained *** benchmark problems involving different types of PDEs and I/BCs are used to demonstrate the superior performance of d PINN compared with regular PINN in terms of solution accuracy and computational cost.

关键词： decoupled physics-informed neural network(dPINN) decoupled formulation Galerkin method initial-boundary value problem(IBVP) machine learning

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Integrating machine learning in digital architecture: enhancing sustainable design and energy efficiency in urban environments

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Asian Journal of Civil engineering 2025年第2期26卷 813-827页

作者： Abu-Shaikha, Ma’in F. Al-Karablieh, Mutasem A. Musa, Akram M. Almashayikh, Maryam I. Al-Abed, Razan Y. Department of Architecture Engineering College of Engineering Amman Arab University Amman Jordan Visual Art Department School of Arts and Design The University of Jordan Amman Jordan Department of Renewable Energy Engineering College of Engineering Amman Arab University Amman Jordan

The following work applies metaheuristic optimization algorithms—PSO, ACO, Genetic Algorithm, and Enhanced Colliding Bodies Optimization (ECBO)—to the optimum design of a sustainable building with respect to prominent metrics such as energy savings, improvement in indoor comfort, and reduction in carbon footprint. These algorithms are applied to a wide dataset that includes variable intensity factors such as window-to-wall variation ratio, HVAC efficiency, and integration of renewable energy. Results also proved that PSO is the fittest strategy to balance energy efficiency and sustainability, with the highest energy savings of 24.1%. Besides, PSO wasn’t just the fastest convergence rate;it also obtained a Platinum LEED certification. ACO was second in order of magnitude, with high energy savings and carbon footprint reduction values, and also obtained the Platinum LEED certificate. The results obtained for GA were positive from the occupant comfort point of view but were slower in terms of energy savings and convergence speed. In contrast, ECBO had the slowest convergence and lowest energy savings, demonstrating the limitation of the application of ECBO for large-scale multi-objective optimization. These results imply that PSO and ACO would be suitable for practical applications linked to urban sustainable design, while GA and ECBO are more suited for niche applications. The obtained results can provide useful guidelines in developing more energy-efficient and sustainable designs for architects, urban planners, and policymakers. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Carbon footprint reduction energy efficiency Metaheuristic algorithms Occupant comfort optimization Sustainable building design

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Dynamic anaerobic digestion-based biorefineries for on-demand renewable energy and bioproducts in a circular bioeconomy

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Trends in Biotechnology 2025年第5期43卷 1140-1165页

作者： Shinde, Rajas Hackula, Anga Marycz, Milena Bose, Archishman O'Shea, Richard Barth, Susanne Murphy, Jerry D. Wall, David M. SFI MaREI Centre for Energy Climate and Marine Environmental Research Institute University College Cork Cork Ireland Civil Structural and Environmental Engineering School of Engineering and Architecture University College Cork Cork Ireland Crops Environment and Land Use Programme Crop Science Department Teagasc Oak Park Ireland Faculty of Electronics Telecommunications and Informatics and Advanced Materials Centre Gdansk University of Technology Gdańsk Poland Process and Chemical Engineering School of Engineering and Architecture University College Cork Cork Ireland

Anaerobic digestion (AD) is an important biotechnology for treating biodegradable residues and producing bioenergy, yet its full potential remains untapped. We investigate a two-phase AD system for biorefinery applications, producing valuable bioproducts, such as volatile fatty acids (VFAs) and biogas, from grass feedstock. We introduce a demand-driven operational approach to match market conditions, while minimising water use by reusing the process effluent. The proposed biorefinery model yields ~23 kg of VFAs and 75 kWh of biogas, with a potential gross revenue of €84 per tonne of grass. However, a preliminary economic analysis indicates that this biorefinery model is currently unprofitable. A sensitivity analysis suggests that reducing operating costs through technology advancements and policy support are vital to ensure economic viability. Such biorefineries offer opportunities for the diversification of farmers' incomes and the transition away from fossil resources. Our work exemplifies the role of AD as a key biotechnology in the circular bioeconomy. © 2025 The Author(s)

