Soft Computing and Machine Learning in Dam Engineering
1. Introduction and Overview
2. Soft Computing in Dam Engineering
3. About the Special Issue
4. Contributions to Current Special Issue
5. Future Research Directions
- The validation of manual measurements in real-time, allowing manual record errors to be immediately identified (resulting from human error or defects in measuring devices). This enables the technicians, in-situ and in real-time, to have the opportunity to repeat the measurement before making the final record.
- The validation of the automated measurements from ADAS taking advantage of the multi-dimensionality of the measurements carried out.
- The definition/confirmation of the best location of the measurement devices that better identify potential failure scenarios, enhancing the definition of subsystems of devices that allow the confirmation of scenarios.
- The construction of advanced predictive models of physical quantities that present a nonlinear behavior, such as seepage and leakage, uplift pressures, and joint movements, among others.
- Multivariate and simultaneous analysis of quasi-static and dynamic quantities for interpreting observed behavior.
- The short-term prediction of the structural behavior under extreme flood scenarios, taking into account the short-term evolution of water level.
- The development of key operational indicators to assess the performance of the observed structural behavior. For example, to assess the efficiency of the waterproofing concrete curtain.
- The development of dashboards that allow, in an easy way for the end user, to assess the quality of the forecast models adopted (including the quantification of the effect of each of the main actions in the final response through sensitivity analyses).
- The identification of potential failure scenarios based on monitoring data.
- Validation, verification, and uncertainty quantification of probabilistic numerical simulations that are used in soft computing models.
Conflicts of Interest
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Hariri-Ardebili, M.A.; Salazar, F.; Pourkamali-Anaraki, F.; Mazzà, G.; Mata, J. Soft Computing and Machine Learning in Dam Engineering. Water 2023, 15, 917. https://doi.org/10.3390/w15050917
Hariri-Ardebili MA, Salazar F, Pourkamali-Anaraki F, Mazzà G, Mata J. Soft Computing and Machine Learning in Dam Engineering. Water. 2023; 15(5):917. https://doi.org/10.3390/w15050917Chicago/Turabian Style
Hariri-Ardebili, Mohammad Amin, Fernando Salazar, Farhad Pourkamali-Anaraki, Guido Mazzà, and Juan Mata. 2023. "Soft Computing and Machine Learning in Dam Engineering" Water 15, no. 5: 917. https://doi.org/10.3390/w15050917