Special Issue "Soft Computing and Machine Learning in Dam Engineering"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: 21 October 2022.

Special Issue Editors

Dr. M. Amin Hariri-Ardebili
E-Mail Website
Guest Editor
1. College of Computer, Mathematical and Natural Sciences, University of Maryland, College Park, MD 20742, USA
2. Department of Civil Engineering, University of Colorado, Boulder, CO 80309, USA
3. X-Elastica LLC, Boulder, CO, USA
Interests: advanced analysis of infrastructures; earthquake engineering; machine learning; coupled systems mechanics; uncertainty quantification
Special Issues, Collections and Topics in MDPI journals
Dr. Fernando Salazar
E-Mail Website
Guest Editor
Project Development Director, International Centre For Numerical Methods In Engineering (CIMNE), P. Gral. Martínez Campos, 41, 28010 Madrid, Spain
Interests: machine learning in civil engineering problems; dam safety; dam hydraulics; structural health monitoring; uncertainty quantification
Prof. Farhad Pourkamali-Anaraki
E-Mail Website
Guest Editor
Assistant Professor of Computer Science, University of Massachusetts Lowell, MA, USA
Interests: machine learning for scientific computing; uncertainty quantification; large-scale data analysis; computational mathematics; mathematical optimization
Mr. Guido Mazzà
E-Mail Website
Guest Editor
Chairman of ICOLD Technical Committee “Computational Aspects of Dam Analysis and Design”; Vice President of the Italian National Committee on Large Dams, Italy
Interests: safety and risk assessment of dams and appurtenant structures
Special Issues, Collections and Topics in MDPI journals
Dr. Juan Mata
E-Mail Website
Guest Editor
Assistant Researcher, National Laboratory for Civil (LNEC); Avenida do Brasil, 101, 1700-066 Lisbon, Portugal
Interests: dam engineering; dam surveillance; structural health monitoring; machine learning; management information systems; decision making

Special Issue Information

Dear Colleagues,

Dams are critical infrastructures, and their operation is governed by many engineering, social, economic, and (and sometimes) political factors. The role of dam engineers has dramatically changed over the last three decades (mainly from the design of new dams to safety analysis of the existing ones). Thanks to new technologies, a considerable amount of data is collected from field monitoring or can be generated from computer simulations. Now the question is, “how can we effectively utilize all the available data?” A promising solution relies on integrating “Dam Engineering” and “Computer Science”. There are several innovative solutions at a high level of maturity that have great potential for application in dam engineering, including machine learning and soft computing.

In this Special Issue, we solicit high-quality original research articles focused on state-of-the-art techniques and methods employed in the design and analysis of dams. We welcome both theoretical and application papers of high technical standards across various disciplines, thus facilitating an awareness of techniques and methods in one area that may apply to other areas. We seek high-quality submissions of original research articles as well as review articles on all aspects related to artificial intelligence in dam engineering and appurtenant structures that have the potential for practical application.

Topics of interest include but are not limited to:

  • Soft computing and machine learning for dam response prediction;
  • Application of machine learning on dam monitoring data;
  • Big data analytics from numerical simulations;
  • Data processing from structural, material, and hydrologic aspects;
  • Advanced design techniques with optimized geometry;
  • Case studies with open-source data to be used in machine learning;
  • Advanced mathematical models and machine learning techniques;
  • Dam safety, risk-informed decision making, and failure mode analysis;
  • Multihazard (e.g., earthquake, flood, aging) prediction models;
  • Validations and verifications of response prediction models;
  • Advances in sustainable and resilient dams, as well as socioeconomic aspects;
  • Investigating the robustness of machine learning methods.

Dr. M. Amin Hariri-Ardebili
Prof. Fernando Salazar
Prof. Farhad Pourkamali-Anaraki
Eng. Guido Mazza
Dr. Juan Mata
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • dams
  • machine learning
  • soft computing
  • natural hazard
  • monitoring data
  • numerical simulations
  • safety
  • risk
  • resilience
  • validation
  • uncertainty quantification

Published Papers (5 papers)