关键词： Sensitivity analysis

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Optimizing waste cooking biodiesel production using ultrasonic-assisted and studying its combustion characteristics blended with diesel in diesel engine

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Environmental Science and Pollution Research 2025年第11期32卷 6984-7001页

作者： Belal, Belal Y. Li, Gesheng Zhang, Zunhua Liang, Junjie Zhou, Mengni Masoud, Shaimaa M. Attia, Ali M. A. El-Zoheiry, Radwan M. El-Seesy, Ahmed I. School of Naval Architecture Ocean and Energy Power Engineering Wuhan University of Technology Hubei Wuhan430063 China Mechanical and Nuclear Engineering Department College of Engineering and Physical Sciences Khalifa University Abu Dhabi127788 United Arab Emirates Mechanical Engineering Department College of Engineering University of Bisha Bisha Saudi Arabia Mechanical Engineering Department Benha Faculty of Engineering Benha University Qalubia Benha Egypt

This study aimed to optimize biodiesel production from waste cooking oil using ultrasonic-assisted transesterification and evaluate the combustion characteristics of a diesel engine powered by various biodiesel blends. The effects of transesterification parameters, including reaction time, alcohol-to-oil molar ratio, catalyst concentration, and ultrasonic amplitude, were experimentally investigated. The optimal conditions for achieving a biodiesel yield of 96.65% were found to be a reaction time of 6 min, an alcohol-to-oil molar ratio of 6:1, a catalyst concentration of 1.0 wt.%, and an ultrasonic amplitude of 75% with a duty cycle of 0.7. In the subsequent phase, engine performance and emissions were evaluated for biodiesel–diesel blends at volume ratios of 0:100 (B0), 10:90 (B10), 20:80 (B20), 30:70 (B30), 40:60 (B40), and 100:0 (B100) under varying load conditions at a constant speed of 1500 rpm. The results indicated that biodiesel blends exhibited similar engine performance to diesel, with a slight increase in brake-specific fuel consumption and a minor decrease in brake thermal efficiency. Emission analysis revealed significant improvements, with B100 reducing carbon monoxide (CO), unburned hydrocarbons (HC), and smoke opacity by 42.9%, 29.9%, and 42.1%, respectively, compared to B0. Notably, B40 showed the highest reduction in nitrogen oxide (NOx) emissions, achieving a 4.94% decrease. These findings suggested that B40 can serve as a viable diesel substitute without requiring fuel system modifications, offering comparable mechanical performance and enhanced emission characteristics. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Waste incineration

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A seaweed-inspired separator for high performance Zn metal batteries:Boosting kinetics and confining side-reactions

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Journal of energy Chemistry 2025年第2期101卷 191-200,I0005页

作者： Qianzhi Gou Horan Luo Long Qu Feilin Yu Kaixin Wang Sida Zhang Ziga Luogu Ben Zhang Yujie Zheng Bingye Song John Wang Meng Li National Innovation Center for Industry-Education Integration of Energy Storage MOE Key Laboratory of Low-grade Energy Utilization Technologies and SystemsCQU-NUS Renewable Energy Materials&Devices Joint LaboratoryCollege of Energy&Power EngineeringChongqing UniversityChongqing 400044China Schoolof Building Services Science and Engineering Xi'an University of Architecture and TechnologyXi'an 710055ShaanxiChina School of Chemistry and Chemical Engineering Chongqing University of Science and TechnologyChongqing 401331China Southwest Technology and Engineering Research Institute Chongqing 400039China National University of Singapore(Chongqing)Research Institute Chongqing 401123China

