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In-situ monitoring of layer-wise process quality and signal analysis for laser powder bed fusion using multi-source optical signal 认领 引用
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作者 Di Wang Tao Tang +10 位作者 Tingyi Wang Renwu Jiang Xiaoqiang Zheng Long Zhou Laizhu Chen Wenlong Chen Pan Wang Zhiguang Zhou Ying Ma Yongqiang Yang Linqing Liu 《Additive Manufacturing Frontiers》 CAS CSCD 2026年第2期59-77,共19页
Laser powder bed fusion is a key metal additive manufacturing technology capable of fabricating geometrically complex parts,yet its reliable industrial adoption is hindered by the inherent complexity and stochastic de... Laser powder bed fusion is a key metal additive manufacturing technology capable of fabricating geometrically complex parts,yet its reliable industrial adoption is hindered by the inherent complexity and stochastic defect formation of the process.Current quality assessment is constrained by the inherent latency of offline methods and the diagnostic limitations of single-sensor monitoring.To address these challenges,this study developed a multi-source optical signal monitoring system integrating coaxial photodiodes and an off-axis industrial camera to achieve simultaneous powder spreading detection and radiation signal monitoring during LPBF layer-wise process quality monitoring.Based on the successful identification and analysis of typical detectable features,the YOLOv5s deep learning model was employed to achieve rapid and accurate detection of lack-of-powder defects during the printing process.The training results indicated that the model exhibited good performance metrics.The relationships between process parameters,typical defects,and multi-channel monitoring data were also investigated.The monitoring system achieved a spatial resolution of 300μm for in-process monitoring and demonstrated high accuracy in detecting various defect types,including lack of powder,pores,warping,stitching seams,and printing failures.Furthermore,the algorithm-detected signal anomalies exhibited good spatial correlation with the actual surface defects.Simultaneously,wavelet time-frequency analysis was employed to evaluate molten pool dynamic stability under different process parameters and to analyze energy distribution for different defects.Furthermore,3D model reconstruction from signals enabled effective correlation with actual part defects.Based on the signal-driven process optimization,complex conformal cooling molds were successfully fabricated with a grafting accuracy error of less than 0.12 mm on high-performance substrates,demonstrating the practical efficacy of the developed monitoring methodology.This study provides both a technological and a theoretical foundation for intelligent quality control in LPBF and its practical implementation in industry. 展开更多
关键词 Laser powder bed fusion Multi-source optical signal Signal analysis Layer-wise quality monitoring
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Monitoring of agricultural drought based on multi-source remote sensing data in Heilongjiang Province,China 认领 引用
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作者 Chenfa Jiang Changhui Ma +4 位作者 Sibo Duan Xiaoxiao Min Youzhi Zhang Dandan Li Xia Zhang 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第4期1716-1730,共15页
Agriculture is the foundation of socio-economic development and is highly influenced by weather and climate conditions.Drought is one of the most significant threats to agricultural development and food security.Curre... Agriculture is the foundation of socio-economic development and is highly influenced by weather and climate conditions.Drought is one of the most significant threats to agricultural development and food security.Currently,in-situ drought monitoring based on weather stations and based on remote sensing data has limitations,including infrequent updates,limited coverage,and low accuracy.This study leverages multi-source remote sensing data to monitor agricultural drought in Heilongjiang Province,China.We developed multi-source composite drought indices(MCDIs)at various timescales(3,6,9,and 12 months)by integrating precipitation,land surface temperature,soil moisture,and vegetation indices.Utilizing remote sensing data from various sources,we calculated a series of single drought indices,which are the precipitation condition index,soil moisture condition index,vegetation condition index,and temperature condition index.These are then integrated into MCDIs using a multivariable linear regression approach.The analysis reveals that MCDIs correlate more with standardized precipitation evapotranspiration index(SPEI)than single drought indices.When examining the correlation between different MCDIs and the affected area of crops and major grain production,MCDI-9 showed the highest correlation with the affected area of crops,while MCDI-12 showed the highest correlation with grain production.This suggests that these two MCDIs at different timescales are better indicators of agricultural drought.The spatio-temporal analysis of MCDI indicates that drought in Heilongjiang Province primarily occurs in early spring,gradually spreading from the Greater Khingan Mountains region to the southeastern plains.The drought gradually alleviates during the summer,ending by the autumn harvest period.Therefore,the MCDIs constructed in this study can serve as effective methods and indicators for drought monitoring in Heilongjiang Province and similar regions. 展开更多
关键词 agricultural drought spatio-temporal monitoring multi-source remote sensing data SPEI Heilongjiang Province