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Research

Article
Validation of Machine Learning Models for Structural Dam Behaviour Interpretation and Prediction
Water 2021, 13(19), 2717; https://doi.org/10.3390/w13192717 - 01 Oct 2021
Viewed by 510
Abstract
The main aim of structural safety control is the multiple assessments of the expected dam behaviour based on models and the measurements and parameters that characterise the dam’s response and condition. In recent years, there is an increase in the use of data-based [...] Read more.
The main aim of structural safety control is the multiple assessments of the expected dam behaviour based on models and the measurements and parameters that characterise the dam’s response and condition. In recent years, there is an increase in the use of data-based models for the analysis and interpretation of the structural behaviour of dams. Multiple Linear Regression is the conventional, widely used approach in dam engineering, although interesting results have been published based on machine learning algorithms such as artificial neural networks, support vector machines, random forest, and boosted regression trees. However, these models need to be carefully developed and properly assessed before their application in practice. This is even more relevant when an increase in users of machine learning models is expected. For this reason, this paper presents extensive work regarding the verification and validation of data-based models for the analysis and interpretation of observed dam’s behaviour. This is presented by means of the development of several machine learning models to interpret horizontal displacements in an arch dam in operation. Several validation techniques are applied, including historical data validation, sensitivity analysis, and predictive validation. The results are discussed and conclusions are drawn regarding the practical application of data-based models. Full article
(This article belongs to the Special Issue Soft Computing and Machine Learning in Dam Engineering)
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Article
Anomaly Detection in Dam Behaviour with Machine Learning Classification Models
Water 2021, 13(17), 2387; https://doi.org/10.3390/w13172387 - 30 Aug 2021
Viewed by 656
Abstract
Dam safety assessment is typically made by comparison between the outcome of some predictive model and measured monitoring data. This is done separately for each response variable, and the results are later interpreted before decision making. In this work, three approaches based on [...] Read more.
Dam safety assessment is typically made by comparison between the outcome of some predictive model and measured monitoring data. This is done separately for each response variable, and the results are later interpreted before decision making. In this work, three approaches based on machine learning classifiers are evaluated for the joint analysis of a set of monitoring variables: multi-class, two-class and one-class classification. Support vector machines are applied to all prediction tasks, and random forest is also used for multi-class and two-class. The results show high accuracy for multi-class classification, although the approach has limitations for practical use. The performance in two-class classification is strongly dependent on the features of the anomalies to detect and their similarity to those used for model fitting. The one-class classification model based on support vector machines showed high prediction accuracy, while avoiding the need for correctly selecting and modelling the potential anomalies. A criterion for anomaly detection based on model predictions is defined, which results in a decrease in the misclassification rate. The possibilities and limitations of all three approaches for practical use are discussed. Full article
(This article belongs to the Special Issue Soft Computing and Machine Learning in Dam Engineering)
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Article
Deep Neural Network and Polynomial Chaos Expansion-Based Surrogate Models for Sensitivity and Uncertainty Propagation: An Application to a Rockfill Dam
Water 2021, 13(13), 1830; https://doi.org/10.3390/w13131830 - 30 Jun 2021
Viewed by 964
Abstract
Computational modeling plays a significant role in the design of rockfill dams. Various constitutive soil parameters are used to design such models, which often involve high uncertainties due to the complex structure of rockfill dams comprising various zones of different soil parameters. This [...] Read more.
Computational modeling plays a significant role in the design of rockfill dams. Various constitutive soil parameters are used to design such models, which often involve high uncertainties due to the complex structure of rockfill dams comprising various zones of different soil parameters. This study performs an uncertainty analysis and a global sensitivity analysis to assess the effect of constitutive soil parameters on the behavior of a rockfill dam. A Finite Element code (Plaxis) is utilized for the structure analysis. A database of the computed displacements at inclinometers installed in the dam is generated and compared to in situ measurements. Surrogate models are significant tools for approximating the relationship between input soil parameters and displacements and thereby reducing the computational costs of parametric studies. Polynomial chaos expansion and deep neural networks are used to build surrogate models to compute the Sobol indices required to identify the impact of soil parameters on dam behavior. Full article
(This article belongs to the Special Issue Soft Computing and Machine Learning in Dam Engineering)
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Article
Accounting for Uncertainties in the Safety Assessment of Concrete Gravity Dams: A Probabilistic Approach with Sample Optimization
Water 2021, 13(6), 855; https://doi.org/10.3390/w13060855 - 20 Mar 2021
Cited by 1 | Viewed by 831
Abstract
Important advances have been made in the methodologies for assessing the safety of dams, resulting in the review and modification of design guidelines. Many existing dams fail to meet these revised criteria, and structural rehabilitation to achieve the updated standards may be costly [...] Read more.
Important advances have been made in the methodologies for assessing the safety of dams, resulting in the review and modification of design guidelines. Many existing dams fail to meet these revised criteria, and structural rehabilitation to achieve the updated standards may be costly and difficult. To this end, probabilistic methods have emerged as a promising alternative and constitute the basis of more adequate procedures of design and assessment. However, such methods, in addition to being computationally expensive, can produce very different solutions, depending on the input parameters, which can greatly influence the final results. Addressing the existing challenges of these procedures to analyze the stability of concrete dams, this study proposes a probabilistic-based methodology for assessing the safety of dams under usual, unusual, and extreme loading conditions. The proposed procedure allows the analysis to be updated while avoiding unnecessary simulation runs by classifying the load cases according to the annual probability of exceedance and by using an efficient progressive sampling strategy. In addition, a variance-based global sensitivity analysis is performed to identify the parameters most affecting the dam stability, and the parameter ranges that meet the safety guidelines are formulated. It is observed that the proposed methodology is more robust, more computationally efficient, and more easily interpretable than conventional methods. Full article
(This article belongs to the Special Issue Soft Computing and Machine Learning in Dam Engineering)
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Article
An RF-PCE Hybrid Surrogate Model for Sensitivity Analysis of Dams
Water 2021, 13(3), 302; https://doi.org/10.3390/w13030302 - 26 Jan 2021
Cited by 6 | Viewed by 961
Abstract
Quantification of structural vibration characteristics is an essential task prior to perform any dynamic health monitoring and system identification. Anatomy of vibration in concrete arch dams (especially tall dams with un-symmetry shape) is very complicated and requires special techniques to solve the eigenvalue [...] Read more.
Quantification of structural vibration characteristics is an essential task prior to perform any dynamic health monitoring and system identification. Anatomy of vibration in concrete arch dams (especially tall dams with un-symmetry shape) is very complicated and requires special techniques to solve the eigenvalue problem. The situation becomes even more complicated if the material distribution is assumed to be heterogeneous within the dam body (as opposed to conventional isotropic homogeneous relationship). This paper proposes a hybrid Random Field (RF)–Polynomial Chaos Expansion (PCE) surrogate model for uncertainty quantification and sensitivity assessment of dams. For different vibration modes, the most sensitive spatial locations within dam body are identified using both Sobol’s indices and correlation rank methods. Results of the proposed hybrid model is further validated using the classical random forest regression method. The outcome of this study can improve the results of system identification and dynamic analysis by properly determining the vibration characteristics. Full article
(This article belongs to the Special Issue Soft Computing and Machine Learning in Dam Engineering)
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