Improving autoencoder-based unsupervised damage detection in uncontrolled structural health monitoring under noisy conditions

Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.

Structural Health Monitoring by Accelerometric Data of a Continuously Monitored Structure with Induced Damages

The possibility of determining the integrity of a real structure subjected to non-invasive and non-destructive monitoring,such as that carried out by a series of accelerometers placed on the structure,is certainly a goal of extreme and current interest.In the present work,the results obtained from the processing of experimental data of a real structure are shown.The analyzed structure is a lattice structure approximately 9 m high,monitored with 18 uniaxial accelerometers positioned in pairs on 9 different levels.The data used refer to continuous monitoring that lasted for a total of 1 year,during which minor damage was caused to the structure by alternatively removing some bracings and repositioning them in the structure.Two methodologies detecting damage based on decomposition techniques of the acquired data were used and tested,as well as a methodology combining the two techniques.The results obtained are extremely interesting,as all the minor damage caused to the structure was identified by the processing methods used,based solely on the monitored data and without any knowledge of the real structure being analyzed.The results use 15 acquisitions in environmental conditions lasting 10 min each,a reasonable amount of time to get immediate feedback on possible damage to the structure.

Research on High-Velocity Impact Damage Monitoring Method of CFRP Based on Guided Wave

Carbon fiber-reinforced polymer(CFRP)is widely used in aerospace applications.This kind of material may face the threat of high-velocity impact in the process of dedicated service,and the relevant research mainly considers the impact resistance of the material,and lacks the high-velocity impact damage monitoring research of CFRP.To solve this problem,a real high-velocity impact damage experiment and structural health monitoring(SHM)method of CFRP plate based on piezoelectric guided wave is proposed.The results show that CFRP has obvious perforation damage and fiber breakage when high-velocity impact occurs.It is also proved that guided wave SHM technology can be effectively used in the monitoring of such damage,and the damage can be reflected by quantifying the signal changes and damage index(DI).It provides a reference for further research on guided wave structure monitoring of high/hyper-velocity impact damage of CFRP.

Assessing the corrosion protection property of coatings loaded with corrosion inhibitors using the real-time atmospheric corrosion monitoring technique

The atmospheric corrosion monitoring(ACM)technique has been widely employed to track the real-time corrosion behavior of metal materials.However,limited studies have applied ACM to the corrosion protection properties of organic coatings.This study compared a bare epoxy coating with one containing zinc phosphate corrosion inhibitors,both applied on ACM sensors,to observe their corrosion protection properties over time.Coatings with artificial damage via scratches were exposed to immersion and alternating dry and wet environments,which allowed for monitoring galvanic corrosion currents in real-time.Throughout the corrosion tests,the ACM currents of the zinc phosphate/epoxy coating were considerably lower than those of the blank epoxy coating.The trend in ACM current variations closely matched the results obtained from regular electrochemical tests and surface analysis.This alignment highlights the potential of the ACM technique in evaluating the corrosion protection capabilities of organic coatings.Compared with the blank epoxy coating,the zinc phosphate/epoxy coating showed much-decreased ACM current values that confirmed the effective inhibition of zinc phosphate against steel corrosion beneath the damaged coating.

Structural Modal Parameter Recognition and Related Damage Identification Methods under Environmental Excitations: A Review

Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.

Genetic Regression Model for Dam Safety Monitoring

Under-fitting problems usually occur in regression models for dam safety monitoring.To overcome the local convergence of the regression, a genetic algorithm (GA) was proposed using a real parameter coding, a ranking selection operator, an arithmetical crossover operator and a uniform mutation operator, and calculated the least-square error of the observed and computed values as its fitness function. The elitist strategy was used to improve the speed of the convergence. After that, the modified genetic algorithm was applied to reassess the coefficients of the regression model and a genetic regression model was set up. As an example, a slotted gravity dam in the Northeast of China was introduced. The computational results show that the genetic regression model can solve the under-fitting problems perfectly.

