19 citations as recorded by crossref.

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3. Deformation modelling of structures enriched by inter-element continuity condition based on multi-sensor data fusion M. Jafari https://doi.org/10.1016/j.apm.2016.06.010
4. Deformation prediction based on denoising techniques and ensemble learning algorithms for concrete dams M. Liu et al. https://doi.org/10.1016/j.eswa.2023.122022
5. The Use of SISO ARX Models in the Determination of the Damage to Dams E. Gülal et al. https://doi.org/10.1177/1056789510369333
6. Deformation Analysis Using Static, Kinematic and Dynamic Geodetic Deformation Models with GNSS: Deriner Dam, Artvin, Turkey B. Konakoglu https://doi.org/10.1007/s40799-020-00435-z
7. Verifying Pressure of Water on Dams, a Case Study T. Bayrak https://doi.org/10.3390/s8095376
8. Simultaneous estimation of dam displacements and reservoir level variation from GPS measurements R. Xi et al. https://doi.org/10.1016/j.measurement.2018.03.036
9. Analysis of Annual Deformation Characteristics of Xilongchi Dam Using Historical GPS Observations R. Xi et al. https://doi.org/10.3390/rs14164018
10. Improved hybrid wavelet neural network methodology for time-varying behavior prediction of engineering structures M. Cao et al. https://doi.org/10.1007/s00521-009-0240-8
11. Interpretable stacking-SHAP prediction model for dam deformation during long term operation M. Chen et al. https://doi.org/10.1016/j.ymssp.2025.113646
12. Multi-sensor approach to settlement analysis of earth dams A. Ardalan & M. Jafari https://doi.org/10.1007/s10596-011-9258-y
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14. Study on the Influence of Water Level on Earth Dam Reinforced by Cut-Off Wall: A Case Study in Wujing Reservoir D. Liu et al. https://doi.org/10.3390/w15010140
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17. The Prediction of Concrete Dam Displacement Using Copula-PSO-ANFIS Hybrid Model F. Tong et al. https://doi.org/10.1007/s13369-021-06100-w
18. Structural deformation prediction model based on extreme learning machine algorithm and particle swarm optimization S. Jiang et al. https://doi.org/10.1177/14759217211072237
19. Physically informed multivariate prediction of structural joint deformation in CFRDs: A fusion approach with interpretable decomposition and hybrid machine learning J. Qian et al. https://doi.org/10.1016/j.eswa.2025.130796