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.

Coupling sensor observation services and web processing services for online geoprocessing in water dam monitoring

Novel sensor technologies are rapidly emerging.They enable a monitoring and modelling of our environment in a level of detail that was not possible a few years ago.However,while the raw data produced by these sensors are useful to get a first overview,it usually needs to be post-processed and integrated with other data or models in different applications.In this paper,we present an approach for integrating several geoprocessing components in the TaMIS water dam monitoring system developed with the Wupperverband,a regional waterbody authority in Germany.The approach relies upon the OGC Web Processing Service and is tightly coupled with Sensor Observation Service instances running at the Wupperverband.Besides implementing the standardized XML-based interface,lightweight REST APIs have been developed to ease the integration with thin Web clients and other Web-based components.Using this standards-based approach,new processing facilities can be easily integrated and coupled with different observation data sources.

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.

Deformation prediction model of concrete face rockfill dams based on an improved random forest model

The unique structure and complex deformation characteristics of concrete face rockfill dams(CFRDs)create safety monitoring challenges.This study developed an improved random forest(IRF)model for dam health monitoring modeling by replacing the decision tree in the random forest(RF)model with a novel M5'model tree algorithm.The factors affecting dam deformation were preliminarily selected using the statistical model,and the grey relational degree theory was utilized to reduce the dimensions of model input variables.Finally,a deformation prediction model of CFRDs was established using the IRF model.The ten-fold cross-validation method was used to quantitatively analyze the parameters affecting the IRF algorithm.The performance of the established model was verified using data from three specific measurement points on the Jishixia dam and compared with other dam deformation prediction models.At point ES-10,the performance evaluation indices of the IRF model were superior to those of the M5'model tree and RF models and the classical support vector regression(SVR)and back propagation(BP)neural network models,indicating the satisfactory performance of the IRF model.The IRF model also outperformed the SVR and BP models in settlement prediction at points ES2-8 and ES4-10,demonstrating its strong anti-interference and generalization capabilities.This study has developed a novel method for forecasting and analyzing dam settlements with practical significance.Moreover,the established IRF model can also provide guidance for modeling health monitoring of other structures.

Seismic Deformation and Seismic Resistance Analysis of Shapai Roller Compacted Concrete Arch Dam Based on Field Monitoring and Dynamic Finite Element Method

Shapai Roller Compacted Concrete(RCC) Arch Dam is the highest RCC arch dam of the 20th century in the world with a maximum height of 132m,and it is the only concrete arch dam near the epicentre of Wenchuan earthquake on May 12th,2008.The seismic damage to the dam and the resistance of the dam has drawn great attention.This paper analyzed the response and resistance of the dam to the seismic wave using numerical simulations with comparison to the monitored data.The field investigation after the earthquake and analysis of insitu data record showed that there was only little variation in the opening size at the dam and foundation interface,transverse joints and inducing joints before and after the earthquake.The overall stability of the dam abutment resistance body was quite good except a little relaxation was observed.The results of the dynamic finite element method(FEM) showed that the sizes of the openings obtained from the numerical modeling are comparable with the monitored values,and the change of the opening size is in millimeter range.This study revealed that Shapai arch dam exhibited high seismic resistance and overload capacity in the Wenchuan earthquake event.The comparison of the monitored and simulated results showed that the numerical method applied in this paper well simulated the seismic response of the dam.The method could be useful in the future application on the safety evaluation of RCC dams.

Information fusion diagnosis and early-warning method for monitoring the long-term service safety of high dams

Analyzing the service behavior of high dams and establishing early-warning systems for them have become increasingly important in ensuring their long-term service.Current analysis methods used to obtain safety monitoring data are suited only to single survey point data.Unreliable or even paradoxical results are inevitably obtained when processing large amounts of monitoring data,thereby causing difficulty in acquiring precise conclusions.Therefore,we have developed a new method based on multi-source information fusion for conducting a comprehensive analysis of prototype monitoring data of high dams.In addition,we propose the use of decision information entropy analysis for building a diagnosis and early-warning system for the long-term service of high dams.Data metrics reduction is achieved using information fusion at the data level.A Bayesian information fusion is then conducted at the decision level to obtain a comprehensive diagnosis.Early-warning outcomes can be released after sorting analysis results from multi-positions in the dam according to importance.A case study indicates that the new method can effectively handle large amounts of monitoring data from numerous survey points.It can likewise obtain precise real-time results and export comprehensive early-warning outcomes from multi-positions of high dams.