Monitoring and Early Warning of Paddy Rice Low Temperature Cold Damage in Ningxia with the Support of GIS and RS

The low temperature cold damage of paddy rice is the major agricultural meteorological disaster in Ningxia.The real-time monitoring and early warning of low temperature cold damage are very important to develop the advantages,avoid the disadvantages and reduce the disaster losses.Based on the prior researches on the low temperature cold damage indexes of paddy rice,we improved the small grid reckoning method of temperature and the reckoning precision with the support of GIS.By using the multitemporal remote sensing data,the paddy rice planting zone was picked.The calculation results of low temperature cold damage monitoring indexes were combined with the paddy rice planting zone,which judged the grade distribution and zone of low temperature cold damage in real time.Meanwhile,the low temperature cold damage of paddy rice was done the early warning,and the automatic monitoring early warning system was developed by using the weather forecast data.This method and system were applied to the business works,and the monitoring and early warning products of paddy rice low temperature cold damage business were made.The monitoring results basically corresponded with the actual situation,and the better monitoring service effect was gained.

Occurrence and Damage of Maize Gray Leaf Spot and Its GPS Monitoring

Maize gray leaf spot is a kind of leaf disease seriously threatened the production of maize,which occurs all around the world.The occurrence and damage conditions of maize gray leaf spot at home and abroad and in Yunnan Province are described,the latest research process of maize gray leaf spot are summarized,and the integrated control methods of maize gray leaf spot and its GPS monitoring are further introduced.

Monitoring models for base flow effect and daily variation of dam seepage elements considering time lag effect

Affected by external environmental factors and evolution of dam performance, dam seepage behavior shows nonlinear time-varying characteristics. In this study, to predict and evaluate the long-term development trend and short-term fluctuation of the dam seepage behavior, two monitoring models were developed, one for the base flow effect and one for daily variation of dam seepage elements. In the first model, to avoid the influence of the time lag effect on the evaluation of seepage variation with the time effect component of seepage elements, the base values of the seepage element and the reservoir water level were extracted using the wavelet multi-resolution analysis method, and the time effect component was separated by the established base flow effect monitoring model. For the development of the daily variation monitoring model for dam seepage elements, all the previous factors, of which the measured time series prior to the dam seepage element monitoring time may have certain influence on the monitored results, were considered. Those factors that were positively correlated with the analyzed seepage element were initially considered to be the support vector machine(SVM) model input factors, and then the SVM kernel function-based sensitivity analysis was performed to optimize the input factor set and establish the optimized daily variation SVM model. The efficiency and rationality of the two models were verified by case studies of the water level of two piezometric tubes buried under the slope of a concrete gravity dam.Sensitivity analysis of the optimized SVM model shows that the influences of the daily variation of the upstream reservoir water level and rainfall on the daily variation of piezometric tube water level are processes subject to normal distribution.

一种S波段雷达通信电子战多功能宽带DAM的设计

本文创新性地提出了一种S波段多功能宽带DAM,首先介绍了该型DAM的顶层架构设计,接着介绍了各个功能组成模块的设计过程,最后给出了该型DAM的指标实现情况。该型DAM可以分时实现雷达、通信及电子战功能,最大瞬时带宽可达200MHz,支持任意波形产生,在多功能综合射频系统中具有良好的应用前景。

Applications of online monitoring technology for tailings dam on digital mine

Considering its structural features, geometric shapes, service mode, environmental media, mechanical behavior, etc, the special nature and complexity of tailings dam were summarized. The technical approach to safety management for tailings dam was proposed, which is the on-line automated monitoring and early warning information. The results show that a strong theoretical basis can be provided for security monitoring and security management of tailings dam. Online automated monitoring system for tailings dam has full implementation of the information. It is applied widely in Lingnan gold mine, Xiadian gold mine and Hedong gold mine in Zhaoyuan, Shandong Province, and achieves good effect.

Seepage safety monitoring model for an earth rock dam under influence of high-impact typhoons based on particle swarm optimization algorithm

Extreme hydrological events induced by typhoons in reservoir areas have presented severe challenges to the safe operation of hydraulic structures. Based on analysis of the seepage characteristics of an earth rock dam, a novel seepage safety monitoring model was constructed in this study. The nonlinear influence processes of the antecedent reservoir water level and rainfall were assumed to follow normal distributions. The particle swarm optimization （PSO） algorithm was used to optimize the model parameters so as to raise the fitting accuracy. In addition, a mutation factor was introduced to simulate the sudden increase in the piezometric level induced by short-duration heavy rainfall and the possible historical extreme reservoir water level during a typhoon. In order to verify the efficacy of this model, the earth rock dam of the Siminghu Reservoir was used as an example. The piezometric level at the SW1-2 measuring point during Typhoon Fitow in 2013 was fitted with the present model, and a corresponding theoretical expression was established. Comparison of fitting results of the piezometric level obtained from the present statistical model and traditional statistical model with monitored values during the typhoon shows that the present model has a higher fitting accuracy and can simulate the uprush feature of the seepage pressure during the typhoon perfectly.

