Causes of failure of Weirs & their Remedies

Common causes of failure of weirs include:

• Excessive and progressive downstream erosion, both from within the stream and through lateral erosion of the banks.

• Erosion of inadequately protected abutments.

• Hydraulic removal of fines and other support material from downstream protection (gabions and aprons) resulting in erosion of the apron protection.

• Deterioration of the cutoff and subsequent loss of containment.

• Additional aspects specific to concrete, rockfill or steel structures.

PIPING:

• Piping is caused by groundwater seeping out of the bank face. Grains are detached and entrained by the seepage flow and may be transported away from the bank face by surface runoff generated by the seepage, if there is sufficient volume of flow.

• The exit gradient of water seeping under the base of the weir at the downstream end may exceed a certain critical value of soil. As a result the surface soil starts boiling and is washed away by percolating water. The progressive erosion backwash at the upstream results in the formation of channel (pipe) underneath the floor of weir.

• Since there is always a differential head between upstream & downstream, water is constantly moving form upstream to downstream from under the base of weir. However, if the hydraulic gradient becomes big, greater than the critical value, then at the point of existance of water at the downstream end, it begins to dislodge the soil particles and carry them away.

• In due course, when this erosion continues, a sort of pipe or channel is formed within the floor through which more particles are transported downstream which can bring about failure of weir.

• Piping is especially likely in high banks backed by the valley side, a terrace, or some other high ground. In these locations the high head of water can cause large seepage pressures to occur. Evidence includes: Pronounced seep lines, especially along sand layers or lenses in the bank; pipe shaped cavities in the bank; notches in the bank associated with seepage zones and layers; run-out deposits of eroded material on the lower bank.

Remedies:

• Decrease Hydraulic gradient i.e. increase path of percolation by providing sufficient length of impervious floor.

• Providing curtains or piles at both upstream and downstream.

RUPTURE OF FLOOR DUE TO UPLIFT:

If the weight of the floor is insufficient to resist the uplift pressure, the floor may burst. This bursting of the floor reduces the effective length of the impervious floor, which will resulting increasing exit gradient, and can cause failure of the weir.

Remedies:

• Providing impervious floor of sufficient length of appropriate thickness.

• Pile at upstream to reduce uplift pressure downstream.

RUPTURE OF FLOOR DUE TO SUCTION CAUSED BY STANDING WAVES:

Hydraulic jump formed at the downstream of water.

Remedies:

• Additional thickness

• Floor thickness in one concrete mass

SCOUR ON THE UPSTREAM AND DOWNSTREAM OF THE WEIR:

Scouring in Weirs Occurs due to contraction of natural water way.

Remedies:

• Piles at greater depth than scour level

(Courtesy : http://www.aboutcivil.org)

Seminar Topics

1. Feasibility Of Drip Irrigation
2. Methods Of Ground Water Recharge & It’s Effect On Quality Of Ground Water
3. Technological Potential For Improvements Of Water Harvesting
4. Coastal   Protection Structures
5. Relevance Of Remote Sensing And GIS In Civil Engineering
6. Surge Tanks
7. Sewage Water As The Source Of Irrigation And Plant Nutrients
8. Study Of Coastal Villages Affected By  Salinity Ingress In Amreli District
9. Socio Economic Analysis Of The Kalpsar Project
10. Soil Erosion And It’s Controlling Techniques
11. Land Reclamation For Agricultural Purpose
12. A Case Study On: Social And Environmental Impacts Of Sardar Sarovar Dam Project
13. Advanced Technologies In Sewer Maintenance
14. Review And Analysis Of Drought Monitoring And Management In India
15. Digital Elevation Model
16. Seminar On Subsurface Dams
17. Impact Of Climate Change On Groundwater Resources
18. Reclamation Of Waterlogged And Saline Soils For Agricultural Purpose
19. Recycling Of Waste Water
20. Importance of Liners For Canals
21. Qyality Analysis Of Ground Water
22. Qualitative Anlysis Of Irrigation Water
23. Roof Rainwater Harvesting – A Case Study
24. Groundwater Potential And Problems : A Case Study
25. Rain Water Harvesting And Ground Water Conservation
26. Interlinking Of Indian Rivers -Challenges And Prospects OR Interlinking Of Indian Rivers intrusion Detection System
27. Surface Water Pre-treatment Using Floating Media Filter
28. GIS – For Rain Water Recharge To Enhance The Ground Water
29. Ground Water Conservation By Artificial Recharge Techniques
30. Impact Of Urbanization On Groundwater Resources
31. Application Of Remote Sensing & G.I.S. In Groundwater Prospecting
32. Water Evaporation And Weeds Control By Mulching In Drip Irrigation System
33. Sediment And Water Quality Analysis
34. Remote Sensing , GIS And Its Applications
35. Water Resources Development in Developing Countries
36. Piano Key Spillways for Dams
37. Proposed site for Bandhara In order to Raise Ground Water level.
38. Fusegate System for Ungated Irrigation Scheme

Model Evaluation Methods

Root Mean Square Error (RMSE)
The Root Mean Square Error (RMSE) (also called the root mean square deviation, RMSD) is a frequently used measure of the difference between values predicted by a model and the values actually observed from the environment that is being modelled. These individual differences are also called residuals, and the RMSE serves to aggregate them into a single measure of predictive power.

The RMSE of a model prediction with respect to the estimated variable X_model is defined as the square root of the mean squared error:

where X_obs is observed values and X_model is modelled values at time/place i.

The RMSE values can be used to distinguish model performance in a calibration period with that of a validation period as well as to compare the individual model performance to that of other predictive models.

Normalized Root Mean Square Error (NRMSE)

Non-dimensional forms of the RMSE are useful because often one wants to compare RMSE with different units. There are two approaches: normalize the RMSE to the range of the observed data, or normalize to the mean of the observed data.

(the latter one is also called C_V,RMSE for the resemblance with calculating the coefficient of variance).

Pearson Correlation Coefficient (r)

Correlation – often measured as a correlation coefficient – indicates the strength and direction of a linear relationship between two variables (for example model output and observed values). A number of different coefficients are used for different situations. The best known is the Pearson product-moment correlation coefficient (also called Pearson correlation coefficient or the sample correlation coefficient), which is obtained by dividing the covariance of the two variables by the product of their standard deviations. If we have a series n observations and n model values, then the Pearson product-moment correlation coefficient can be used to estimate the correlation between model and observations.

The correlation is +1 in the case of a perfect increasing linear relationship, and -1 in case of a decreasing linear relationship, and the values in between indicates the degree of linear relationship between for example model and observations. A correlation coefficient of 0 means the there is no linear relationship between the variables.

The square of the Pearson correlation coefficient (r²), known as the coefficient of determination, describes how much of the variance between the two variables is described by the linear fit.

Nash-Sutcliffe Coefficient (E)

The Nash-Sutcliffe model efficiency coefficient (E) is commonly used to assess the predictive power of hydrological discharge models. However, it can also be used to quantitatively describe the accuracy of model outputs for other things than discharge (such as nutrient loadings, temperature, concentrations etc.). It is defined as:

where X_obs is observed values and X_model is modelled values at time/place i.

Nash-Sutcliffe efficiencies can range from -¥ to 1. An efficiency of 1 (E = 1) corresponds to a perfect match between model and observations. An efficiency of 0 indicates that the model predictions are as accurate as the mean of the observed data, whereas an efficiency less than zero (-¥ < E < 0) occurs when the observed mean is a better predictor than the model.

Essentially, the closer the model efficiency is to 1, the more accurate the model is.