Seismic safety | As a result, it was possible not only to monitor work
Above: Aerial view of Kariba Dam on the Zambezi River basin between Zambia and Zimbabwe. The Zambezi River Authority has been discussing recent seismic activity around the dam
in real time, but also to adapt processes accordingly as needed. Bauer says, thanks to the support of the experienced team on site as well as laboratory tests carried out in Germany, the quality of the plastic concrete used for the cut-off wall was optimised. The seismic upgrade of the John Hart Dam has been described as a milestone from a technical and ecological standpoint. The extensive works will help to safeguard the regional water supply while minimising the environmental impact at the same time, and Pattison says Bauer was proud to have made an important contribution to the region with the successful completion of a challenging aspect of the dam safety upgrade. BC Hydro’s upgrading project at John Hart dam will also include permanent flood and flow upgrades, notably a new overflow spillway below the road deck on top of the dam and replacement of three spillway gates and related works, such as the hoist system. Furthermore, new and/or upgraded spillway gates on all three of the Strathcona, Ladore and John Hart dams in the system, will allow improved flow regulation in the event of a major earthquake.
Kariba Dam The Zambezi River Authority recently confirmed the
Below: Recognising constraints and evaluating the influence of various methods is essential for improving the comprehension and effectiveness of dam safety assessments. With this in mind, an extensive study was recently carried out to evaluate the seismic response variability of the 130m high Pine Flat Dam in California, US.
occurrence of earthquake incidents in the region surrounding the Kariba Dam. The most recent incident with a magnitude M4.9 occurred on 23 December 2024, with its epicentre approximately 19km from Kariba Dam at a depth of 10km. This region is located within the Kariba reservoir, directly behind the Antelope Island in Zimbabwe. In a press release, the authority stated it: “wishes
to advise its stakeholders and the public at large that earthquakes are primarily reservoir-induced- seismic (RIS) activities that are triggered by not only the presence, but also the water level fluctuations from the drawing down of the Kariba reservoir. The earthquakes are not only limited to Kariba as they have been reported from the filling and subsequent operation of several other large dams and reservoirs worldwide”. In the interest of dam safety, the authority went
on to say it doesn’t focus on magnitudes of the earthquakes, but the ground accelerations or vibrations generated from the event. The vibrations/ shaking are further influenced by such characteristics as distance and depth of the epicentre. Therefore, an earthquake with a smaller magnitude but with its epicentre closer to the dam wall could be more damaging than a large earthquake with an epicentre located far away from the dam wall. “Within this context,” Zambezi River Authority
References
Song T, Guan D, Wang Z and Ghorbani H (2024), Maximizing hydropower station safety against earthquake through extreme learning machine-enabled shear waves velocity prediction. Front. Environ. Sci. 12:1414461. doi: 10.3389/fenvs.2024.1414461
stated, “the earthquakes that have been experienced in Kariba, Siavonga and surrounding areas since the construction of the Kariba Dam are relatively small and have not generated large ground accelerations or vibrations as to cause concern regarding the safety of the dam wall.” As part of emergency preparedness and standard operating procedures following the occurrence of unusual incidents, the authority inspects the dam and analyses data from the various dam monitoring instruments each time there is an an earthquake incident. These did not reveal any unusual
16 | February 2025 |
www.waterpowermagazine.com
observations and the dam continued to exhibit normal behaviour that is consistent with prevailing loading conditions. The Zambezi River Authority is a bilateral
organisation jointly owned by the Governments of the Republics of Zambia and Zimbabwe with the primary function to operate, maintain, monitor and regulate the water level in the Kariba Reservoir on the Zambezi River.
Station safety
Seismic activity is highly complex, and even with advanced data analytics tools, there are still many unknown factors that influence when and where an earthquake will strike. As such, current efforts are focused on improving understanding of seismic phenomena and developing more sophisticated tools to detect and respond to earthquakes as they occur. This is what Tao Song et al have set out to achieve in their recent paper published in Frontiers in Environmental Science. Here the researchers focused on maximising hydropower station safety against earthquakes through extreme learning machine- enabled shear waves velocity prediction. As the authors explain, to evaluate the potential shear rate of surrounding rock layers, the shear wave velocity (Vs) parameter can be a useful tool. This parameter helps to determine the velocity at which shear waves travel through the rock layers, which can indicate their stability and susceptibility to earthquakes. This study investigated the significance of the Vs parameter in evaluating the potential shear rate of rock layers surrounding hydropower stations and how it can be used to ensure their safety and efficiency in earthquake-prone regions. Furthermore, the researchers say they proposed a novel approach which involves using extreme learning machine (ELM) technology to predict Vs and enhance the seismic safety of hydropower stations. The ELM model predicts the Vs of the soil layers around the hydropower station, a crucial factor in determining the structure’s seismic response. The predicted Vs is then used to assess seismic hazard and then design appropriate safety measures. In this study, a large dataset of 3200 data points was analysed to gain insights into the rock formations of hydropower plants’ foundations. The dataset contained information about measured depth, rock type, gamma ray log, thermal neutron porosity, shallow electrical resistivity, deep electrical resistivity, compressive wave velocity (Vp), and shear wave velocity (Vs) recorded during vertical seismic logging. The study used four newly developed models and it was found that the multi-layered extreme learning model (ML-ELM) outperformed both the ELM and empirical models, as well as fewer outlier data predictions. Although ELM is simpler and faster than ML-ELM, since it has only one hidden layer with random weights and does not involve backpropagation, ML-ELM has the advantage of learning more complex patterns in data due to its multiple hidden layers with non-linear transformations. The authors believe this approach shows promise
for predicting Vs in similar environments, and future research could explore its effectiveness in other datasets and practical application.
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45