An intelligent singular value diagnostic method for concrete dam deformation monitoring

Extracting implicit anomaly information through deformation monitoring data mining is highly significant to determining dam safety status.As an intelligent singular value diagnostic method for concrete dam deformation monitoring, shallow neural network models result in local optima and overfitting, and require manual feature extraction.To obtain an intelligent singular value diagnosis model that can be used for dam safety monitoring, a convolutional neural network (CNN) model that has advantages of deep learning (DL), such as automatic feature extraction, good model fitting, and strong generalizability, was trained in this study.An engineering example shows that the predicted result of the intelligent singular value diagnostic method based on CNN is highly compatible with the confusion matrix, with a precision of 92.41%, receiver operating characteristic (ROC) coordinates of (0.03, 0.97), an area-under-curve (AUC) value of 0.99, and an F1-score of 0.91.Moreover, the performance of the CNN model is better than those of models based on decision tree (DT) and k-nearest neighbor (KNN) methods.Therefore, the intelligent singular value diagnostic method based on CNN is simple to operate, highly intelligent, and highly reliable, and it has a high potential for application in engineering.

Real-Time Spreading Thickness Monitoring of High-core Rockfill Dam Based on K-nearest Neighbor Algorithm

During the storehouse surface rolling construction of a core rockfilldam, the spreading thickness of dam face is an important factor that affects the construction quality of the dam storehouse' rolling surface and the overallquality of the entire dam. Currently, the method used to monitor and controlspreading thickness during the dam construction process is artificialsampling check after spreading, which makes it difficult to monitor the entire dam storehouse surface. In this paper, we present an in-depth study based on real-time monitoring and controltheory of storehouse surface rolling construction and obtain the rolling compaction thickness by analyzing the construction track of the rolling machine. Comparatively, the traditionalmethod can only analyze the rolling thickness of the dam storehouse surface after it has been compacted and cannot determine the thickness of the dam storehouse surface in realtime. To solve these problems, our system monitors the construction progress of the leveling machine and employs a real-time spreading thickness monitoring modelbased on the K-nearest neighbor algorithm. Taking the LHK core rockfilldam in Southwest China as an example, we performed real-time monitoring for the spreading thickness and conducted real-time interactive queries regarding the spreading thickness. This approach provides a new method for controlling the spreading thickness of the core rockfilldam storehouse surface.

Monitoring technique for seepage line of tailings dam

An automatic monitoring technique of the seepage line, including the monitoring design, the automatic monitoring system and the backfill technique of the measuring probe of pore-water pressure, was used in a tailings dam, and a shallow refractive seismic method was investigated for obtaining the seepage line of those areas outside the monitoring zone. The results show that the automatic monitoring has the error within ±3% relative to piezometric tube method and improves monitoring efficiency greatly, and the shallow refractive seismic method has the error within ±10% but expands the area of monitoring. Both of them can be used for a daily measurement in monitoring the seepage line. The result of the automatic monitoring also shows that not only the design of the survey line and the backfill technique of the measuring probe of pore-water pressure are reasonable and economic but also the reliability and safety of the automatic monitoring system are better. Testing result by the shallow refractive seismic method in tailings reveals that the energy excited by hammering iron sheet-pole is strong enough and safe, and that the character of anti-jamming by the detectors with long tailcone is better.

Numerical analysis and geophysical monitoring for stability assessment of the Northwest tailings dam at Westwood Mine

The Westwood Mine aims to reuse the tailings storage facility #1(TSF #1) for solid waste storage, but,downstream of the Northwest dike is considered critical in terms of stability. This paper uses numerical modeling along with geophysical monitoring for assessing the Northwest dike stability during the restoration phase. The impact of waste rock deposition in the upstream TSF #1 is considered. The geophysical monitoring is based on electrical resistivity methods and was used to investigate the internal structure of the dike embankment in different deposition stages. The numerical simulations were performed with SLOPE/W code. The results show a factor of safety well above the minimum recommended value of 1.5. Geophysical monitoring revealed a vertical variation in the electrical resistivity across the dike, which indicates a multilayer structure of the embankment. Without any current in situ data, the geophysical monitoring helped estimating the nature of the materials used and the internal structure of the embankment. These interpretations were validated by geological observation of geotechnical log of the embankment. Based on this study, it is recommended that the water polishing pond be partly filled before waste rock is deposited in TSF #1. In addition, to ensure the stability of the dike, the piezometric head monitoring prior to and during waste rock deposition is recommended.