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Monitoring track irregularities using multi-source on-board measurement data 认领 引用 被引量：1
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作者 Qinglin Xie Fei Peng +4 位作者 Gongquan Tao Yu Ren Fangbo Liu Jizhong Yang Zefeng Wen 《Railway Engineering Science》 EI 2025年第4期746-765,共20页
Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on co... Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on complex signal processing algorithms and lack multi-source data analysis.Driven by multi-source measurement data,including the axle box,the bogie frame and the carbody accelerations,this paper proposes a track irregularities monitoring network(TIMNet)based on deep learning methods.TIMNet uses the feature extraction capability of convolutional neural networks and the sequence map-ping capability of the long short-term memory model to explore the mapping relationship between vehicle accelerations and track irregularities.The particle swarm optimization algorithm is used to optimize the network parameters,so that both the vertical and lateral track irregularities can be accurately identified in the time and spatial domains.The effectiveness and superiority of the proposed TIMNet is analyzed under different simulation conditions using a vehicle dynamics model.Field tests are conducted to prove the availability of the proposed TIMNet in quantitatively monitoring vertical and lateral track irregularities.Furthermore,comparative tests show that the TIMNet has a better fitting degree and timeliness in monitoring track irregularities(vertical R2 of 0.91,lateral R2 of 0.84 and time cost of 10 ms),compared to other classical regression.The test also proves that the TIMNet has a better anti-interference ability than other regression models. 展开更多
关键词 Track irregularities Vehicle accelerations On-board monitoring Multi-source data Deep learning
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Noninvasive On-Skin Biosensors for Monitoring Diabetes Mellitus 认领 引用 被引量：4
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作者 Ali Sedighi Tianyu Kou +1 位作者 Hui Huang Yi Li 《Nano-Micro Letters》 SCIE EI CAS CSCD 2026年第1期375-437,共63页
Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-in... Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-interfaced biosensors capable of analyzing sweat and skin biomarkers,providing innovative solutions for diabetes diagnosis and monitoring.This review comprehensively discusses the current developments in noninvasive wearable biosensors,emphasizing simultaneous detection of biochemical biomarkers(such as glucose,cortisol,lactate,branched-chain amino acids,and cytokines)and physiological signals(including heart rate,blood pressure,and sweat rate)for accurate,personalized diabetes management.We explore innovations in multimodal sensor design,materials science,biorecognition elements,and integration techniques,highlighting the importance of advanced data analytics,artificial intelligence-driven predictive algorithms,and closed-loop therapeutic systems.Additionally,the review addresses ongoing challenges in biomarker validation,sensor stability,user compliance,data privacy,and regulatory considerations.A holistic,multimodal approach enabled by these next-generation wearable biosensors holds significant potential for improving patient outcomes and facilitating proactive healthcare interventions in diabetes management. 展开更多
关键词 Wearable biosensors Multimodal sensors Diabetes monitoring Sweat biomarkers Glucose biosensors
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Electric charge induction monitoring of deformation and failure behavior of igneous rock:Laboratory test and field application 认领 引用 被引量：2
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作者 Wei Wang Yishan Pan +5 位作者 Hongrui Zhao Yonghui Xiao Xiaoliang Li Xinyang Bao Yan Liu Jinming Wang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第2期861-886,共26页
To advance the theoretical understanding,technological development,and field application of electric charge induction for monitoring rock deformation and failure,this study investigates the induced electric charge gen... To advance the theoretical understanding,technological development,and field application of electric charge induction for monitoring rock deformation and failure,this study investigates the induced electric charge generated during the deformation and failure of igneous rocks.The charge originates mainly from a combination of electrical polarization and triboelectric effects.Through laboratory experiments,we analyzed the time-frequency evolution of induced electric charge signals and identified relevant monitoring parameters.An online downhole electric charge induction monitoring system was developed and validated in the field.Experimental results show that the dominant frequency range of induced electric charge signals generated during igneous rock deformation and failure lies between 0 and 23 Hz,and a low-pass finite impulse response(FIR)filter effectively suppresses noise.Optimal sensor distances for monitoring cubic and cylindrical specimens were determined to be 17 mm and 13 mm,respectively.We proposed early warning indicators,including the maximum absolute value of the induced electric charge,the arithmetic mean value,the distribution dispersion coefficient,and the cumulative sum value.In field application,time-domain curves and spatial distribution charts of these warning indicators correspond well with changes in abutment stress ahead of the mining face,offering indirect insights into local stress evolution.This research provides technical and equipment support for the application of electric charge induction technology to monitoring and early warning of coal bursts. 展开更多
