The integration of digital twin(DT)technology with microseismic(MS)monitoring for evaluating the dynamic response of high-arch dams remains under-explored.This paper investigates the application of MS monitoring on th...The integration of digital twin(DT)technology with microseismic(MS)monitoring for evaluating the dynamic response of high-arch dams remains under-explored.This paper investigates the application of MS monitoring on the Dagangshan high-arch dam during its normal water storage operating period to assess potential damage.The study analyzes the MS characteristics of the dam during the Luding earthquake(Ms=6.8).A framework for constructing a damage driven DT model of a high-arch dam is proposed.The DT model is capable of self-updating its mechanical parameters based on MS data.Seismic response calculations are conducted utilizing cloud computing,allowing for the direct presentation of results within the DT model.The results indicate a high-risk area of the Dagangshan arch dam,characterized by significantMS deformation,primarily centered on the arch crown beam.This zone encompasses dam sections Nos.5-6,10-11,13-16,and 19-20,all located above 1030 m elevation.Under seismic loading,the arch dam exhibits a back-and-forth movement along the river,ultimately reaching a stable state.Following the earthquake,the stress state of the dam does not experience substantial changes.The average relative error between numerical results and measured peak ground acceleration values is 17%when considering the cumulative effect of damage,compared to 36%when neglecting this effect.This study presents a more reliable approach for assessing the state of dams.展开更多
The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to u...The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.展开更多
In recent decades,control performance monitoring(CPM)has experienced remarkable progress in research and industrial applications.While CPM research has been investigated using various benchmarks,the historical data be...In recent decades,control performance monitoring(CPM)has experienced remarkable progress in research and industrial applications.While CPM research has been investigated using various benchmarks,the historical data benchmark(HIS)has garnered the most attention due to its practicality and effectiveness.However,existing CPM reviews usually focus on the theoretical benchmark,and there is a lack of an in-depth review that thoroughly explores HIS-based methods.In this article,a comprehensive overview of HIS-based CPM is provided.First,we provide a novel static-dynamic perspective on data-level manifestations of control performance underlying typical controller capacities including regulation and servo:static and dynamic properties.The static property portrays time-independent variability in system output,and the dynamic property describes temporal behavior driven by closed-loop feedback.Accordingly,existing HIS-based CPM approaches and their intrinsic motivations are classified and analyzed from these two perspectives.Specifically,two mainstream solutions for CPM methods are summarized,including static analysis and dynamic analysis,which match data-driven techniques with actual controlling behavior.Furthermore,this paper also points out various opportunities and challenges faced in CPM for modern industry and provides promising directions in the context of artificial intelligence for inspiring future research.展开更多
Addressing the current challenges in transforming pixel displacement into physical displacement in visual monitoring technologies,as well as the inability to achieve precise full-field monitoring,this paper proposes a...Addressing the current challenges in transforming pixel displacement into physical displacement in visual monitoring technologies,as well as the inability to achieve precise full-field monitoring,this paper proposes a method for identifying the structural dynamic characteristics of wind turbines based on visual monitoring data fusion.Firstly,the Lucas-Kanade Tomasi(LKT)optical flow method and a multi-region of interest(ROI)monitoring structure are employed to track pixel displacements,which are subsequently subjected to band pass filtering and resampling operations.Secondly,the actual displacement time history is derived through double integration of the acquired acceleration data and subsequent band pass filtering.The scale factor is obtained by applying the least squares method to compare the visual displacement with the displacement derived from double integration of the acceleration data.Based on this,the multi-point displacement time histories under physical coordinates are obtained using the vision data and the scale factor.Subsequently,when visual monitoring of displacements becomes impossible due to issues such as image blurring or lens occlusion,the structural vibration equation and boundary condition constraints,among other key parameters,are employed to predict the displacements at unknown monitoring points,thereby enabling full-field displacement monitoring and dynamic characteristic testing of the structure.Finally,a small-scale shaking table test was conducted on a simulated wind turbine structure undergoing shutdown to validate the dynamic characteristics of the proposed method through test verification.The research results indicate that the proposed method achieves a time-domain error within the submillimeter range and a frequency-domain accuracy of over 99%,effectively monitoring the full-field structural dynamic characteristics of wind turbines and providing a basis for the condition assessment of wind turbine structures.展开更多
Urban air quality degradation from rising CO_(2) is acute in rapidly developing tropical cities such as Makassar,Indonesia.We deploy a drone-based Internet of Things(IoT)platform for real-time CO_(2) monitoring,integr...Urban air quality degradation from rising CO_(2) is acute in rapidly developing tropical cities such as Makassar,Indonesia.We deploy a drone-based Internet of Things(IoT)platform for real-time CO_(2) monitoring,integrating low-cost sensors(NDIR,MQ135,MG811)on a DJI Phantom 4 with cloud streaming to Firebase.Measurements were collected at five sites,namely Jl.AP.Pettarani,Jl.Ahmad Yani,Jl.Sultan Hasanuddin,Jl.Nusantara,and KIMA at 08:00,12:00,and 16:00 in September 2024 while vertically profiling 1-20 m with three repeat flights per site and time.Descriptive statistics and one-way ANOVA with Tukey HSD assessed spatio-temporal differences;Pearson correlation quantified cross-sensor agreement.Results show marked spatial and diurnal variability:Jl.AP.Pettarani exhibits the highest mean concentration(442.5 ppm),likely due to flyover-induced trapping,whereas Jl.Ahmad Yani records the lowest(390.0 ppm).Vertical profiles reveal mid-altitude peaks in street-canyon and industrial settings,and dilution with height in greener areas,indicating ventilation contrasts.Preprocessing removed outliers and applied temperature-humidity corrections to low-cost sensors.Differences across locations and times are statistically significant(p<0.05),and cross-sensor correlations are strong(r≈0.88-0.96)after correction.Compared with fixed ground stations,the system provides fine-scale three-dimensional coverage and real-time visualization useful for field decisions.Limitations include payload-constrained endurance and intermittent data loss in obstructed areas.Findings support targeted interventions,improving canyon ventilation around flyovers and expanding urban greenery relevant to Makassar and similar tropical cities.展开更多
