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Impact of digital transformation for sustainable circular economic environments

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Carbon Neutral Systems

Carbon Neutral Systems 2025年第1期1卷 1-22页

作者： Möller, Dietmar P. F. Wang, Qichen Huang, Liangchao Xiong, Bin Guo, Yilin Shi, Tianle Zhang, Ru Department of Applied Stochastics and Operations Research Clausthal Zellerfeld Germany Sino-German Research Institute of Carbon Neutralization and Green Development Zhengzhou University Zhengzhou China Institute of Subsurface Energy Systems Clausthal University of Technology Clausthal Zellerfeld Germany Research Centre of Energy Storage Technologies Clausthal University of Technology Goslar Germany Faculty of Management and Economics Kunming University of Science and Technology Kunming China China ASEAN Research Centre Kunming University of Science and Technology Kunming China State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering Sichuan University Chengdu China

Digital Transformation is a global phenomenon, capturing the attention in every industry and spurring major investment. However, digital transformation is not a single objective, but a multi-faceted approach depending on specific industrial goals and their digital maturity. Digital transformation is the way of change from a monolithic business approach to fully digitalized business strategies, resulting in digital transformation a key economic objective and a major success factor of a comprehensive corporate transformation. In addition to digital technologies, processes, and procedures, and aside from that also skilled employees required keeping pace in reacting quickly to changes in order to adapt to necessary needs and opportunities in the markets, to drive growth and innovation. In the first of the four industrial evolutions, steam power was a disruptive technology that has changed the working capabilities. In the second it was the assembly line and in the third the computer. Today, in the fourth industrial evolution, everything is digital. intelligent digital technologies such as Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), Big Data and Analytics, Automated Robots, and others are accelerating a profound transformation in the way industries work and do business. However, beside digital transformation a second transformation narrative occur, which refers to the circularity of business models, based on the foundation, that sustainability, and specifically circularity is a path to an environmentally friendly production and profitability. This business model is not yet widespread everywhere, but economic, social, and technological changes are enabling circularity be enticing. Given this fact, circular economy makes sense not only from an environmental perspective, it’s also likely the way forward to the central goal of waste reduction in production, and also initiating a prosperous environmentally friendly business strategy. In this

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Failure analysis of the ‘backfill column - Immediate roof’ cooperative bearing structure in constructional backfill coal mining

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Science of the Total Environment 2025年 971卷 179022页

作者： Du, Xianjie Huang, Qipeng Feng, Guorui Zhang, Min Liu, Wenhao College of Mining Engineering Taiyuan University of Technology Taiyuan030024 China Shanxi Province Coal-based Resources Green and High-efficiency Development Engineering Center Taiyuan030024 China Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering Taiyuan030024 China State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering Xuzhou221116 China Co. Ltd Zhengzhou450007 China Shanxi Energy College Jinzhong030604 China College of Civil Engineering Taiyuan University of Technology Taiyuan030024 China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd Shandong 250101 China

The present article introduces a method called Constructional Backfill Coal Mining (CBCM) for the purpose of minimizing the expenses of backfill mining and making use of the subterranean space in coal mines. Investigations are made on the failure properties of the cooperative bearing structure, which consists of a backfill column-immediate roof in CBCM. For the analysis of the fracturing characteristics of the immediate roof, a mechanical model of the thick circular plate on elastic foundation is developed. Furthermore, numerical simulation experiments have been carried out to study the manifestations for failure of backfill columns. In the case study of Xinyang Mine, the findings reveal that the critical spacing of CBCM with a broken immediate roof, which contains a backfill rate of 20 % and a backfill column strength of 20.36 MPa, is 2.58 m. The direct roof is prone to tensile fracture when the backfill spacing exceeds 3 m. When the aspect ratio of the backfill columns is large, stress concentration occurs at the top and the underside corners of the column, resulting in the constitution of an elastic core in the middle of the backfill column. The critical aspect ratio for the formation of elastic core in backfill columns obtained from experiments is 0.8. This research can contribute to the application and promotion of CBCM in coal mining while it serves as a theoretical basis. © 2025

关键词： Aspect ratio

An Explicit Method to Calculate the Stress Intensity Factor of Barbed Suture with Mode I Crack Under Arbitrary Stress Distribution

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SSRN 2024年

作者： Xia, Weihai Dou, Guijing Wang, Yuxuan Chen, Peijian Pu, Jian Peng, Guangjian Zhang, Taihua College of Mechanical Engineering Zhejiang University of Technology Hangzhou310014 China School of Intelligent Manufacturing Huzhou College Huzhou313000 China State Key Laboratory for Geomechanics and Deep Underground Engineering School of Mechanics and Civil Engineering China University of Mining and Technology Xuzhou221116 China General Surgery Department Shizhong District People's Hospital Leshan614000 China Institute of Solid Mechanics Beihang University Beijing100191 China