关键词 Time-frequency domain evolution law Noise reduction filtering Electric charge induction monitoring parameters Early warning index Online downhole electric charge induction monitoring system
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Multi-source errors evaluation of machine tools:from research gaps to methodologies and applications 认领 引用 被引量：1
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作者 Jingang Sun Yanbin Zhang +14 位作者 Xiao Ma Benkai Li Min Yang Liandi Xu Haiyuan Xin Qinglong An Lida Zhu Qingfeng Bie Xianxin Yin Shouhai Chen Guanqun Li Yusuf Suleiman Dambatta Rui Xue Zhenwei Yu Changhe Li 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2026年第2期44-96,共53页
Multi-source errors,as critical obstacles limiting the accuracy retention and machining performance of machine tools,hold fundamental and strategic significance for achieving high-precision,high-efficiency,and high-re... Multi-source errors,as critical obstacles limiting the accuracy retention and machining performance of machine tools,hold fundamental and strategic significance for achieving high-precision,high-efficiency,and high-reliability machining in modern manufacturing systems.However,these errors typically exhibit complex characteristics such as strong coupling,time-variance,and nonlinearity,which challenge traditional methods of error identification,modeling,and compensation in terms of adaptability,real-time capability,and integration.Therefore,it is imperative to establish a systematic and intelligent multi-source error control framework.Firstly,this work systematically reviews typical error sources and their evolution mechanisms,evaluates multi-scale detection technologies including laser interferometry,double ball-bar systems,multi-sensor fusion,and vision-based systems,and constructs an intelligent error identification and evaluation framework.Next,it reviews classical modeling methods such as homogeneous transformation matrices,screw theory,thermal equilibrium models,finite element analysis,and modal analysis,compares physical modeling,data-driven,and hybrid modeling strategies,and develops an integrated multi-source error modeling architecture centered on digital twin technology and artificial intelligence.Furthermore,key technologies,including geometric error mapping and real-time compensation,online thermal error prediction and active temperature control,dynamic error suppression,and adaptive control,are summarized.A multi-level integrated error compensation architecture is proposed by combining physical models,data models,and cyber-physical synchronization.This architecture encompasses core processes such as error traceability and decoupling,dynamic prediction,real-time compensation,and closed-loop optimization,emphasizing engineering implementation mechanisms based on cyber-physical collaboration,multi-physics coupling,and multi-scale fusion,thereby effectively enhancing accuracy stability and control robustness under complex operating conditions.Finally,frontier challenges such as constructing high-fidelity coupled models from heterogeneous multi-source data,edge-cloud collaborative control,and cross-platform interoperability are discussed.The application prospects of multi-source error evaluation are also envisioned,providing theoretical foundations and technical support for the precise management and optimization of the entire lifecycle accuracy of machine tools. 展开更多
关键词 multi-source error evaluation error coupling integrated error modeling traceability and decoupling hybrid prediction closed-loop error compensation
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Real-time evaluation of molten-pool dynamic stability in laser-directed energy deposition based on interframe similarity monitoring 认领 引用 被引量：1
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作者 Hui Xiao Mingtao Wu +4 位作者 Yanqin Li Jinhai Wang Wenjia Xiao Kuanfang He Lijun Song 《Additive Manufacturing Frontiers》 CAS CSCD 2026年第2期132-143,共12页
The dynamic stability of the molten pool during laser-directed energy deposition(L-DED)critically affects the forming quality and material properties.However,existing monitoring methods primarily focus on the static g... The dynamic stability of the molten pool during laser-directed energy deposition(L-DED)critically affects the forming quality and material properties.However,existing monitoring methods primarily focus on the static geometric features of the molten pool,such as width,height,area,and geometric center,yet fail to capture its instantaneous morphological evolution.This work established a lightweight real-time monitoring framework that integrates YOLOv8n and the perceptual Hashing(PHash)algorithm to monitor the dynamic stability of the molten pool in l-DED online.Furthermore,the molten-pool interframe similarity(MPIFS)is developed as a novel metric to quantify dynamic stability.The experimental results show that the YOLOv8n-PHash framework achieves a processing speed of 85 FPS(3.15×faster than U-Net)and reduces computational latency to 10.87 ms/frame,which is 6×faster than the structural similarity(SSIM),satisfying industrial closed-loop control requirements.The MPIFS metric shows three times higher sensitivity to molten-pool fluctuations than the static geometric parameters,with a standard deviation of 2.8%for MPIFS versus 0.15%-0.98%for the width and height.This enhanced sensitivity significantly improves the anomaly detection capabilities.In addition,a strong correlation among the process,molten-pool stability,and microstructure was confirmed.An appropriately low laser power was shown to improve MPIFS stability,resulting in smooth interfaces and uniform fine grains.This work provides a novel approach for the online monitoring of molten-pool stability and microstructure prediction in L-DED additive manufacturing. 展开更多