In common practice in the oil fields,the injection of water and gas into reservoirs is a crucial technique to increase production.The control of the waterflooding front in oil/gas exploitation is a matter of great con...In common practice in the oil fields,the injection of water and gas into reservoirs is a crucial technique to increase production.The control of the waterflooding front in oil/gas exploitation is a matter of great concern to reservoir engineers.Monitoring the waterflooding front in oil/gas wells plays a very important role in adjusting the well network and later in production,taking advantage of the remaining oil po-tential and ultimately achieving great success in improving the recovery rate.For a long time,micro-seismic monitoring,numerical simulation,four-dimensional seismic and other methods have been widely used in waterflooding front monitoring.However,reconciling their reliability and cost poses a significant challenge.In order to achieve real-time,reliable and cost-effective monitoring,we propose an innovative method for waterflooding front monitoring through the similarity analysis of passive source time-lapse seismic images.Typically,passive source seismic data collected from oil fields have extremely low signal-to-noise ratio(SNR),which poses a serious problem for obtaining structural images.The proposed method aims to visualize and analyze underground changes by highlighting time-lapse images and provide a strategy for underground monitoring using long-term passive source data under low SNR conditions.First,we verify the feasibility of the proposed method by designing a theoretical model.Then,we conduct an analysis of the correlation coefficient(similarity)on the passive source time-lapse seismic imaging results to enhance the image differences and identify the simulated waterflooding fronts.Finally,the proposed method is applied to the actual waterflooding front monitoring tasks in Shengli Oilfield,China.The research findings indicate that the monitoring results are consistent with the actual devel-opment conditions,which in turn demonstrates that the proposed method has great potential for practical application and is very suitable for monitoring common development tasks in oil fields.展开更多
Dynamic stress adjustment in deep-buried high geostress hard rock tunnels frequently triggers catastrophic failures such as rockbursts and collapses.While a comprehensive understanding of this process is critical for ...Dynamic stress adjustment in deep-buried high geostress hard rock tunnels frequently triggers catastrophic failures such as rockbursts and collapses.While a comprehensive understanding of this process is critical for evaluating surrounding rock stability,its dynamic evolution are often overlooked in engineering practice.This study systematically summarizes a novel classification framework for stress adjustment types—stabilizing(two-zoned),shallow failure(three-zoned),and deep failure(four-zoned)—characterized by distinct stress adjustment stages.A dynamic interpretation technology system is developed based on microseismic monitoring,integrating key microseismic parameters(energy index EI,apparent stressσa,microseismic activity S),seismic source parameter space clustering,and microseismic paths.This approach enables precise identification of evolutionary stages,stress adjustment types,and failure precursors,thereby elucidating the intrinsic linkage between geomechanical processes(stress redistribution)and failure risks.The study establishes criteria and procedures for identifying stress adjustment types and their associated failure risks,which were successfully applied in the Grand Canyon Tunnel of the E-han Highway to detect 50 instances of disaster risks.The findings offer invaluable insights into understanding the evolution process of stress adjustment and pinpointing the disaster risks linked to hard rock in comparable high geostress tunnels.展开更多
This study developed a digital twin(DT)and structural health monitoring(SHM)system for a balanced cantilever bridge,utilizing advanced measurement techniques to enhance accuracy.Vibration and dynamic strain measuremen...This study developed a digital twin(DT)and structural health monitoring(SHM)system for a balanced cantilever bridge,utilizing advanced measurement techniques to enhance accuracy.Vibration and dynamic strain measurements were obtained using accelerometers and piezo-resistive strain gauges,capturing low-magnitude dynamic strains during operational vibrations.3D-LiDAR scanning and Ultrasonic Pulse Velocity(UPV)tests captured the bridge's as-is geometry and modulus of elasticity.The resulting detailed 3D point cloud model revealed the structure's true state and highlighted discrepancies between the as-designed and as-built conditions.Dynamic properties,including modal frequencies and shapes,were extracted from the strain and acceleration measurements,providing critical insights into the bridge's structural behavior.The neutral axis depth,indicating stress distribution and potential damage,was accurately determined.Good agreement between vibration measurement data and the as-is model results validated the reliability of the digital twin model.Dynamic strain patterns and neutral axis parameters showed strong correlation with model predictions,serving as sensitive indicators of local damage.The baseline digital twin model and measurement results establish a foundation for future bridge inspections and investigations.This study demonstrates the effectiveness of combining digital twin technology with field measurements for real-time monitoring and predictive maintenance,ensuring the sustainability and safety of the bridge infrastructure,thereby enhancing its overall resilience to operational and environmental stressors.展开更多
In the leakage detection of reservoir dam bodies,traditional geophysical methods can only achieve one-time detection.Meanwhile,due to the non-uniqueness of geophysicalin version,how to improve the fidelity of geophysi...In the leakage detection of reservoir dam bodies,traditional geophysical methods can only achieve one-time detection.Meanwhile,due to the non-uniqueness of geophysicalin version,how to improve the fidelity of geophysical detection inversion profiles has become a key challenge in the industry.This study aims to construct a long-term real-time monitoring system using the time-lapse resistivity method,reveal the spatiotemporal evolution law of dam leakage,and provide technical support for accurate treatment.By integrating the Internet of Things(IoT),5G technology,and AI technology,real-time data acquisition,real-time transmission,and automatic inversionare realized.Through dynamic imaging analysis of the electrical anomaly characteristics of the leakage area and comparison between corresponding rainfall events,the leakage range and resistivity changes,the reliability and efficiency of dam leakagedete ction are significantly improved.This achieves long-termdynamic monitoring of dam leakage and provides a new perspective for the safe operation and maintenance of reservoirs.展开更多
Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently d...Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently dynamic and need to be monitored using dynamic algorithms.Mainstream dynamic algorithms rely on concatenating current measurement with past data.This work proposes a new,alternative dynamic process monitoring algorithm,using dot product feature analysis(DPFA).DPFA computes the dot product of consecutive samples,thus naturally capturing the process dynamics through temporal correlation.At the same time,DPFA's online computational complexity is lower than not just existing dynamic algorithms,but also classical static algorithms(e.g.,principal component analysis and slow feature analysis).The detectability of the new algorithm is analyzed for three types of faults typically seen in process systems:sensor bias,process fault and gain change fault.Through experiments with a numerical example and real data from a thermal power plant,the DPFA algorithm is shown to be superior to the state-of-the-art methods,in terms of better monitoring performance(fault detection rate and false alarm rate)and lower computational complexity.展开更多