The processing of barbs in sutures introduces cracks, reducing the fracture resistance of the barbed sutures. Obtaining stress intensity factor (SIF) is pivotal for the optimal design and safe usage of barbed sutures. In this study, an explicit method was proposed to calculate the SIFs for barbed suture with Mode I crack under arbitrary stress distribution. The barbed suture was modeled as a round bar with different shapes of Mode I cracks. The shape coefficient, which was defined to describe the shape of crack, was computed using the point load weight function. Based on these shape coefficients, the basic stress intensity factors (BSIFs) for cracks under basic stress distributions, such as uniform, linear, and quadratic stress distributions, were determined. Then, the SIFs under arbitrary stress distributions were calculated through linear superposition of these BSIFs according to the corresponding stress distribution. The relative errors between the SIFs calculated by this method and the finite element are commonly within ±8%. This demonstrates that the proposed explicit method is capable of directly and accurately calculating SIFs for round bars with Mode I cracks under arbitrary stress distributions, thereby avoiding the time-consuming processes of finite element analysis and numerical integration. © 2024, The Authors. All rights reserved.

关键词： Stress concentration

Numerical Simulation Analysis of Limestone Mechanical Properties Based on the Novel Igbm Using Pfc3d

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SSRN 2024年

作者： Song, Zi-Ming Meng, Qing-Bin Pu, Hai Wu, Jiang-Yu Liu, Jiang-Feng State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering China University of Mining and Technology Xuzhou221116 China School of Mechanics and Civil Engineering China University of Mining and Technology Xuzhou221116 China Department of Materials University of Oxford Parks Road OxfordOX1 3PH United Kingdom Xinjiang Key Laboratory of Coal-bearing Resources Exploration and Exploitation Xinjiang Institute of Engineering Urumqi830023 China

The mechanical properties of granular rocks are intrinsically linked to the heterogeneity of mineral grains. In this study, an improved three-dimensional (3D) equivalent grain-based model (GBM), i.e PFC3D-IGBM, was introduced, which was developed using Particle Flow Code (PFC). The IGBM adopts a hybrid contact model and is capable of classifying mineral grains and the refilling of diverse mineral particles on a 3D scale. Triaxial compression numerical simulations were performed to explore how confining pressure and mineral heterogeneity (characterized by spatial distribution, volume fraction, geometric size, and boundary strength of mineral grains) affect the fracture behavior and mechanical properties of limestone. The results show that force chains form at particle contacts, acting as load-bearing paths that distribute the external load. The orientation of these chains remains uniform, irrespective of the load magnitude. The ratio of high-strength force chains to all force chains (RHF/WF) and the ratio of high-strength force chains to microcracks (RHF/c) serve as quantitative indicators of bearing capacity and fracture resistance. At lower confining pressure (σ3≤10 MPa), the specimens exhibit low peak strength and increased brittleness, with failure typically occurring along a single macroscopic fracture plane. As σ3 increases, the formation of "X" -shaped failure becomes more pronounced. At the grain scale, a higher dolomite volume promotes the formation of more high-strength force chains within the specimen, along with an increase in Tran-D-cs at peak stress. As more Tran-D-cs require higher concentrated stress, this leads to an 13% increase in σp. Moreover, stronger grain boundary strength raises the stress concentration required for fracture by altering the proportion of Trans-c, resulting in a 70 MPa increase in σp. Larger grain sizes, in turn, accommodate more force chains, requiring higher σp for fracture. © 2024, The Authors. All rights reserved.

关键词： Limestone

Sparse data-driven knowledge discovery for interpretable prediction of permeability in tight sandstones

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engineering Geology 2025年 353卷

作者： Xu, Lulu Du, Zhengyang Cai, Meifeng Yin, Shangxian Dong, Shuning Thanh, Hung Vo Carroll, Kenneth C. Soltanian, Mohamad Reza Dai, Zhenxue College of Construction Engineering Jilin University State Key Laboratory of Deep Earth Exploration and Imaging Changchun China Institute of Intelligent Simulation and Early Warning for Subsurface Environment Jilin University Changchun130026 China School of Civil and Resource Engineering University of Science and Technology Beijing Beijing100083 China College of Safety Engineering North China Institute of Science and Technology Langfang065201 China Xi'an Research Institute Co. Ltd. China Coal Technology and Engineering Group Corp Xi'an710077 China Waseda Research Institute for Science and Engineering Waseda University 3-4-1 Okubo Tokyo Shinjuku-ku169-8555 Viet Nam Department of Plant and Environmental Sciences New Mexico State University Las CrucesNM88003 United States Department of Geosciences University of Cincinnati CincinnatiOH45220 United States Department of Environmental Engineering University of Cincinnati CincinnatiOH45220 United States School of Environmental and Municipal Engineering Qingdao University of Technology Qingdao266033 China