Uncontrolled dendrite growth,sluggish reaction kinetics,and drastic side reactions on the anodeelectrolyte interface are the main obstacles that restrict the application prospect of aqueous zinc-ion *** glass fiber(GF)separator with chemical inertness is almost ineffective in restricting these ***,inspired by the ionic enrichment behavior of seaweed plants,a facile biomass species,anionic sodium alginate(SA),is purposely decorated on the commercial GF separator to tackle these issues towards Zn *** from the abundant zincophilic functional groups and superior mechanical strength properties,the as-obtained SA@GF separator could act as ion pump to boost the Zn^(2+)transference number(0.68),reduce the de-solvation energy barrier of hydrated Zn^(2+),and eliminate the undesired concentration polarization effect,which are verified by experimental tests,theoretical calculations,and finite element simulation,*** on these efficient modulation mechanisms,the SA@GF separator can synchronously achieve well-aligned Zn deposition and the suppression of parasitic ***,the Zn‖Zn coin cell integrated with SA@GF separator could yield a prolonged calendar lifespan over 1230 h(1 mA cm^(-2)and 1 mAh cm^(-2)),exhibiting favorable competitiveness with previously reported separator modification ***,the Zn-MnO_(2)full and pouch cell assembled with the SA@GF separator also delivered superior cycling stability and rate performance,further verifying its practical application *** work provides a new design philosophy to stabilize the Zn anode from the aspect of separator.

关键词： Aqueous zinc-ionbatteries Separator modification Sodium alginate Dendrite-free Interfacial reaction kinetics

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DTST-MFN: Enhancing Anomalous Sound Detection with Deep Temporal Features and Efficient Attention

DTST-MFN: Enhancing Anomalous Sound Detection with Deep Temp...

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Zhang, Linke Cai, Zhuoran Yu, Yongsheng Zhang, Shiqi Wei, Na School of Naval Architecture Ocean and Energy Power Engineering Wuhan University of Technology Wuhan430070 China State Key Laboratory of Silicate Materials for Architecture Wuhan University of Technology Wuhan430070 China College of Electronic Engineering Naval University of Engineering Wuhan430070 China

ISBN: (纸本)9783031734069

In the wake of deep learning's rapid development, the realm of machine anomalous sound detection has notably advanced through self-supervised learning techniques. Despite significant progress, existing models display deficiencies in adequately utilizing deep temporal audio features. To address this shortfall, this study proposes Deep-TgramNet, a novel feature extraction framework that, by markedly increasing the convolutional depth and widening the receptive field, achieves the effective utilization of temporal features neglected by existing models, thereby significantly enhancing the model's ability to discern between normal and abnormal samples. In addition, we developed the DTST-MFN framework by integrating the Deep-TgramNet, STgram-MFN, and efficient attention mechanisms. DTST-MFN not only fully extracts temporal features but also focuses on key features in the temporal and frequency domains that are beneficial for anomalous sound detection, and adaptively adjusts the weights between channels. Experiments on the DCASE2020 Task 2 dataset demonstrate DTST-MFN's superiority in anomaly sound detection, achieving an average AUC of 94.06% and pAUC of 88.79%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Acoustic Fault Identification in Maritime Vessels with Limited Data Using WGAN-Based Approach

Acoustic Fault Identification in Maritime Vessels with Limit...

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Wei, Na Zhang, Xian Liu, Yuxiu Cai, Zhuoran Yu, Yongsheng College of Electronic Engineering Naval University of Engineering Wuhan430033 China School of Naval Architecture Ocean and Energy Power Engineering Wuhan University of Technology Wuhan430070 China State Key Laboratory of Silicate Materials for Architecture Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9783031734069