Differential interferometric synthetic aperture radar （DInSAR） technology is a new method to monitor the dynamic surface subsidence. It can monitor the large scope of dynamic deformation process of surface subsidenc...Differential interferometric synthetic aperture radar （DInSAR） technology is a new method to monitor the dynamic surface subsidence. It can monitor the large scope of dynamic deformation process of surface subsidence basin and better reflect the surface subsidence form in different stages. But under the influence of factors such as noise and other factors, the tilt and horizontal deformation curves regularity calculated by DInSAR data are poorer and the actual deviation is larger. The tilt and horizontal deformations are the important indices for the safety of surface objects protection. Numerical simulation method was used to study the dynamic deformation of LW32 of West Cliff colliery in Australia based on the DInSAR monitoring data. The result indicates that the subsidence curves of two methods fit well and the correlation coefficient is more than 95%. The other deformations calculated by numerical simulation results are close to the theory form. Therefore, considering the influence, the surface and its subsidiary structures and buildings due to mining, the numerical simulation method based on the DInSAR data can reveal the distribution rules of the surface dynamic deformation values and supply the shortcomings of DInSAR technology. The research shows that the method has good applicability and can provide reference for similar situation.展开更多
The phase velocity of seismic waves varies with the propagation frequency, and thus frequency-dependent phenomena appear when CO2 gas is injected into a reservoir. By dynamically considering these phenomena with reser...The phase velocity of seismic waves varies with the propagation frequency, and thus frequency-dependent phenomena appear when CO2 gas is injected into a reservoir. By dynamically considering these phenomena with reservoir conditions it is thus feasible to extract the frequency-dependent velocity factor with the aim of monitoring changes in the reservoir both before and after CO2 injection. In the paper, we derive a quantitative expression for the frequency-dependent factor based on the Robinson seismic convolution model. In addition, an inversion equation with a frequency-dependent velocity factor is constructed, and a procedure is implemented using the following four processing steps： decomposition of the spectrum by generalized S transform, wavelet extraction of cross-well seismic traces, spectrum equalization processing, and an extraction method for frequency-dependent velocity factor based on the damped least-square algorithm. An attenuation layered model is then established based on changes in the Q value of the viscoelastic medium, and spectra of migration profiles from forward modeling are obtained and analyzed. Frequency-dependent factors are extracted and compared, and the effectiveness of the method is then verified using a synthetic data. The frequency-dependent velocity factor is finally applied to target processing and oil displacement monitoring based on real seismic data obtained before and after CO2 injection in the G89 well block within Shengli oilfield. Profiles and slices of the frequency-dependent factor determine its ability to indicate differences in CO2 flooding, and the predicting results are highly consistent with those of practical investigations within the well block.展开更多
Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings...Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings in dynamics,interdisciplinary integration,and industry adaptability.It builds a multi-dimensional dynamic model covering seven core dimensions with quantitative scoring,non-linear weighting,and DivClust grouping.An intelligent platform with real-time monitoring,early warning,and personalized recommendations integrates AI like multi-modal fusion and large-model diagnosis.The“monitoring-warning-improvement”loop helps optimize training programs,support personalized planning,and bridge talent-industry gaps,enabling digital transformation in software engineering education evaluation.展开更多
Exploring the spatio-temporal dynamics of poverty is important for research on sustainable poverty reduction in China. Based on the perspective of development geography, this paper proposes a panel vector autoregressi...Exploring the spatio-temporal dynamics of poverty is important for research on sustainable poverty reduction in China. Based on the perspective of development geography, this paper proposes a panel vector autoregressive(PVAR) model that combines the human development approach with the global indicator framework for Sustainable Development Goals(SDGs) to identify the poverty-causing and the poverty-reducing factors in China. The aim is to measure the multidimensional poverty index(MPI) of China’s provinces from 2007 to 2017, and use the exploratory spatio-temporal data analysis(ESTDA) method to reveal the characteristics of the spatio-temporal dynamics of multidimensional poverty. The results show the following:(1) The poverty-causing factors in China include the high social gross dependency ratio and crop-to-disaster ratio, and the poverty-reducing factors include the high per capita GDP, per capita social security expenditure, per capita public health expenditure, number of hospitals per 10,000 people, rate of participation in the new rural cooperative medical scheme, vegetation coverage, per capita education expenditure, number of universities, per capita research and development(R&D) expenditure, and funding per capita for cultural undertakings.(2) From 2007 to 2017, provincial income poverty(IP), health poverty(HP), cultural poverty(CP), and multidimensional poverty have been significantly reduced in China, and the overall national poverty has dropped by 5.67% annually. there is a differentiation in poverty along different dimensions in certain provinces.(3) During the study period, the local spatial pattern of multidimensional poverty between provinces showed strong spatial dynamics, and a trend of increase from the eastern to the central and western regions was noted. The MPI among provinces exhibited a strong spatial dependence over time to form a pattern of decrease from northwestern and northeastern China to the surrounding areas.(4) The spatio-temporal networks of multidimensional poverty in adjacent provinces were mainly negatively correlated, with only Shaanxi and Henan, Shaanxi and Ningxia, Qinghai and Gansu, Hubei and Anhui, Sichuan and Guizhou, and Hainan and Guangdong forming spatially strong cooperative poverty reduction relationships. These results have important reference value for the implementation of China’s poverty alleviation strategy.展开更多
The study concentrates mainly on the development of failure process incomposite rock mass. By use of acoustic emission (AE), convergence inspection, pressure monitoring,level measurement techniques and the modem signa...The study concentrates mainly on the development of failure process incomposite rock mass. By use of acoustic emission (AE), convergence inspection, pressure monitoring,level measurement techniques and the modem signal analysis technology, as well as scan electronmicroscopy (SEM) experiment, various aspects of nonlinear dynamic damage of composite rock masssurrounding the transport roadway in Linglong gold mine are discussed. According to the monitoringresults, the stability of the rock mass can be synthetically evaluated, and the intrinsic relationbetween the damage and the characteristic parameters of acoustic emission can be determined. Thelocation of the damage of rock mass can also be detected based on the acoustic emission couplemonitoring signals. Finally, the key factors which influence the stability of the transport roadwaysupported by composite hard rock materials are found out.展开更多