Permeability (k) is crucial for subsurface fluid flow, but predicting k-values in tight sandstones remains challenging due to their complex pore structure and heterogeneity. Although machine learning (ML) has shown promise, it faces significant challenges, including limited high-quality data, high computational costs, and unclear prediction mechanisms. This study proposes a sparse data-driven knowledge discovery framework aimed at enhancing the accuracy and interpretability of k-value predictions in tight sandstone formations. We integrate ML models with data augmentation (ML-DA), using Extreme Gradient Boosting (XGBoost-DA) and Least Squares Support Vector Regression (LSSVR-DA), optimized through genetic algorithms (GA), particle swarm optimization (PSO), and Bayesian optimization (BO). SHapley Additive Explanations (SHAP) are employed to elucidate the interactions between key factors influencing predictions. Monte Carlo simulations demonstrate the robust performance of our ML-DA models, even under data constraints. SHAP analysis identifies key predictors, including porosity, displacement pressure, median pore throat radius, median pressure, and carbonate content. Partial dependence plots (PDPs) reveal a significant interaction between porosity and carbonate content, as well as a decrease in model stability at low carbonate content. This study presents an interpretable ML framework with data augmentation, enabling improved predictions from sparse data while exploring the interactions between key factors. The framework can be adapted to other domains facing similar challenges, enhancing the accuracy and transparency of model predictions. © 2025 Elsevier B.V.

关键词： Data augmentation Explainable AI Hybrid intelligence Optimization algorithms Permeability prediction Tight sandstones

Degradation Behavior of Concrete Under the Compound Effects of Acid-Salt Mist and Load

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SSRN 2023年

作者： Wu, Yuanzhou Zhang, Jie Tang, Peng Lv, Henglin Huang, Tianhua Wang, Hui State Key Laboratory of Intelligent Construction and Health Operation Maintenance of Deep Underground Engineering China University of Mining and Technology Xuzhou221116 China School of Mechanics and Civil Engineering China University of Mining and Technology Xuzhou221116 China Jiangsu Key Laboratory of Environment Impact and Structure Safety in Engineering Jiangsu Vocational Institute of Architectural Technology Xuzhou221116 China Xuzhou Housing Expropriation and Property Security Management Center Xuzhou221000 China

The effect of the compound effects of acid-salt mist and load on the performance of concrete was experimentally studied, and the variation of the neutralization depth and compressive strength of concrete with the degree of damage was revealed. The results showed that with the increase of the deterioration period, the neutralization depth of concrete first presented the trend of "fast and slow alternation" and then continued to increase rapidly. The compressive strength of concrete increased slightly at first and then decreased rapidly with the increase of the deterioration time. The degradation rate of concrete performance under the coupling of environment and load was faster than that under the environmental effect only. The thickness of the damage concrete layer increased with the increase of the deterioration time. A functional relationship between the damage degree D of concrete and the deterioration time was established. © 2023, The Authors. All rights reserved.

关键词： Deterioration

Thermal Conductivity of Sands (Ⅲ)

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SSRN 2024年

作者： Wang, Yiming Chu, Zhaoxiang Li, Xiaozhao Gu, Xiaobin Jia, Guosheng School of Mechanics & Civil Engineering China University of Mining & Technology Xuzhou221116 China State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering Xuzhou221116 China Yun Long Lake Laboratory Xuzhou221116 China Guangzhou Institute of Energy Conversion Chinese Academy of Science Guangdong Guangzhou510640 China School of Human Settlements and Civil Engineering Xi'an Jiaotong University Shaanxi Xi'An710049 China

This paper provides a novel ortho-triangular prism unit cell model to estimate the effective thermal conductivity (ETC) of a porous or granular geomaterial, particularly of sands. At the meso-scale, the liquid phase in sands is modelled as capillary water while the geometry of the solid particle and gas phase are respectively described as quasi-sphere and residual space-filling bodies embedded in a trigonal prismatic unit cell. Following the homogenization theory of micro-mechanics, the model is physically and mathematically established using the lumped parameter thermo-electric analogy method, leading to an explicit formula for the ETC of geomaterials. This new model overcomes the inherent spatial correction defects of the spherical and cylindrical unit cell used in previous upscaling models, and extends the applicability to a porosity of 0~0.6 and to unsaturated to fully-saturated media, which covers most natural soil-rock media (with void ratios up to 1.5). To validate the proposed model, predicted values for sands’ ETC are compared with predictions from three kinds of previously developed models (hybrid model, cell model and empirical model), and with experimental values of five samples under different conditions. The results indicate that the error of current model is significantly lower than those of previous unit cell models, in which the root mean squared error and normalized root mean squared error are respectively 0.459 W/(m•K) and 14.4%, very close to Lu et al.’s empirical model (0.402 W/(m•K) and 12.6%). © 2024, The Authors. All rights reserved.