Singular value diagnosis in dam safety monitoring effect values

Based on the principal component analysis, principal components that have major influence on data variance are determined by the energy percentage method according to the correlation between monitoring effects. Then principal components are extracted through reconstructing multi effects. Moreover, combining with the optimal estimation theory, the method of singular value diagnosis in dam safety monitoring effect values is proposed. After dam monitoring information matrix is obtained, single effect state estimation matrix and multi effect fusion estimation matrix are constructed to make diagnosis on singular values to reduce false alarm rate. And the diagnosis index is calculated by PCA. These methods have already been applied to an actual project and the result shows the ability of the monitoring effect reflecting dam evolution behavior is improved as dam safety monitoring effect fusion estimation can take accurate identification on singular values and achieve data reduction, filter out noise and lower false alarm rate effectively.

Long-term dam safety monitoring of Punt dal Gall arch dam in Switzerland

The 130m high Punt dal Gall dam is located at the Swiss-Italian border in the South-eastern part of Switzerland and was completed in 1969.The dam is founded on highly folded and partially crushed dolomite and limestone formations.A grout curtain with an area of 120,000m^(2) was provided for controlling seepage.For the monitoring of the dam deformations five inverted pendulums were installed in the dam and three in the rock foundation of the right abutment outside of the dam.For a seasonal water level fluctuation in the reservoir of about 60 m the maximum amplitude of the radial displacement is 25 mm,which includes both the effects of the water load and temperature effects.Furthermore a comprehensive geodetic network was established,57 joint meters were installed and cracks in the crest gallery are monitored by crack meters.There are also thermometers,piczometers and rocmeters.Springs at the left and fight banks of the dam are monitored and chemical analyses of the seepage water and springs are performed regularly.The dam is equipped with strong motion instruments and several near-field earthquakes have been recorded in the past.The paper describes the long-term safety monitoring of this 42 years old arch dam.A short description of the Swiss practice in dam safety monitoring and emergency planning is also given.

Study on early warning index of spatial deformation for high concrete dam

Owing to the deficiency in early warning for high concrete dam,a formula was established to characterize the deformation behavior of high concrete dam as a whole.The early warning index of spatial deformation was calculated by low probability principle on the basis of the deformation entropy formula proposed.The steps were as follows.Firstly,the probability method was utilized to define the degree of order and degree of chaos for each survey point.Secondly,the weight distribution among the survey points was calculated by projection pursuit analysis.Thirdly,the formula of holistic deformation entropy,which can represent the degree of order of high concrete dam,was established on the basis of synergetics and information entropy.Lastly,the early warning index of deformation entropy was computed by low probability method based on series of calculated deformation entropy values.An example showed that the dynamic property of deformation entropy is in accordance with both environmental variables and deflection deformation.Moreover,deformation entropy can be used to improve warning ability and safety management for high concrete dam.

Embankment dam foundation analysis for the decrease of internal erosion likelihood

A zoned embankment dam is founded on clay underlain by a sand deposit.Major seepage phenomena were noticed in the foundation downstream from the dam where the vertical seepage forces in the sand layer were expected to exceed the downward forces due to the overlying clay.Modern technologies were applied to delineate critical zones to help design optimal rehabilitation measures.A global electromagnetic survey was carried out to detect and map the main sources,pathways and exits of seepage.Based on these global findings,a more detailed analysis was then conducted to identify zones where thickness of the foundation clay is minimal,pore pressures in sand are higher and thus where the factor of safety against uplift is lower and internal erosion is more likely to occur.Clay thickness evaluation required the determination of land surface as well as clay-sand contact elevations.A laser airborne survey was performed to model the land surface elevation.Data concerning the clay-sand contact elevation came from the interpreted stratigraphy based on a series of boreholes and cone penetration tests.This data was combined in a geostatistical model along with the measured piezometric levels in the foundation.This resulted in a contour map showing factors of safety against uplift over the entire downstream area.The use of modern technologies,namely electromagnetic and laser surveys as well as geostatistical tools,was instrumental in defining the limits of an otherwise spread-out problem and to provide an optimal solution,in terms of costs and effectiveness,for the long-term stabilization of the foundation.