The aim of this study was to evaluate the effects of low-dose tibolone therapy on ovarian area, uterine volume and endometrial thickness, and define the cut-off value of endometrial thickness for curettage during uter...The aim of this study was to evaluate the effects of low-dose tibolone therapy on ovarian area, uterine volume and endometrial thickness, and define the cut-off value of endometrial thickness for curettage during uterine bleeding. We followed 619 postmenopausal women, aged 40-60 years, for two years. There were 301 subjects in the low-dose tibolone treatment group and 318 subjects in the control group. The ovarian area, uterine volume and endometrial thickness in all participants were measured by transvaginal ultrasound prior to, one and two years post enrollment, respectively. Endometrial specimens were collected from all subjects with abnormal uterine bleeding during the follow-up period. We found that the uterine volume in the treatment group was greater than that in the control group, and the difference was significant （P〈0.05）, but there were no significant differences in ovarian area and endometrial thickness between the two groups （P〉0.05）. When the cut-off value for endometrial thickness was 7.35 ram, the sensitivity and specificity were 100% and 79.07%, respectively, and 85.71% and 93.02% when 7.55 mm was set as the cut-offduring tibolone therapy. The results indicate that low-dose tibolone therapy may postpone uterine atrophy and the cut-off value of endometrial thickness may be appropriately adjusted for curettage.展开更多
The underground hydropower projects in Southwest China is characterized by large excavation sizes,high geostresses,complicated geological conditions and multiple construction processes.Various disasters such as collap...The underground hydropower projects in Southwest China is characterized by large excavation sizes,high geostresses,complicated geological conditions and multiple construction processes.Various disasters such as collapses,large deformations,rockbursts are frequently encountered,resulting in serious casualties and huge economic losses.This review mainly presents some representative results on microseismic(MS)monitoring and forecasting for disasters in hydropower underground engineering.First,a set of new denoising,spectral analysis,and location methods were developed for better identification and location of MS signals.Then,the tempo-spatial characteristics of MS events were analyzed to understand the relationship between field construction and damages of surrounding rocks.Combined with field construction,geological data,numerical simulation and parametric analysis of MS sources,the focal mechanism of MS events was revealed.A damage constitutive model considering MS fracturing size was put forward and feedback analysis considering the MS damage of underground surrounding rocks was conducted.Next,an MS multi-parameter based risk assessment and early warning method for dynamic disasters were proposed.The technology for control of the damage and deformation of underground surrounding rocks was proposed for underground caverns.Finally,two typical underground powerhouses were selected as case studies.These achievements can provide significant references for prevention and control of dynamic disasters for underground engineering with similar complicated geological conditions.展开更多
The level of deformation development of surrounding rocks is a vital predictor to evaluate impending coal mine disasters and it is important to establish accurate measurements of the deformed status to ensure coal min...The level of deformation development of surrounding rocks is a vital predictor to evaluate impending coal mine disasters and it is important to establish accurate measurements of the deformed status to ensure coal mine safety. Traditional deformation monitoring methods are mostly based on single parameter, in this paper, multiple approaches are integrated: firstly, both electric and elastic models are established,from which electric field distribution and seismic wave recording are calculated and finally, the resistivity profiles and source position information are determined using inversion methods, from which then the deformation and failure of mine floor are evaluated. According to the inversion results of both electric and seismic field signals, multiple-parameter dynamic monitoring of surrounding rock deformation in deep mine can be performed. The methodology is validated using numerical simulation results which shows that the multi-parameter dynamic monitoring methods have better results for surrounding rock deformation in deep mine monitoring than single parameter methods.展开更多
Based on digital image processing technique, a real-time system is developed to monitor and detect the dynamic displacement of engineering structures. By processing pictures with a self-programmed software, the real-t...Based on digital image processing technique, a real-time system is developed to monitor and detect the dynamic displacement of engineering structures. By processing pictures with a self-programmed software, the real-time coordinate of an object in a certain coordinate system can be obtained, and further dynamic displacement data and curve of the object can also be achieved. That is, automatic gathering and real-time processing of data can be carried out by this system simultaneously. For this system, first, an untouched monitoring technique is adopted, which can monitor or detect objects several to hundreds of meters apart; second, it has flexible installation condition and good monitoring precision of sub-millimeter degree; third, it is fit for dynamic, quasi-dynamic and static monitoring of large engineering structures. Through several tests and applications in large bridges, good reliability and dominance of the system is proved.展开更多
The construction period of large cable-stayed bridges is long, and the structure deformation is complicated. Any error during construction will potentially affect the cantilever alignments and the internal forces. In ...The construction period of large cable-stayed bridges is long, and the structure deformation is complicated. Any error during construction will potentially affect the cantilever alignments and the internal forces. In order to ensure safety during construction and exactly determine the cantilever alignments, dynamic deformation monitoring is needed immediately when the con struction of the superstructure starts. This paper aims at the requirement of deformation monitoring during the Sutong Bridge construction, and introduces the realization and observing schemes of the GPS and georobot based on remote real-time dynamic geometrical deformation monitoring system, then researches the data processing methods and enumerates some of the application achievements. Long-term operation during the Sutong Bridge construction indicates that the system runs steadily and the results are reliable.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52379098 and 42122052)the Liaoning XingLiao Talent Program(Grant No.XLYC2203008).
文摘The integration of digital twin(DT)technology with microseismic(MS)monitoring for evaluating the dynamic response of high-arch dams remains under-explored.This paper investigates the application of MS monitoring on the Dagangshan high-arch dam during its normal water storage operating period to assess potential damage.The study analyzes the MS characteristics of the dam during the Luding earthquake(Ms=6.8).A framework for constructing a damage driven DT model of a high-arch dam is proposed.The DT model is capable of self-updating its mechanical parameters based on MS data.Seismic response calculations are conducted utilizing cloud computing,allowing for the direct presentation of results within the DT model.The results indicate a high-risk area of the Dagangshan arch dam,characterized by significantMS deformation,primarily centered on the arch crown beam.This zone encompasses dam sections Nos.5-6,10-11,13-16,and 19-20,all located above 1030 m elevation.Under seismic loading,the arch dam exhibits a back-and-forth movement along the river,ultimately reaching a stable state.Following the earthquake,the stress state of the dam does not experience substantial changes.The average relative error between numerical results and measured peak ground acceleration values is 17%when considering the cumulative effect of damage,compared to 36%when neglecting this effect.This study presents a more reliable approach for assessing the state of dams.