关键词： Porosity

Calculation model for kinetic energy and rock burst risk evaluation method during roadway excavation

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International Journal of Mining Science and technology 2025年第5期 677-690页

作者： Yunliang Tan Yan Tan Weiyao Guo Bo Li Shudong He Lei Zhang Yujiang Zhang Qiuyuan Zhang College of Energy and Mining Engineering Shandong University of Science and Technology Shandong Key Laboratory of Intelligent Prevention and Control of Dynamic Disaster in Deep Mines Shandong University of Science and Technology School of Civil Engineering Tongji University China Coal Technology&Engineering Group Chongqing Research Institute College of Coal Engineering Shanxi Datong University College of Mining Engineering Taiyuan University of Technology Yuanbaoshan Open-pit Coal Mine Inner Mongolia Pingzhuang Coal Group Co.Ltd.

The accumulation and release of deformation energy within the rock mass of a roadway are primary contributors to the occurrence of rock bursts. This study introduces a calculation model for the kinetic energy generated during roadway excavation, which is based on the fracture and energy states of the rock *** relationships among the mining depth, width of the plastic zone, rebound range of the roof and floor,stress concentration factor, and the induced kinetic energy are systematically explored. Furthermore, a rock burst risk evaluation method is proposed. The findings indicate that the energy evolution of the rock mass can be categorized into four stages: energy accumulation due to in-situ stress, energy accumulation resulting from coal compression, energy dissipation through coal plastic deformation, and energy consumption due to coal failure. The energy release from the rock mass is influenced by several factors,including mining depth, stress concentration factor, the width of the plastic zone, and the rebound range of the roof and floor. Within the plastic zone of coal, the energy released per unit volume of coal and the induced kinetic energy exhibit a nonlinear increase with mining depth and stress concentration factor,while they decrease linearly as the width of the plastic zone increases. Similarly, the driving energy per unit volume of the roof and floor shows a nonlinear increase with mining depth and stress concentration factor, a linear increase with the rebound range of the roof and floor, and a linear decrease with the width of the plastic zone. A rock burst risk evaluation method is developed based on the kinetic energy model. Field observations demonstrate that this method aligns with the drilling cuttings rock burst risk assessment method, thereby confirming its validity.

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Radar-Based Swimming Activity Recognition with Temporal Dynamic Convolution and Spectral Data Augmentation

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IEEE Sensors Journal 2024年

作者： Zhou, Yi Yu, Xuliang Lopez-Benitez, Miguel Yu, Limin Yue, Yutao Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China University of Liverpool Department of Electrical Engineering and Electronics LiverpoolL69 3GJ United Kingdom Antonio de Nebrija University Aries Research Centre Madrid28040 Spain Xi'an Jiaotong-Liverpool University School of Advanced Technology Department of Electrical and Electronic Engineering Suzhou215123 China Jitri Institute of Deep Perception Technology Wuxi214000 China Xi'an Jiaotong-Liverpool University XJTLU-JITRI Academy of Industrial Technology Suzhou215123 China Thrust of Artificial Intelligence and Thrust of Intelligent Transportation Guangzhou511400 China

Radar-based human activity recognition (HAR) is a popular area of research. In this paper, we investigate methods to improve the generalization of micro-Doppler-based swimming activity recognition. We identify three main challenges to this task: a small dataset lacking motion diversity, inaccurate period estimation, and inefficient network design that does not take into account the unique characteristics of spectrograms. To address the limited motion diversity, we propose a spectral data augmentation tailored for micro-Doppler spectrograms, including positive augmentations that account for physical fidelity and negative augmentations that penalize the unrealistic examples. We also investigate self-supervised pre-training to effectively use these negative augmentations. To address inaccurate period estimation, we introduce a segmentation approach based on energy distribution to handle temporal period variation. To exploit the spreading pattern of limb motion in the Doppler dimension and the continuous properties of torso motion in the temporal dimension, we design a module consisting of both 2D convolution and 1D temporal dynamic convolution to serve as a feature extractor. Our evaluation on a self-collected swimming activity recognition dataset shows that our model achieves the best classification accuracy and robustness to corruptions, even compared to much larger models and multi-domain fusion models. © 2001-2012 IEEE.

关键词： Spectrographs