关键词 Additive manufacturing Online monitoring Molten-pool stability Microstructure
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In-situ quality monitoring in LPBF via melt-pool radiation:Compressive sampling and deep feature extraction 认领 引用 被引量：1
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作者 Hanxiang Zhou Yongqiang Yang +5 位作者 Vyacheslav Trofimov Hui Li Zibin Liu Yunmian Xiao Tuixin Chen Changhui Song 《Additive Manufacturing Frontiers》 CAS CSCD 2026年第2期26-38,共13页
In-situ monitoring methods and deep learning models are increasingly being used for the quality assessment of parts fabricated using laser powder bed fusion to overcome the limitations of poor process repeatability.Ho... In-situ monitoring methods and deep learning models are increasingly being used for the quality assessment of parts fabricated using laser powder bed fusion to overcome the limitations of poor process repeatability.However,the massive data collection required for part-quality monitoring results in high transmission loads and storage costs.To address this problem,this study utilized the compressed sensing theory to acquire compressed photodiode signals.These signals were then used to train and test convolutional neural networks(CNN)to identify the lack-of-fusion,normal,and keyhole modes.At a compressive-sampling rate of 25%,the classification accuracy decreased from 93.1%(raw signals)to 79.3%.However,increasing the compression rate from 25%to 90%did not significantly decrease the classification accuracy.The linear mapping of the raw signal via a Gaussian measurement matrix causes coordinate information folding,thereby impairing the representation of latent features.Therefore,Gaussian process modeling was adopted for the features extracted using a pretrained CNN to mitigate the temporal information collapse and allow the compressed signals to achieve an accuracy comparable to that of the raw data.Furthermore,the sparsity and rank complexity of the melt-pool radiation signals were evaluated using sparse representation and principal component analysis. 展开更多
关键词 Laser powder bed fusion Compressive sampling In-situ quality monitoring Convolutional neural network Gaussian process
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Experimental study on real-time monitoring of surrounding rock 3D wave velocity structure and failure zone in deep tunnels 认领 引用
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作者 Hongyun Yang Chuandong Jiang +4 位作者 Yong Li Zhi Lin Xiang Wang Yifei Wu Wanlin Feng 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2026年第2期423-437,共15页
An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of a... An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering. 展开更多
关键词 Deep-buried tunnel Microseismic monitoring Wave velocity tomography Surrounding rock damage zone Real-time monitoring
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Emergency monitoring index evaluation for collapse scenarios:A TriFAHPDBN model addressing data-scarce conditions 认领 引用
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作者 LIU Jikun WANG Chenghu +2 位作者 GAO Guiyun MA Haitao YANG Xiaolin 《Journal of Mountain Science》 SCIE CSCD 2026年第7期3241-3258,共18页
In the initial phase of emergency response to geological disasters,decision-makers are frequently challenged by extreme environmental uncertainty and a critical shortage of quantitative monitoring data.To bridge this ... In the initial phase of emergency response to geological disasters,decision-makers are frequently challenged by extreme environmental uncertainty and a critical shortage of quantitative monitoring data.To bridge this decision-making gap prior to the full establishment of monitoring networks,this study systematically selects emergency monitoring indicators by first characterizing geological disasters and identifying monitoring requirements through a literature review.An evaluation model is subsequently developed,comprising four primary factors—monitorability,timeliness,sensitivity,and feasibility—and nine secondary factors related to accuracy,stability,monitoring frequency,and sensitivity to catastrophic geological changes.The triangular fuzzy analytic hierarchy process(TriFAHP)is employed to address the inherent fuzziness in expert judgment,while a deep belief network(DBN)extracts expert decision features and generates an individual correction coefficient(β)to optimize weight allocation.The model yields a consistency ratio(CR)of 0.0329,confirming high reliability of the derived weights.Factor weights are ranked as follows:monitorability(0.3808)>timeliness(0.3353)>sensitivity(0.1874)>feasibility(0.0966).Secondary factors,including accuracy(0.2083),monitoring frequency(0.2682),and indicator sensitivity to Disaster abrupt changes(0.0948),significantly influence the model’s early warning efficacy.The model is applied to an emergency monitoring scenario involving slope collapse,evaluating 16 commonly used indicators.Displacement,velocity,acceleration,and rainfall are identified as key monitoring indicators.These indicators are subsequently applied to the emergency monitoring of a slope collapse in Inner Mongolia,where they demonstrate effectiveness in supporting early warning decisions,thereby validating the model’s practicality and reliability.Further analysis reveals a decision-making tendency among experts to prioritize monitorability,while placing relatively less intrinsic value on emergency response speed.This study advances the theoretical framework of geological disaster management by shifting the focus from postdeployment data analysis to pre-deployment strategic configuration,offering a systematic and quantitative indexing tool to solve the initial monitoring configuration problem under data-scarce and highly uncertain emergency conditions. 展开更多