文摘The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.
基金supported in part by the National Natural Science Foundation of China(62125306)Zhejiang Key Research and Development Project(2024C01163)the State Key Laboratory of Industrial Control Technology,China(ICT2024A06)
文摘In recent decades,control performance monitoring(CPM)has experienced remarkable progress in research and industrial applications.While CPM research has been investigated using various benchmarks,the historical data benchmark(HIS)has garnered the most attention due to its practicality and effectiveness.However,existing CPM reviews usually focus on the theoretical benchmark,and there is a lack of an in-depth review that thoroughly explores HIS-based methods.In this article,a comprehensive overview of HIS-based CPM is provided.First,we provide a novel static-dynamic perspective on data-level manifestations of control performance underlying typical controller capacities including regulation and servo:static and dynamic properties.The static property portrays time-independent variability in system output,and the dynamic property describes temporal behavior driven by closed-loop feedback.Accordingly,existing HIS-based CPM approaches and their intrinsic motivations are classified and analyzed from these two perspectives.Specifically,two mainstream solutions for CPM methods are summarized,including static analysis and dynamic analysis,which match data-driven techniques with actual controlling behavior.Furthermore,this paper also points out various opportunities and challenges faced in CPM for modern industry and provides promising directions in the context of artificial intelligence for inspiring future research.
基金supported by the National Science Foundation of China(Grant Nos.52068049 and 51908266)the Science Fund for Distinguished Young Scholars of Gansu Province(No.21JR7RA267)Hongliu Outstanding Young Talents Program of Lanzhou University of Technology.
文摘Addressing the current challenges in transforming pixel displacement into physical displacement in visual monitoring technologies,as well as the inability to achieve precise full-field monitoring,this paper proposes a method for identifying the structural dynamic characteristics of wind turbines based on visual monitoring data fusion.Firstly,the Lucas-Kanade Tomasi(LKT)optical flow method and a multi-region of interest(ROI)monitoring structure are employed to track pixel displacements,which are subsequently subjected to band pass filtering and resampling operations.Secondly,the actual displacement time history is derived through double integration of the acquired acceleration data and subsequent band pass filtering.The scale factor is obtained by applying the least squares method to compare the visual displacement with the displacement derived from double integration of the acceleration data.Based on this,the multi-point displacement time histories under physical coordinates are obtained using the vision data and the scale factor.Subsequently,when visual monitoring of displacements becomes impossible due to issues such as image blurring or lens occlusion,the structural vibration equation and boundary condition constraints,among other key parameters,are employed to predict the displacements at unknown monitoring points,thereby enabling full-field displacement monitoring and dynamic characteristic testing of the structure.Finally,a small-scale shaking table test was conducted on a simulated wind turbine structure undergoing shutdown to validate the dynamic characteristics of the proposed method through test verification.The research results indicate that the proposed method achieves a time-domain error within the submillimeter range and a frequency-domain accuracy of over 99%,effectively monitoring the full-field structural dynamic characteristics of wind turbines and providing a basis for the condition assessment of wind turbine structures.
基金supported by the Directorate of Research,Technology,and Community Service(DRTPM),Ministry of Education,Culture,Research,and Technology,grant number 2817/UN36.11/LP2M/2024.
文摘Urban air quality degradation from rising CO_(2) is acute in rapidly developing tropical cities such as Makassar,Indonesia.We deploy a drone-based Internet of Things(IoT)platform for real-time CO_(2) monitoring,integrating low-cost sensors(NDIR,MQ135,MG811)on a DJI Phantom 4 with cloud streaming to Firebase.Measurements were collected at five sites,namely Jl.AP.Pettarani,Jl.Ahmad Yani,Jl.Sultan Hasanuddin,Jl.Nusantara,and KIMA at 08:00,12:00,and 16:00 in September 2024 while vertically profiling 1-20 m with three repeat flights per site and time.Descriptive statistics and one-way ANOVA with Tukey HSD assessed spatio-temporal differences;Pearson correlation quantified cross-sensor agreement.Results show marked spatial and diurnal variability:Jl.AP.Pettarani exhibits the highest mean concentration(442.5 ppm),likely due to flyover-induced trapping,whereas Jl.Ahmad Yani records the lowest(390.0 ppm).Vertical profiles reveal mid-altitude peaks in street-canyon and industrial settings,and dilution with height in greener areas,indicating ventilation contrasts.Preprocessing removed outliers and applied temperature-humidity corrections to low-cost sensors.Differences across locations and times are statistically significant(p<0.05),and cross-sensor correlations are strong(r≈0.88-0.96)after correction.Compared with fixed ground stations,the system provides fine-scale three-dimensional coverage and real-time visualization useful for field decisions.Limitations include payload-constrained endurance and intermittent data loss in obstructed areas.Findings support targeted interventions,improving canyon ventilation around flyovers and expanding urban greenery relevant to Makassar and similar tropical cities.
基金supported by the CNPC-SWPU Innovation Alliance Technology Cooperation Project(2020CX020000)the National Natural Science Foundation of China(42022028)+1 种基金the Natural Science Foundation of Sichuan Province(24NSFSC0808)the China Scholarship Council(202306440144)。
文摘In common practice in the oil fields,the injection of water and gas into reservoirs is a crucial technique to increase production.The control of the waterflooding front in oil/gas exploitation is a matter of great concern to reservoir engineers.Monitoring the waterflooding front in oil/gas wells plays a very important role in adjusting the well network and later in production,taking advantage of the remaining oil po-tential and ultimately achieving great success in improving the recovery rate.For a long time,micro-seismic monitoring,numerical simulation,four-dimensional seismic and other methods have been widely used in waterflooding front monitoring.However,reconciling their reliability and cost poses a significant challenge.In order to achieve real-time,reliable and cost-effective monitoring,we propose an innovative method for waterflooding front monitoring through the similarity analysis of passive source time-lapse seismic images.Typically,passive source seismic data collected from oil fields have extremely low signal-to-noise ratio(SNR),which poses a serious problem for obtaining structural images.The proposed method aims to visualize and analyze underground changes by highlighting time-lapse images and provide a strategy for underground monitoring using long-term passive source data under low SNR conditions.First,we verify the feasibility of the proposed method by designing a theoretical model.Then,we conduct an analysis of the correlation coefficient(similarity)on the passive source time-lapse seismic imaging results to enhance the image differences and identify the simulated waterflooding fronts.Finally,the proposed method is applied to the actual waterflooding front monitoring tasks in Shengli Oilfield,China.The research findings indicate that the monitoring results are consistent with the actual devel-opment conditions,which in turn demonstrates that the proposed method has great potential for practical application and is very suitable for monitoring common development tasks in oil fields.
