Sara Zouriq

Monitoring dam stability is critical to ensure structural safety and operational reliability. This study integrates Persistent Scatterer Interferometry (PSI) based on Sentinel-1 SAR imagery (2020–2023) with Finite Element Method (FEM) simulations to assess the behavior of the Koyna Dam in India. PSI detected crest displacements between −1.0 and −1.8 mm yr⁻¹, while FEM simulations predicted a maximum vertical displacement of approximately −3.2 mm at the crest. Although these results represent different quantities (time-averaged displacement rates versus peak static displacement), both approaches indicate millimeter-scale deformation and a consistent pattern of settlement at the dam crest, supporting the interpretation of hydrologically driven structural response. The observed differences are primarily attributed to differences in spatial resolution and methodology between point-based FEM outputs and pixel-averaged satellite observations. The study demonstrates that combining satellite-based monitoring with numerical simulations provides a robust and cost-effective framework for dam safety assessment. This integrated approach supports improved interpretation of deformation behavior and offers practical value in extreme conditions, such as during flood events or climate-driven hydrological changes. Furthermore, continued advances in remote sensing and numerical modeling are expected to enhance the reliability of such approaches, making this methodology a transferable and sustainable solution for dam management worldwide.

Concrete gravity dams are among the most important elements of water infrastructure, ensuring reliable water supply, effective flood control, and renewable energy generation. Their safety, however, depends on understanding how they respond to both everyday operations and extreme events. This study focuses on the Koyna Dam, using a two-dimensional finite element model in ANSYS to explore its behaviour under different loading scenarios. The model includes dam–reservoir–foundation interaction and pays particular attention to seismic forces and hydrodynamic effects, which are often the most critical for long-term stability. The loading conditions were defined using guidelines from the United States Army Corps of Engineers (USACE), providing a standardized and realistic basis for evaluation. To refine accuracy, a mesh sensitivity analysis was performed to identify the most efficient element size. The results highlight clear patterns of displacement and stress within the dam, offering practical insights into how different loads influence its performance and resilience.

Detection of structural damages is significant step in structural health monitoring process, and should be done as accurately as possible. Assessing the condition of a dam is one of the crucial steps in dam conditions assessment in the traditional method of Dam Health Index known as Dam Finite Element Analysis (DFA). In this paper, the author examines the methods of improving and extending the evaluation and scenario analysis based on analytical techniques of Machine Learning (ML). In particular, this paper examines the damage identification in the numerical simulation of the displacement of the dam from finite element analysis (FEA) using the classification techniques of support vector machine and artificial neural networks. Sizeable numerical nonlinear FEM simulation was carried out using ANSYS software on elements of water height changes with respect to upstream load, wave load and uplift forces database creation. This baseline FEA data provides the basis for more efficient and effective use of ML approaches that can then derive displacement performance within minutes under different operational rules or changed climate conditions.The study shows that in the assessment of displacement performance of Koyna dam in India, the ANN model offers a better result than the SVM. This study shows that the application of ML technologies can be an indispensable addition that reduces the efforts, time, and calculations needed in comparison with pure FEA. The integration of ML techniques with FEA is shown to be a promising approach for supporting structural health monitoring in dam engineering.