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.

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.

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.

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.