In the practical realm of acoustic fault identification in maritime vessels, the predicament stems from the limited availability of actual test fault data and the associated high testing costs, leading to a small sample identification problem. To address this issue, sample expansion emerges as a feasible approach to amplify the identification rate of fault sources in scenarios with limited samples. This study is centered on the utilization of a generative adversarial network (GAN) model, Wasserstein GAN (WGAN), to generate pseudo acoustic data on two-dimensional time-frequency maps, thereby supplementing insufficient fault source samples originating from the actual small test data. The efficacy of proposed data augmentation is confirmed through the application of t-SNE method. Subsequently, a CNN model is trained by mixing the generated fault samples with real samples, and this model is employed to conduct fault identification experiments. The experiments demonstrate that the augmented dataset contributes to an enhanced fault source identification rate and effectively mitigates the adverse influence of limited sample data on the identification outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Acoustic Fault Identification Generative Adversarial Network Sample Expansion Small Sample Wasserstein GAN

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Quantitative Evaluation of Rotational Wood Welding Joint Strength Based on Regression of Data Sets

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BioResources 2025年第2期20卷 2933-2948页

作者： Xu, Yun Wang, Xuejiao Chernykh, Aleksandr G. Svetlana, Roshchina I. Koval, Pavel S. Danilov, Egor V. Naichuk, Anatoly Y. School of Civil Engineering and Communication North China University of Water Resources and Electric Power Zhengzhou450045 China School of International Education Zhengzhou Railway Vocational and Technical College Zhengzhou451460 China Faculty of Civil Engineering Saint Petersburg State University of Architecture and Civil Engineering Saint Petersburg190005 Russia Institute of Architecture Construction and Energy Engineering Vladimir State University named after Alexander and Nikolay Stoletovs 87 Gorky Street Vladimir600000 Russia Faculty of Civil Engineering Brest State Technical University Brest224023 Belarus

This study aimed to enhance rotational wood welding technology by developing a simplified prediction model for pull-out strength. The key findings can offer a robust evaluation framework to advance rotational wood welding and expand its applications in woodworking. For instance, (1) A comprehensive database of 689 previously published trials was curated to identify key factors: substrate diameter, effective welded length, and substrate density. (2) Comparative analysis of test outcomes and predictive models revealed consistent trends, suggesting that modeling techniques for self-tapping wood screws could be applied to rotational wood welding joints. (3) Univariate linear regression validated the primary factors, leading to a multivariate model for predicting withdrawal capacity. Theoretical predictions closely matched empirical data, highlighting the model’s industrial applicability. © 2025, North Carolina State University. All rights reserved.

关键词： Woodworking

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Ni-Embedded SiO2 Nanosheet for Reverse Water–Gas Shift Reaction 8th

Ni-Embedded SiO2 Nanosheet for Reverse Water–Gas Shift Reac...

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8th International Conference on Manufacturing, Material and Metallurgical engineering, ICMMME 2024

作者： Zou, Xuhui Xu, Runjing Wang, Yangang Cui, Lifeng School of Environment and Architecture University of Shanghai for Science and Technology Shanghai200093 China College of Biological Chemical Science and Engineering Jiaxing University Jiaxing314001 China College of Smart Energy Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783031782947

The hydrogenation of CO2 to CO through reverse water–gas shift (RWGS) reaction is a process that offers significant potential for future large-scale industrial utilization. Within the scope of this study, a novel catalyst system comprising nickel (Ni) immobilized on silica (SiO2) nanosheets, denoted as Ni@SiO2NS, has been synthesized through an in-situ calcination technique. This method has been demonstrated to significantly augment the conversion of CO2 and the selectivity for CO production in the RWGS reaction. The SiO2 nanosheets has a large surface area and improved the CO2 absorption, served as a suitable platform for anchoring Ni. Notably, the Ni@SiO2NS catalyst with a Ni content of 10 wt% exhibited a higher CO selectivity of 84% at reaction temperature of 500℃, while a low CO selectivity of 41% for impregnation contrast sample Ni/SiO2. The study further revealed that the in-situ calcination method facilitated a strong bond between Ni and the SiO2 nanosheets, enhancing the dispersion of Ni particles and strong metal–support interaction. This, in turn, contributed to the improved activity of the catalyst in the RWGS, while simultaneously preventing the deactivation of Ni through sintering during the reaction process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Sintering

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