基金supported by the National Natural Science Foundation of China(Nos.42177173,U23A20651 and 42130719)and the Outstanding Youth Science Fund Project of Sichuan Provincial Natural Science Foundation(No.2025NSFJQ0003)。
文摘Dynamic stress adjustment in deep-buried high geostress hard rock tunnels frequently triggers catastrophic failures such as rockbursts and collapses.While a comprehensive understanding of this process is critical for evaluating surrounding rock stability,its dynamic evolution are often overlooked in engineering practice.This study systematically summarizes a novel classification framework for stress adjustment types—stabilizing(two-zoned),shallow failure(three-zoned),and deep failure(four-zoned)—characterized by distinct stress adjustment stages.A dynamic interpretation technology system is developed based on microseismic monitoring,integrating key microseismic parameters(energy index EI,apparent stressσa,microseismic activity S),seismic source parameter space clustering,and microseismic paths.This approach enables precise identification of evolutionary stages,stress adjustment types,and failure precursors,thereby elucidating the intrinsic linkage between geomechanical processes(stress redistribution)and failure risks.The study establishes criteria and procedures for identifying stress adjustment types and their associated failure risks,which were successfully applied in the Grand Canyon Tunnel of the E-han Highway to detect 50 instances of disaster risks.The findings offer invaluable insights into understanding the evolution process of stress adjustment and pinpointing the disaster risks linked to hard rock in comparable high geostress tunnels.
基金funded by the Thailand Science Research and Innovation Fund,Chulalongkorn University(BCG_FF_68_165_2100_027)The first author(Tidarut Jirawattanasomkul)also gratefully acknowledges support from the Grants for Development of New Faculty Staff,Ratchadaphiseksomphot Fund,Chulalongkorn University.The corresponding author(Supasit Srivaranun)acknowledges the Research and Innovation Funding from National Research Council of Thailand(No.N84A680208)+2 种基金the Research Grant from Faculty of Engineering,Kasetsart University(No.67/05/CE)The fourth author(Suched Likitlersuang)acknowledges Thailand Science Research and Innovation Fund Chulalongkorn University(DISF68210001)the National Research Council of Thailand(NRCT):Grant No.N42A670572.
文摘This study developed a digital twin(DT)and structural health monitoring(SHM)system for a balanced cantilever bridge,utilizing advanced measurement techniques to enhance accuracy.Vibration and dynamic strain measurements were obtained using accelerometers and piezo-resistive strain gauges,capturing low-magnitude dynamic strains during operational vibrations.3D-LiDAR scanning and Ultrasonic Pulse Velocity(UPV)tests captured the bridge's as-is geometry and modulus of elasticity.The resulting detailed 3D point cloud model revealed the structure's true state and highlighted discrepancies between the as-designed and as-built conditions.Dynamic properties,including modal frequencies and shapes,were extracted from the strain and acceleration measurements,providing critical insights into the bridge's structural behavior.The neutral axis depth,indicating stress distribution and potential damage,was accurately determined.Good agreement between vibration measurement data and the as-is model results validated the reliability of the digital twin model.Dynamic strain patterns and neutral axis parameters showed strong correlation with model predictions,serving as sensitive indicators of local damage.The baseline digital twin model and measurement results establish a foundation for future bridge inspections and investigations.This study demonstrates the effectiveness of combining digital twin technology with field measurements for real-time monitoring and predictive maintenance,ensuring the sustainability and safety of the bridge infrastructure,thereby enhancing its overall resilience to operational and environmental stressors.
文摘In the leakage detection of reservoir dam bodies,traditional geophysical methods can only achieve one-time detection.Meanwhile,due to the non-uniqueness of geophysicalin version,how to improve the fidelity of geophysical detection inversion profiles has become a key challenge in the industry.This study aims to construct a long-term real-time monitoring system using the time-lapse resistivity method,reveal the spatiotemporal evolution law of dam leakage,and provide technical support for accurate treatment.By integrating the Internet of Things(IoT),5G technology,and AI technology,real-time data acquisition,real-time transmission,and automatic inversionare realized.Through dynamic imaging analysis of the electrical anomaly characteristics of the leakage area and comparison between corresponding rainfall events,the leakage range and resistivity changes,the reliability and efficiency of dam leakagedete ction are significantly improved.This achieves long-termdynamic monitoring of dam leakage and provides a new perspective for the safe operation and maintenance of reservoirs.
基金supported in part by the National Science Fund for Distinguished Young Scholars of China(62225303)the National Natural Science Fundation of China(62303039,62433004)+2 种基金the China Postdoctoral Science Foundation(BX20230034,2023M730190)the Fundamental Research Funds for the Central Universities(buctrc202201,QNTD2023-01)the High Performance Computing Platform,College of Information Science and Technology,Beijing University of Chemical Technology
文摘Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently dynamic and need to be monitored using dynamic algorithms.Mainstream dynamic algorithms rely on concatenating current measurement with past data.This work proposes a new,alternative dynamic process monitoring algorithm,using dot product feature analysis(DPFA).DPFA computes the dot product of consecutive samples,thus naturally capturing the process dynamics through temporal correlation.At the same time,DPFA's online computational complexity is lower than not just existing dynamic algorithms,but also classical static algorithms(e.g.,principal component analysis and slow feature analysis).The detectability of the new algorithm is analyzed for three types of faults typically seen in process systems:sensor bias,process fault and gain change fault.Through experiments with a numerical example and real data from a thermal power plant,the DPFA algorithm is shown to be superior to the state-of-the-art methods,in terms of better monitoring performance(fault detection rate and false alarm rate)and lower computational complexity.