关键词 Collapse Emergency monitoring Monitoring index Triangular fuzzy analytic hierarchy process Deep belief network
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A review on research of system dynamics and multi-source fault diagnosis of key components in high-speed train 认领 引用
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作者 Baosen Wang Yongqiang Liu +4 位作者 Qilan Li Min Wang Qiaoying Ma Yingying Liao Shaopu Yang 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第1期496-507,共12页
As China's high-speed railway technology advances,high-speed trains have emerged as a pivotal mode of transportation,instrumental in facilitating passenger and freight mobility while fostering robust regional eco-... As China's high-speed railway technology advances,high-speed trains have emerged as a pivotal mode of transportation,instrumental in facilitating passenger and freight mobility while fostering robust regional eco-nomic and trade interactions.Nonetheless,the safety of train operations remains a paramount concern,prompting extensive research into the dynamic behavior of critical components,which is essential to ensuring seamless and secure transportation services.This article commences by comprehensively reviewing the current landscape and evolutionary trajectory of dynamic model analysis for both traditional bearings and axle box bearings.Emphasis is placed on elucidating the profound influence of diverse bearing fault types on the system's kinematic state,alongside delving into the research methodologies employed in developing multi-physics field coupling models.Subsequently,it expounds on the content of investigations focusing on various wheel and track impairments,grounded in the dynamic modeling of the bearing vehicle coupling system.Concurrently,the intricate interplay between wheel-rail excitation and axle box bearing faults on the system's performance is elucidated.Concludingly,the article underscores the inadequacy of current multi-source fault diagnosis meth-odologies in tackling the intricacies of complex train operating environments,thereby highlighting its sig-nificance as a pressing and vital research agenda for the future. 展开更多
关键词 High-speed train Axle box bearing Dynamic model Wheel rail excitation Multi-source fault
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Drive-by spatial offset detection for high-speed railway bridges based on fusion analysis of multi-source data from comprehensive inspection train 认领 引用
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作者 Chuang Wang Jiawang Zhan +4 位作者 Nan Zhang Yujie Wang Xinxiang Xu Zhihang Wang Zhen Ni 《Railway Engineering Science》 EI 2026年第1期128-148,共21页
The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR ... The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR bridges.Drive-by monitoring of bridge uneven settlement demonstrates significant potential due to its practicality,cost-effectiveness,and efficiency.However,existing drive-by methods for detecting bridge offset have limitations such as reliance on a single data source,low detection accuracy,and the inability to identify lateral deformations of bridges.This paper proposes a novel drive-by inspection method for spatial offset of HSR bridge based on multi-source data fusion of comprehensive inspection train.Firstly,dung beetle optimizer-variational mode decomposition was employed to achieve adaptive decomposition of non-stationary dynamic signals,and explore the hidden temporal relationships in the data.Subsequently,a long short-term memory neural network was developed to achieve feature fusion of multi-source signal and accurate prediction of spatial settlement of HSR bridge.A dataset of track irregularities and CRH380A high-speed train responses was generated using a 3D train-track-bridge interaction model,and the accuracy and effectiveness of the proposed hybrid deep learning model were numerically validated.Finally,the reliability of the proposed drive-by inspection method was further validated by analyzing the actual measurement data obtained from comprehensive inspection train.The research findings indicate that the proposed approach enables rapid and accurate detection of spatial offset in HSR bridge,ensuring the long-term operational safety of HSR bridges. 展开更多
关键词 High-speed railway bridge Drive-by inspection Spatial offset Multi-source data fusion Deep learning
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A condition control-based dual-reliability evaluation for structural health monitoring 认领 引用
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作者 Qiuhui XU Shenfang YUAN +1 位作者 Jian CHEN Hutao JING 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第1期247-262,共16页