Safety evaluation of replacement reinforcement quality in abutment contact zones of ultra-high arch dam in first impoundment period based on prototype monitoring

Reinforcement quality evaluation at the abutment is an important research direction. Prototype monitoring and theoretical derivation were integrated to study the replacement reinforcement quality in abutment contact zones of the Xiaowan ultra-high arch dam. The principles of monitoring layout and design are introduced in detail. Prototype monitoring shows that the increment of the interfacial compressive stress is much larger in the impoundment stage than in the regulating stage. The water pressure and time-effect are two main factors affeeting the interfacial stress. The time-effect is the key factor in the initial impoundment stage, and the water pressure is the key factor after impoundment. The contact properties are significantly improved by grouting. This study shows that there are three typical stages in the joint opening hydrographs, namely the compression stage, opening stage, and stable stage. There is a nonlinear relationship between the joint opening and temperature, which can be well described by the S-function. In conclusion, the reinforcement effect is satisfying, and the abutment is safe.

Multi-dimensional database design and implementation of dam safety monitoring system

To improve the effectiveness of dam safety monitoring database systems, the development process of a multi-dimensional conceptual data model was analyzed and a logic design wasachieved in multi-dimensional database mode. The optimal data model was confirmed by identifying data objects, defining relations and reviewing entities. The conversion of relations among entities to external keys and entities and physical attributes to tables and fields was interpreted completely. On this basis, a multi-dimensional database that reflects the management and analysis of a dam safety monitoring system on monitoring data information has been established, for which factual tables and dimensional tables have been designed. Finally, based on service design and user interface design, the dam safety monitoring system has been developed with Delphi as the development tool. This development project shows that the multi-dimensional database can simplify the development process and minimize hidden dangers in the database structure design. It is superior to other dam safety monitoring system development models and can provide a new research direction for system developers.

Theory and Application of System Integration for Real-Time Monitoring of Core Rock-Fill Dam Filling Construction Quality

The theory and method of system integration for the real-time monitoring of core rock-fill dam filling con- struction quality are studied in this paper. First, the importance analysis of system integration factors is carried out with the analytic hierarchy process. Then, according to the analysis result of integration factors, the conceptual model of system integration is built based on function integration, index integration, technology integration and information integration, the index structure of core rock-fill dam filling construction quality control is constructed and the method of function integration and technology integration is studied. The mathematical model of process monitoring is built according to monitoring objective, process and indexes. Research results have been applied in Nuozhadu core rock-fill dam construction management, realizing system integration through building appropriate monitoring work flow and comprehensive information platform of digital dam.

Deformation warning index for reinforced concrete dam based on structural health monitoring data and numerical simulation

The material mechanical parameters of the dam body and foundation will change when a dam is reinforced during the aging process.This causes significant changes in the structural state of the project and makes it difficult to ensure its structural safety.In this study,a new deformation warning index for reinforced concrete dams was developed according to the prototype monitoring data,statistical models,three-dimensional finite element model(FEM)numerical simulation,and the critical conditions of the dam structure.A statistical model was established to separate the water pressure component.Then,a three-dimensional FEM of the reinforced concrete dam was constructed to simulate the water pressure component.Furthermore,the deformation components that affected the mechanical parameters of the dam under the same amount of reservoir water level change were separated and quantified accurately.In addition,the method for inversion of comprehensive mechanical parameters after dam reinforcement was used.The influence mechanisms of the deformation behavior of concrete dams under the reservoir water level and temperature changes were investigated.A new deformation warning index was developed by combining the forward-simulated critical water pressure component and temperature component in the period of extreme temperature decrease with the aging component separated by the statistical model.The new deformation warning index considers the structural state of the dam before and after reinforcement and links the structural strength criterion and the deformation evolution mechanisms.It provides a theoretical foundation and decision support for long-term service and operation management of reinforced dams.