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Soft Computing and Machine Learning in Dam Engineering

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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: closed (21 October 2022) | Viewed by 34641

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Special Issue Editors

Dr. M. Amin Hariri-Ardebili

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Guest Editor

1. Department of Civil Engineering, University of Colorado, Boulder, CO 80309, USA
2. College of Computer, Mathematical and Natural Sciences, University of Maryland, College Park, MD 20742, USA
Interests: advanced analysis of infrastructures; earthquake engineering; scientific machine learning; coupled systems mechanics; uncertainty quantification and resilience
Special Issues, Collections and Topics in MDPI journals

Dr. Fernando Salazar

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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
Special Issues, Collections and Topics in MDPI journals

Prof. Farhad Pourkamali-Anaraki

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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

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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
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Dr. Juan Mata

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Guest Editor

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 Issues, Collections and Topics in MDPI journals

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 submissions that pass pre-check are 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 2600 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 (11 papers)

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Editorial

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8 pages, 1019 KiB

Open AccessEditorial

Soft Computing and Machine Learning in Dam Engineering

by Mohammad Amin Hariri-Ardebili, Fernando Salazar, Farhad Pourkamali-Anaraki, Guido Mazzà and Juan Mata

Water 2023, 15(5), 917; https://0-doi-org.brum.beds.ac.uk/10.3390/w15050917 - 27 Feb 2023

Cited by 3 | Viewed by 2071

Abstract

Dams have played a vital role in human civilization for thousands of years, providing vital resources such as water and electricity, and performing important functions such as flood control [...] Full article

(This article belongs to the Special Issue Soft Computing and Machine Learning in Dam Engineering)

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Research

Jump to: Editorial

21 pages, 1580 KiB

Open AccessArticle

Characterization of Relative Movements between Blocks Observed in a Concrete Dam and Definition of Thresholds for Novelty Identification Based on Machine Learning Models

by Juan Mata, Fabiana Miranda, António Antunes, Xavier Romão and João Pedro Santos

Water 2023, 15(2), 297; https://0-doi-org.brum.beds.ac.uk/10.3390/w15020297 - 11 Jan 2023

Cited by 4 | Viewed by 1447

Abstract

Dam surveillance activities are based on observing the structural behaviour and interpreting the past behaviour supported by the knowledge of the main loads. For day-to-day activities, data-driven models are usually adopted. Most applications consider regression models for the analysis of horizontal displacements recorded in pendulums. Traditional regression models are not commonly applied to the analysis of relative movements between blocks due to the non-linearities related to the simultaneity of hydrostatic and thermal effects. A new application of a multilayer perceptron neural network model is proposed to interpret the relative movements between blocks measured hourly in a concrete dam under exploitation. A new methodology is proposed for threshold definition related to novelty identification, taking into account the evolution of the records over time and the simultaneity of the structural responses measured in the dam under study. The results obtained through the case study showed the ability of the methodology presented in this work to characterize the relative movement between blocks and for the identification of novelties in the dam behaviour. Full article

(This article belongs to the Special Issue Soft Computing and Machine Learning in Dam Engineering)

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40 pages, 8014 KiB

Open AccessArticle

Risk-Informed Design of RCC Dams under Extreme Seismic Loading

by Keith A. Ferguson

Water 2023, 15(1), 116; https://doi.org/10.3390/w15010116 - 29 Dec 2022

Cited by 2 | Viewed by 2340

Abstract

The existing Scoggins Dam and reservoir are in Washington County, Oregon, and the title is held by the U.S. Bureau of Reclamation (Reclamation). Reclamation has previously identified dam safety concerns related to the existing embankment dam. Regional project sponsors, including Clean Water Services, have identified the need for expanded storage capacity in the reservoir to meet growing water demands and address water quality issues in the Tualatin River downstream of the dam. As part of efforts to resolve dam safety issues and increase the water storage in the reservoir, a comprehensive feasibility level design of a new 185-foot-high Roller Compacted Concrete (RCC) dam. Extraordinary seismic hazards have been identified in the region associated with the Cascadia Subduction Zone (CSZ). Further, any dam alternative carried forward for funding, final design, and construction will have to meet the Public Protection Guidelines (PPG) of Reclamation that require a formal quantitative risk analysis. A risk-informed design approach was adopted to configure the layout and cross-section properties of the dam. A multi-phase site characterization program and preliminary RCC mix design program were performed to support the design. In addition, models were developed, and an extensive suite of both (two-dimensional) 2D and (three-dimensional) 3D structural analyses were performed for seismic loadings with total durations of over 200 s, strong shaking of over 140 s, and peak ground accelerations of over 2 gravitational accelerations (g) (up to 50,000-year return period event). This paper describes the feasibility design configuration of the dam, including the seismic hazard characterization, structural analysis models, and seismic response modeling results. The expected performance of the dam relative to the risk-informed design criteria and Reclamation PPGs will be generally described. Full article

(This article belongs to the Special Issue Soft Computing and Machine Learning in Dam Engineering)

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24 pages, 4401 KiB

Open AccessFeature PaperArticle

An Automated Machine Learning Engine with Inverse Analysis for Seismic Design of Dams

by Mohammad Amin Hariri-Ardebili and Farhad Pourkamali-Anaraki

Water 2022, 14(23), 3898; https://0-doi-org.brum.beds.ac.uk/10.3390/w14233898 - 30 Nov 2022