It is well recognized that Structural Health Monitoring(SHM)reliability evaluation is a key aspect that needs to be urgently addressed to promote the wide application of SHM methods.However,the existing studies typica... It is well recognized that Structural Health Monitoring(SHM)reliability evaluation is a key aspect that needs to be urgently addressed to promote the wide application of SHM methods.However,the existing studies typically transfer the Non-Destructive Testing/Evaluation(NDT/E)reliability metrics to SHM without a systematic analysis of where these metrics originated.Seldom attentions are paid to the evaluation conditions which are very important to apply these metrics.Aimed at this issue,a new condition control-based Dual-Reliability Evaluation(Dual-RE)method for SHM is proposed.This new method is proposed based on a systematic analysis of the whole framework of reliability evaluation from instrument to NDT,and emphasis is paid to the evaluation condition control.Based on these analyses,considering the special online application scenario of SHM,the proposed Dual-RE method contains two key components:Integrated Sensor-based SHM-RE(IS-SHM-RE)and Critical Service Condition-based SHM-RE(CSC-SHM-RE).ISSHM-RE evaluates the reliability of integrated SHM sensor and system themselves under approximate repeatability conditions,while CSC-SHM-RE assesses SHM reliability under the dominant uncertainties during service,namely intermediate conditions.To demonstrate the Dual-RE,crack monitoring by using the Guided Wave-based-SHM(GW-SHM)on aircraft lug structures is taken as a case study.Both the crack detection and sizing performance are evaluated from accuracy and uncertainty. 展开更多
关键词 Crack detection and sizing Dual-reliability evaluation Evaluation condition control Guided wave-based monitoring Reliability evaluation Structural health monitoring
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In situ Multiple Optical Monitoring of Bulk Polymerization 认领 引用
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作者 ZHANG Ruiqing YIN Kuo +3 位作者 CHEN Yanjie GU Fan LIU Jian MA Xiang 《高等学校化学学报》 SCIE EI CAS CSCD 北大核心 2026年第5期247-253,共7页
Optical visualization provides a highly sensitive,non-invasive,and straightforward approach for the in situ monitoring of bulk polymerization processes.This paper synthesized a 9,14-diphenyl-9,14-dihydrodibenzo[a,c]ph... Optical visualization provides a highly sensitive,non-invasive,and straightforward approach for the in situ monitoring of bulk polymerization processes.This paper synthesized a 9,14-diphenyl-9,14-dihydrodibenzo[a,c]phenazine(DPAC)-based molecule,whose distinct excited-state conformational responses under different microenvironments enabled the monitoring of microscopic dynamic changes within the system.During the polymerization of methyl methacrylate(MMA),the system transfer from a liquid monomer to a solid polymer.Accompanying this process,the fluorescence of DPAC shifts from red to blue,reflecting the increase in local viscosity and the restriction of molecular motion.Subsequently,the gradual enhancement of phosphorescence and the extension of its lifetime indicate the rising rigidity of the polymer network.Through dual-channel monitoring based on ratio-metric fluores⁃cence and phosphorescence,this strategy makes the visual tracking of bulk polymerization feasible. 展开更多
关键词 In situ monitoring Bulk polymerization Vibration-induced emission
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Multi-source remote sensing unveils hydrological dynamics and drought escalation in Central Asia's arid regions 认领 引用
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作者 WANG Yuhao ZHENG Zhijie +13 位作者 ZHU Guofeng HUANG Enwei MENG Gaojia LU Siyu QIU Dongdong CHEN Longhu LI Rui JIAO Yinying ZHAO Ling QI Xiaoyu WANG Qinqin LI Wenmin MIAO Yuxin WANG Qingyang 《Journal of Geographical Sciences》 SCIE CSCD 2026年第5期1105-1129,共25页
Despite the reported“warming-wetting”trend,Central Asia faces severe water insecurity due to climate shifts and anthropogenic activities.This study integrates multi-source remote sensing data(GRACE,TRMM,MODIS)with m... Despite the reported“warming-wetting”trend,Central Asia faces severe water insecurity due to climate shifts and anthropogenic activities.This study integrates multi-source remote sensing data(GRACE,TRMM,MODIS)with machine learning to analyze drought dynamics from 2003 to 2022 using the Water Storage Deficit Index(WSDI).Results reveal significant declines in terrestrial water storage(TWS)particularly in the Tianshan Mountains(–10.20 mm/yr)and the Central Desert(–6.19 mm/yr).Drought severity has intensified since 2014,with 67%of subregions transitioning to moderate drought.Random Forest modeling indicates that drought is no longer solely climate-driven but is increasingly dominated by anthropogenic factors(GDP,urbanization,cropland),which explain over 85%of the variability.Furthermore,the WSDI outperformed traditional indices by identifying 12 major drought events linked to deep aquifer depletion—a“hidden”structural deficit often overlooked by surface-based metrics.These findings challenge the optimistic“warming-wetting”narrative,highlighting the urgent need for storage-based management strategies to address anthropogenic groundwater depletion. 展开更多
关键词 Central Asia’s arid regions drought dynamics multi-source remote sensing GRACE hydrometeorological drivers Random Forest
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Real-time monitoring of tunnel structures using digital twin and artificial intelligence:A short overview 认领 引用
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作者 Mohammad Afrazi Danial Jahed Armaghani +2 位作者 Hossein Afrazi Hadi Fattahi Pijush Samui 《Deep Underground Science and Engineering》 EI CAS CSCD 2026年第2期315-330,共16页
