Seismic safety |


Above: Researchers recently simulated a magnitude 8 earthquake risk scenario at the Vidraru hydropower dam in Romania


Flexible waterproofing elements connecting the facing slab to the crest walls, as well as between wall elements with appropriate capability for accommodating large movements, and post- seismic flooding.


Wave power Other research has recently shown how the shear


wave velocity (Vs) parameter can be used as a useful tool for maximising hydropower station safety against earthquakes. The Vs are a type of seismic wave that move perpendicular to the direction of the wave propagation. They are responsible for causing the most damage during earthquakes as they can create significant ground motion and lead to soil liquefaction and landslides. In Frontiers in Environmental Science, Song et


References


Exposure of Australian dams to seismic hazard from proximal faults by Mark Quigley, Tim Werner, Yuxiang Tang, Dan Clark, Jonathan Griffin, Haibin Yan. Enviroment Systems and


Decisions (2025) 45:24. https://doi.org/10.1007/ s10669-025-10015-4


Maximising hydropower station safety against earthquake through extreme learning machine enabled shear waves velocity prediction. Tao Song, Di Guan, Zhen Wang and Hamzeh Ghorban (2024), Front. Environ. Sci. 12:1414461. doi: 10.3389/ fenvs.2024.1414461


Seismic Resilient Design of Waimea Community Dam by Brian Benson, David Cmeron-Ellis, Peter Amos. ICOLD Chengdou May 2025. Q.111- R.12. DOI: 10.1201/9781003642428-115


al explain how this parameter helps to determine the velocity at which shear waves travel through rock layers, which can indicate their stability and susceptibility to earthquakes. Rock layers with high Vs are more stable and less likely to experience significant shear rates during an earthquake. In contrast, rock layers with low Vs are more susceptible to damage and can experience significant shear rates during an earthquake.


Investigating the significance of the Vs parameter in evaluating the potential shear rate of rock layers surrounding hydropower stations, the authors show how it can be used to ensure their safety and efficiency in earthquake-prone regions.


Machine Learning


As Song et al discuss, machine learning (ML) holds immense promise in enabling proactive safety control by using advanced data analytics to monitor environmental conditions and detect potential safety hazards in real-time, ultimately detecting data patterns that human analysts might miss. Algorithms can learn to recognise subtle changes in seismic activity that might indicate an impending earthquake. The authors say their research uses a novel


approach using vertical seismic profile data, typically collected to assess subsurface formations in oil and gas wells, near various dam construction sites. This methodology aims to facilitate geotectonic


34 | December 2025 | www.waterpowermagazine.com


assessment of the foundation formations of dams and hydropower stations, enabling researchers to gain valuable insights into potential risks and challenges associated with their construction and operation. Additionally, the research introduces a new methodology of using Extreme Learning Machine (ELM) models to predict the Vs of rock layers in hydropower station foundations. The proposed approach uses ELM enabled Vs prediction to assess seismic hazards and design appropriate safety measures. By accurately predicting Vs, this method can assist engineers and policymakers in making informed decisions to mitigate potential risks associated with seismic activity. Seismic activity is highly complex, and even with advanced data analytics tools, the authors say there are still many unknown factors that influence when and where an earthquake will strike. As such, current efforts are focused on improving our understanding of seismic phenomena and developing more sophisticated tools to detect and respond to earthquakes as they occur.


Bhutanese risks


Bhutan is among the most vulnerable countries to natural disasters and climate-related hazards, including earthquakes, droughts, and floods. With its economy highly reliant on the hydropower sector that faces such risks, last year the World Bank approved US$40 million in financing to help strengthen the country’s institutional and technical capacity to manage such increasing hazards. This financing will help strengthen institutions


to safeguard critical infrastructures, including new hydropower projects and buildings as well as strengthening early warning systems and financial resilience of communities. The operation will support safeguarding the


hydropower sector - the country’s economic backbone - from natural disaster and climate risks through measures such as mandating that all new hydropower projects adopt a catchment-wide approach to increase resilience for all stages of development. The Kingdom has already revised the Guidelines for Development of Hydropower Projects and the Dam Safety Guidelines for Hydropower ensuring integrated dam safety and geohazard management.


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