基金Project (20110023110014) supported by Specialized Research Fund for the Doctoral Program of Higher Education of ChinaProject (2010QD01) supported by Fundamental Research Funds for the Central Universities,China
文摘Differential interferometric synthetic aperture radar （DInSAR） technology is a new method to monitor the dynamic surface subsidence. It can monitor the large scope of dynamic deformation process of surface subsidence basin and better reflect the surface subsidence form in different stages. But under the influence of factors such as noise and other factors, the tilt and horizontal deformation curves regularity calculated by DInSAR data are poorer and the actual deviation is larger. The tilt and horizontal deformations are the important indices for the safety of surface objects protection. Numerical simulation method was used to study the dynamic deformation of LW32 of West Cliff colliery in Australia based on the DInSAR monitoring data. The result indicates that the subsidence curves of two methods fit well and the correlation coefficient is more than 95%. The other deformations calculated by numerical simulation results are close to the theory form. Therefore, considering the influence, the surface and its subsidiary structures and buildings due to mining, the numerical simulation method based on the DInSAR data can reveal the distribution rules of the surface dynamic deformation values and supply the shortcomings of DInSAR technology. The research shows that the method has good applicability and can provide reference for similar situation.
基金supported by the Pilot Project of Sinopec(P14085)
文摘The phase velocity of seismic waves varies with the propagation frequency, and thus frequency-dependent phenomena appear when CO2 gas is injected into a reservoir. By dynamically considering these phenomena with reservoir conditions it is thus feasible to extract the frequency-dependent velocity factor with the aim of monitoring changes in the reservoir both before and after CO2 injection. In the paper, we derive a quantitative expression for the frequency-dependent factor based on the Robinson seismic convolution model. In addition, an inversion equation with a frequency-dependent velocity factor is constructed, and a procedure is implemented using the following four processing steps： decomposition of the spectrum by generalized S transform, wavelet extraction of cross-well seismic traces, spectrum equalization processing, and an extraction method for frequency-dependent velocity factor based on the damped least-square algorithm. An attenuation layered model is then established based on changes in the Q value of the viscoelastic medium, and spectra of migration profiles from forward modeling are obtained and analyzed. Frequency-dependent factors are extracted and compared, and the effectiveness of the method is then verified using a synthetic data. The frequency-dependent velocity factor is finally applied to target processing and oil displacement monitoring based on real seismic data obtained before and after CO2 injection in the G89 well block within Shengli oilfield. Profiles and slices of the frequency-dependent factor determine its ability to indicate differences in CO2 flooding, and the predicting results are highly consistent with those of practical investigations within the well block.
基金supported by the Research Funding Project for Graduate Education and Teaching Reform of Beijing University of Posts and Telecommunications(No.2024Y036)the Postgraduate Education and Teaching Reform Research Fund Project of Beijing University of Posts and Telecommunications(No.2024Z007)the Postgraduate Education and Teaching Reform Project of Beijing University of Posts and Telecommunications(2025).
文摘Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings in dynamics,interdisciplinary integration,and industry adaptability.It builds a multi-dimensional dynamic model covering seven core dimensions with quantitative scoring,non-linear weighting,and DivClust grouping.An intelligent platform with real-time monitoring,early warning,and personalized recommendations integrates AI like multi-modal fusion and large-model diagnosis.The“monitoring-warning-improvement”loop helps optimize training programs,support personalized planning,and bridge talent-industry gaps,enabling digital transformation in software engineering education evaluation.
基金National Natural Science Foundation of China,No.71974070, No.41501593National Key R&D Project,No.2016YFA0602500Humanities and Social Sciences Foundation of Ministry of Education of China,No.19YJCZH068。
文摘Exploring the spatio-temporal dynamics of poverty is important for research on sustainable poverty reduction in China. Based on the perspective of development geography, this paper proposes a panel vector autoregressive(PVAR) model that combines the human development approach with the global indicator framework for Sustainable Development Goals(SDGs) to identify the poverty-causing and the poverty-reducing factors in China. The aim is to measure the multidimensional poverty index(MPI) of China’s provinces from 2007 to 2017, and use the exploratory spatio-temporal data analysis(ESTDA) method to reveal the characteristics of the spatio-temporal dynamics of multidimensional poverty. The results show the following:(1) The poverty-causing factors in China include the high social gross dependency ratio and crop-to-disaster ratio, and the poverty-reducing factors include the high per capita GDP, per capita social security expenditure, per capita public health expenditure, number of hospitals per 10,000 people, rate of participation in the new rural cooperative medical scheme, vegetation coverage, per capita education expenditure, number of universities, per capita research and development(R&D) expenditure, and funding per capita for cultural undertakings.(2) From 2007 to 2017, provincial income poverty(IP), health poverty(HP), cultural poverty(CP), and multidimensional poverty have been significantly reduced in China, and the overall national poverty has dropped by 5.67% annually. there is a differentiation in poverty along different dimensions in certain provinces.(3) During the study period, the local spatial pattern of multidimensional poverty between provinces showed strong spatial dynamics, and a trend of increase from the eastern to the central and western regions was noted. The MPI among provinces exhibited a strong spatial dependence over time to form a pattern of decrease from northwestern and northeastern China to the surrounding areas.(4) The spatio-temporal networks of multidimensional poverty in adjacent provinces were mainly negatively correlated, with only Shaanxi and Henan, Shaanxi and Ningxia, Qinghai and Gansu, Hubei and Anhui, Sichuan and Guizhou, and Hainan and Guangdong forming spatially strong cooperative poverty reduction relationships. These results have important reference value for the implementation of China’s poverty alleviation strategy.
基金This work was financially supported by the National Natural Science Foundation of China, No.50074002.
文摘The study concentrates mainly on the development of failure process incomposite rock mass. By use of acoustic emission (AE), convergence inspection, pressure monitoring,level measurement techniques and the modem signal analysis technology, as well as scan electronmicroscopy (SEM) experiment, various aspects of nonlinear dynamic damage of composite rock masssurrounding the transport roadway in Linglong gold mine are discussed. According to the monitoringresults, the stability of the rock mass can be synthetically evaluated, and the intrinsic relationbetween the damage and the characteristic parameters of acoustic emission can be determined. Thelocation of the damage of rock mass can also be detected based on the acoustic emission couplemonitoring signals. Finally, the key factors which influence the stability of the transport roadwaysupported by composite hard rock materials are found out.