Tunnels are essential components of contemporary infrastructure,yet guaranteeing their safety,longevity,and efficiency remains a persistent challenge.Recent breakthroughs in artificial intelligence(AI)and digital twin... Tunnels are essential components of contemporary infrastructure,yet guaranteeing their safety,longevity,and efficiency remains a persistent challenge.Recent breakthroughs in artificial intelligence(AI)and digital twin(DT)technology provide innovative solutions for the real-time monitoring of tunnel systems,suggesting proactive maintenance tactics and improved safety protocols.This review paper offers a comprehensive examination of the application of AI and DT methodologies in tunnel surveillance.We explore the core concepts of AI and DT and their applicability to structural monitoring,encompassing machine learning,computer vision,and sensor integration.Through the utilization of these AI-powered technologies,engineers are equipped with unparalleled insights into the state and behavior of tunnels,facilitating the early identification of irregularities and the optimization of maintenance timelines.We discuss the array of AI techniques utilized for the immediate monitoring of tunnel systems,emphasizing their foundations,benefits,and practical uses.Numerous studies have showcased the effectiveness and adaptability of AI-based monitoring systems in various tunnel settings.Moreover,we address the hurdles and constraints inherent in AI and DT methodologies and suggest strategies for overcoming them,such as data augmentation,interpretable AI,edge computing,and continuous monitoring.Ultimately,the incorporation of AI and DT technologies into tunnel surveillance signifies a paradigm shift,offering substantial advantages over conventional techniques.By adopting AI-driven monitoring systems,tunnel operators can augment safety,prolong the lifespan of infrastructure,and decrease operational expenses,molding the future of subterranean infrastructure management. 展开更多
关键词 artificial intelligence digital twin machine learning monitoring real-time tunnelling
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Deformation warning of surrounding rock mass of underground powerhouse based on octree theory and microseismic monitoring 认领 引用
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作者 Linlu Dong Nuwen Xu +5 位作者 Peng Li Huabo Xiao Yonghong Li Yuepeng Sun Biao Li Tieshuan Zhao 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第2期1160-1176,共17页
The effective early warning of surrounding rock mass deformation is crucial in geotechnical engineering for ensuring the safety and stability of underground constructions.This study introduces a novel risk early warni... The effective early warning of surrounding rock mass deformation is crucial in geotechnical engineering for ensuring the safety and stability of underground constructions.This study introduces a novel risk early warning model based on multi-parameter fuzzy comprehensive evaluation,which quantitatively assesses the risk state of the surrounding rock mass.The microseismic(MS)monitoring system is set up for the underground powerhouse.The spatial and temporal distribution of MS events and the frequency characteristics of MS signals are analyzed during the top arch excavation.The early warning indices for characterizing MS spatial aggregation and frequency-energy dispersion are proposed based on the octree theory to assess the deformation of the surrounding rock mass.The risk warning model for the surrounding rock mass in underground engineering is developed through the integration of the formulated index and the frequency characteristics of MS signals.The results indicate that the multiparameter fuzzy comprehensive assessment model can achieve three-dimensional visualization of risk warnings for the surrounding rock mass.The quantitative results regarding warning time and potential deformation areas are highly consistent with the characteristics of MS precursors.These research results can provide an important reference for early warning of surrounding rock mass risk in similar underground projects. 展开更多
关键词 Underground powerhouse Octree theory Microseismic monitoring Early warning model
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Multi-source hydrocarbon accumulation in the faulted and deeply depressed Tangdong area,Qikou Sag,Bohai Bay Basin 认领 引用
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作者 Xiugang Pu Guochao Xia +6 位作者 Jiangchang Dong Zhannan Shi Wenzhong Han Qianru Shi Wei Zhang Yueqi Dong Xiongying Dong 《Energy Geoscience》 EI CAS CSCD 2026年第2期85-100,共16页
Based on organic geochemical analysis data,this study examined the characteristics of multi-source hydrocarbon accumulation in the Tangdong area of Qikou Depression,Bohai Bay Basin.The results indicate that the Tangdo... Based on organic geochemical analysis data,this study examined the characteristics of multi-source hydrocarbon accumulation in the Tangdong area of Qikou Depression,Bohai Bay Basin.The results indicate that the Tangdong area contains four source rock sequences:the third member of the Dongying Formation(Ed3),the middle and lower sub-members of the first and third member of Shahejie Formation(Es 1M,Es 1L,Es 3).Both the salinity of sedimentary water bodies and the contribution of bacteria in the four source rock sequences gradual increase in the order of Es 3,Es 1M,Es 1L and Ed3,as indicated by the Ga/C30H,ETR,sterane/C30 hopane,C24TeT/C26TT,C23TT/C30H,and the carbon isotope data of group components.Additionally,certain quantities of 4-methylsterane have been detected in source rocks in the Es 3,Es 1M,and Ed3,suggesting the special contribution of dinoflagellates to these source rocks.Based on biomarker parameters and carbon isotope data,the crude oil can be categorized into classes A to D,which show complex multi-source accumulation characterized by different sources in the same well and even in the same layer.Class A crude oil occurring in the higher part of the Ed3 originates from source rocks in the Es 1L and Es 3.Class B oil existing in the lower part of the Ed3 is derived from source rocks in the Ed3.Class C oil found in the Es 1U is sourced from source rocks in the Es 1M.Class D oil present in the Es 1L is contributed indigenously by the source rocks in the Es 1L,establishing this unit as a lithologic hydrocarbon reservoir with source rocks and reservoirs in the same layer.Under the guidance of the understanding of multi-source hydrocarbon accumulation,currently,three wells have been drilled,all yielding satisfactory outcomes. 展开更多
关键词 Oil-source rock correlation Multi-source hydrocarbon accumulation Petroleum geochemistry Source rock Deeply depressed area Qikou sag Exploration well emplacement
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Anti-seepage performance of polymer-enhanced three-layer cover system of landfill:Field monitoring and numerical modelling 认领 引用
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作者 Ming Min Hefu Pu +3 位作者 Chao Zhou Xiao He Lusha Jiang Shengyi Deng 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第5期3891-3903,共13页
Landfill cover system plays a crucial role in reducing leachate generation by limiting rainwater infiltration.This paper evaluates the field performance of a polymer-enhanced three-layer cover system at a leather slud... Landfill cover system plays a crucial role in reducing leachate generation by limiting rainwater infiltration.This paper evaluates the field performance of a polymer-enhanced three-layer cover system at a leather sludge dump site in Xinji city,China over a 1-year monitoring period.Waste soil(WS),sand-bentonite mixture(SB),and sand-polymer-bentonite mixture(SPB)were used as the low-permeability layer,respectively,in three test areas,above which the fine-grained cultivated soil and gravel were used in the top and middle layers to form a capillary barrier.During the 1-year monitoring period,the recorded cumulative rainfall was 452.1 mm,and the volumetric water content(VWC)at the top layer fluctuated significantly from 0.13 to 0.45 in response to rainfall and evaporation,but that of the low-permeability layer maintained stable for both cover SB and SPB.No water percolation was detected during the 1-year monitoring period.Furthermore,numerical simulations were carried out to assess the anti-seepage performance under more extreme climatic conditions(i.e.,higher rainfall intensity and long-term deterioration of soil permeability).Numerical simulations corroborated the field observations that the SPB layer effectively minimized percolation even under extreme climatic conditions.For example,under the most unfavourable conditions,the computed annual percolation through the cover SPB was 4.7 mm,as low as 27.2%and 8.1%that through the cover SB(=17.3 mm)and WS(=57.9 mm).Overall,the results suggest that the polymer-enhanced three-layer soil cover is a promising alternative to traditional geomembrane-based covers and/or thick composite soil covers. 展开更多
关键词 Landfill cover Water percolation Polymer-modified bentonite Climatic conditions Field monitoring
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Evaluating rock instability risk areas in deep mining using microseismic monitoring-based risk fields 认领 引用
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作者 YAN Fang DU Shengnan +3 位作者 DONG Longjun LI Xuan LI Guanguan HE Feifan 《Journal of Mountain Science》 SCIE CSCD 2026年第4期1528-1546,共19页
Microseismic(MS)monitoring is an efficacious technology for the detection and early warning of rock mass instability in deep mining operations.However,existing MS-based early warning methodologies,which primarily rely... Microseismic(MS)monitoring is an efficacious technology for the detection and early warning of rock mass instability in deep mining operations.However,existing MS-based early warning methodologies,which primarily rely on singleparameter analysis or local clustering techniques,typically lack quantitative risk assessment capabilities.Inspired by the concept of a rock instability risk field,this study proposes a spatially continuous methodology for risk evaluation.Specifically,the Empirical Green’s Function(EGF)method is employed to estimate failure probabilities,while a Cloud Model is introduced to quantify potential severity.By integrating these components,a three-dimensional risk field function is formulated,using spatial coordinates as independent variables.Regional risk is then assessed through surface integral formulations,enabling a detailed characterization of its spatial distribution.The proposed methodology was applied to a lead-zinc mine in Northwest China.The calculated regional risk values demonstrate strong spatial consistency with energy density clusters identified using the DBSCAN algorithm.Quantitative comparisons reveal a significant positive correlation between the risk field outputs and independent MS event clusters,confirming the method’s efficacy in capturing the spatiotemporal concentration of instability potential.These findings indicate that representing rock instability risk as a continuous field enhances the regional interpretability of MS data and offers a quantitative foundation for dynamic hazard zonation in deep underground excavations. 展开更多
关键词 Microseismic monitoring Deep mining Rock instability Risk field Risk assessment Surface integral
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