基金supported by the Sci-tech Research Development Program of Shaanxi Province (No.2015SF015)
文摘The aim of this study was to evaluate the effects of low-dose tibolone therapy on ovarian area, uterine volume and endometrial thickness, and define the cut-off value of endometrial thickness for curettage during uterine bleeding. We followed 619 postmenopausal women, aged 40-60 years, for two years. There were 301 subjects in the low-dose tibolone treatment group and 318 subjects in the control group. The ovarian area, uterine volume and endometrial thickness in all participants were measured by transvaginal ultrasound prior to, one and two years post enrollment, respectively. Endometrial specimens were collected from all subjects with abnormal uterine bleeding during the follow-up period. We found that the uterine volume in the treatment group was greater than that in the control group, and the difference was significant （P〈0.05）, but there were no significant differences in ovarian area and endometrial thickness between the two groups （P〉0.05）. When the cut-off value for endometrial thickness was 7.35 ram, the sensitivity and specificity were 100% and 79.07%, respectively, and 85.71% and 93.02% when 7.55 mm was set as the cut-offduring tibolone therapy. The results indicate that low-dose tibolone therapy may postpone uterine atrophy and the cut-off value of endometrial thickness may be appropriately adjusted for curettage.
基金The authors are grateful for the financial support from the National Natural Science Foundation of China(Grant Nos.42177143,42277461)the Science Foundation for Distinguished Young Scholars of Sichuan Province(Grant No.2020JDJQ0011).Thanks to the Chn Energy Dadu River Hydropower Development Co.,Ltd,China Three Gorges Construction Engineering Corporation,Yalong River Hydropower Development Company,Ltd,Power China Chengdu Engineering Co.,Ltd,Power China Northwest Engineering Co.,Ltd,Power China Sinohydro Bureau 7 Co.,Ltd,China Gezhouba Group No.1 Engineering Co.,Ltd.,and the 5th Engineering Co.,Ltd.of China Railway Construction Bridge Engineering Bureau Group for the support and assistance.
文摘The underground hydropower projects in Southwest China is characterized by large excavation sizes,high geostresses,complicated geological conditions and multiple construction processes.Various disasters such as collapses,large deformations,rockbursts are frequently encountered,resulting in serious casualties and huge economic losses.This review mainly presents some representative results on microseismic(MS)monitoring and forecasting for disasters in hydropower underground engineering.First,a set of new denoising,spectral analysis,and location methods were developed for better identification and location of MS signals.Then,the tempo-spatial characteristics of MS events were analyzed to understand the relationship between field construction and damages of surrounding rocks.Combined with field construction,geological data,numerical simulation and parametric analysis of MS sources,the focal mechanism of MS events was revealed.A damage constitutive model considering MS fracturing size was put forward and feedback analysis considering the MS damage of underground surrounding rocks was conducted.Next,an MS multi-parameter based risk assessment and early warning method for dynamic disasters were proposed.The technology for control of the damage and deformation of underground surrounding rocks was proposed for underground caverns.Finally,two typical underground powerhouses were selected as case studies.These achievements can provide significant references for prevention and control of dynamic disasters for underground engineering with similar complicated geological conditions.
基金financial support from the Fundamental Research Funds for the Central Universities of China (No. 2015QNB19)the financial support from the Open Fund of Key Laboratory of Safety and High-efficiency Coal Mining, Ministry of Education of China (No. JYBSYS2015107)+2 种基金the National Natural Science Foundation of China (Nos. 51404254, 41430317 and U1261202)the China Postdoctoral Science Foundation of China (No. 2014M560465)the Jiangsu Planned Projects for Postdoctoral Research Funds of China (No. 1302050B)
文摘The level of deformation development of surrounding rocks is a vital predictor to evaluate impending coal mine disasters and it is important to establish accurate measurements of the deformed status to ensure coal mine safety. Traditional deformation monitoring methods are mostly based on single parameter, in this paper, multiple approaches are integrated: firstly, both electric and elastic models are established,from which electric field distribution and seismic wave recording are calculated and finally, the resistivity profiles and source position information are determined using inversion methods, from which then the deformation and failure of mine floor are evaluated. According to the inversion results of both electric and seismic field signals, multiple-parameter dynamic monitoring of surrounding rock deformation in deep mine can be performed. The methodology is validated using numerical simulation results which shows that the multi-parameter dynamic monitoring methods have better results for surrounding rock deformation in deep mine monitoring than single parameter methods.
基金Supported by the National Natural Science Foundation of China （No.50378041） and the Specialized Research Fund for the Doctoral Program of Higher Education （No.2003487016）.
文摘Based on digital image processing technique, a real-time system is developed to monitor and detect the dynamic displacement of engineering structures. By processing pictures with a self-programmed software, the real-time coordinate of an object in a certain coordinate system can be obtained, and further dynamic displacement data and curve of the object can also be achieved. That is, automatic gathering and real-time processing of data can be carried out by this system simultaneously. For this system, first, an untouched monitoring technique is adopted, which can monitor or detect objects several to hundreds of meters apart; second, it has flexible installation condition and good monitoring precision of sub-millimeter degree; third, it is fit for dynamic, quasi-dynamic and static monitoring of large engineering structures. Through several tests and applications in large bridges, good reliability and dominance of the system is proved.
基金Supported by the National Key Project of Scientific and Technical Supporting Program(NO.2006BAG04B03)
文摘The construction period of large cable-stayed bridges is long, and the structure deformation is complicated. Any error during construction will potentially affect the cantilever alignments and the internal forces. In order to ensure safety during construction and exactly determine the cantilever alignments, dynamic deformation monitoring is needed immediately when the con struction of the superstructure starts. This paper aims at the requirement of deformation monitoring during the Sutong Bridge construction, and introduces the realization and observing schemes of the GPS and georobot based on remote real-time dynamic geometrical deformation monitoring system, then researches the data processing methods and enumerates some of the application achievements. Long-term operation during the Sutong Bridge construction indicates that the system runs steadily and the results are reliable.