Cited by 8 | Viewed by 1756

Abstract

This paper proposes a systematic approach for the seismic design of 2D concrete dams. As opposed to the traditional design method which does not optimize the dam cross-section, the proposed design engine offers the optimal one based on the predefined constraints. A large database of about 24,000 simulations is generated based on transient simulation of the dam-foundation-water system. The database includes over 150 various dam shapes, water levels, and material properties, as well as 160 different ground motion records. Automated machine learning (AutoML) is used to generate a surrogate model of dam response as a function of thirty variables. The accuracy of single- and multi-output surrogate models are compared, and the efficiency of the design engine for various settings is discussed. Next, a simple yet robust inverse analysis method is coupled with a multi-output surrogate model to design a hypothetical dam in the United States. Having the seismic hazard scenario, geological survey data, and also the concrete mix, the dam shape is estimated and compared to direct finite element simulation. The results show promising accuracy from the AutoML regression. Furthermore, the design shape from the inverse analysis is in good agreement with the design objectives and also the finite element simulations. Full article

(This article belongs to the Special Issue Soft Computing and Machine Learning in Dam Engineering)

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22 pages, 5918 KiB

Open AccessArticle

Seismic Safety Assessment of Arch Dams Using an ETA-Based Method with Control of Tensile and Compressive Damage

by André Alegre, Sérgio Oliveira, Paulo Mendes, Jorge Proença, Rafael Ramos and Ezequiel Carvalho

Water 2022, 14(23), 3835; https://0-doi-org.brum.beds.ac.uk/10.3390/w14233835 - 25 Nov 2022

Cited by 5 | Viewed by 2768

Abstract

The seismic safety assessment of large concrete dams remains a significant challenge in dam engineering, as it requires appropriate analysis methods, modern performance criteria, and advanced numerical models to simulate the dam seismic behavior. This paper presents a method for seismic safety assessment of arch dams based on Endurance Time Analysis (ETA), using tensile and compressive damage results from a robust formulation for seismic analysis considering joint opening/sliding and concrete non-linear behavior (finite element program DamDySSA, under development in LNEC). The seismic performance is evaluated by controlling the evolution of the damage state of the dam, according to predefined performance criteria, to estimate acceleration endurance limits for tensile and compressive damage. These acceleration limits are compared, respectively, with the peak ground accelerations prescribed for the Operating Basis Earthquake (OBE) and Safety Evaluation Earthquake (SEE), aiming to evaluate the dam seismic performance relative to both earthquake levels efficiently, using a single intensifying acceleration time history. The ETA-based method is applied to the cases of Cabril Dam (132 m-high) and Cahora Bassa Dam (170 m-high), confirming its usefulness for future seismic safety studies, while the potential of DamDySSA for non-linear seismic analysis of arch dams is highlighted. Full article

(This article belongs to the Special Issue Soft Computing and Machine Learning in Dam Engineering)

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24 pages, 3324 KiB

Open AccessArticle

Estimating the Ice Loads on Concrete Dams Based on Their Structural Response

by Rikard Hellgren, Jonas Enzell, Anders Ansell, Erik Nordström and Richard Malm

Water 2022, 14(4), 597; https://0-doi-org.brum.beds.ac.uk/10.3390/w14040597 - 16 Feb 2022

Cited by 3 | Viewed by 2512

Abstract

In the assessment of concrete dams in cold climate, it is common that the theoretical stability becomes insufficient for load cases that include ice loads. However, the magnitude and return period of these ice loads have a high degree of uncertainty. This study estimates the magnitude of ice loads on eight concrete dam monoliths using measurements of their displacement from 29 winters. In the displacement signals, events are identified and assumed to be caused solely by ice loads. The observed displacement during an event is interpreted as an ice load using a load–displacement relationship derived from FE simulations of each dam. These simulations show that ice loads of the magnitudes given in design guidelines and recorded in previous measurements would significantly affect the structural response of the studied dams. However, only small traces of ice loads can be found in the observed responses of the studied dams. The estimated ice loads are significantly lower than the ice loads recorded in traditional ice load measurements. These results indicate that the average magnitude of ice load on an entire monolith is significantly lower than the measured local pressures. This would imply that ice loads may be a smaller concern regarding dam safety than previously believed. Full article

(This article belongs to the Special Issue Soft Computing and Machine Learning in Dam Engineering)

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27 pages, 4479 KiB

Open AccessArticle

Validation of Machine Learning Models for Structural Dam Behaviour Interpretation and Prediction

by Juan Mata, Fernando Salazar, José Barateiro and António Antunes

Water 2021, 13(19), 2717; https://0-doi-org.brum.beds.ac.uk/10.3390/w13192717 - 01 Oct 2021

Cited by 16 | Viewed by 3365

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 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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22 pages, 1801 KiB

Open AccessArticle

Anomaly Detection in Dam Behaviour with Machine Learning Classification Models

by Fernando Salazar, André Conde, Joaquín Irazábal and David J. Vicente

Water 2021, 13(17), 2387; https://0-doi-org.brum.beds.ac.uk/10.3390/w13172387 - 30 Aug 2021

Cited by 14 | Viewed by 3468

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 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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18 pages, 1852 KiB

Open AccessArticle

Deep Neural Network and Polynomial Chaos Expansion-Based Surrogate Models for Sensitivity and Uncertainty Propagation: An Application to a Rockfill Dam

by Gullnaz Shahzadi and Azzeddine Soulaïmani

Water 2021, 13(13), 1830; https://0-doi-org.brum.beds.ac.uk/10.3390/w13131830 - 30 Jun 2021

Cited by 15 | Viewed by 3726

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 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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19 pages, 11429 KiB

Open AccessArticle

Accounting for Uncertainties in the Safety Assessment of Concrete Gravity Dams: A Probabilistic Approach with Sample Optimization

by Rocio L. Segura, Benjamin Miquel, Patrick Paultre and Jamie E. Padgett

Water 2021, 13(6), 855; https://0-doi-org.brum.beds.ac.uk/10.3390/w13060855 - 20 Mar 2021

Cited by 9 | Viewed by 3691

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 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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21 pages, 4882 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

An RF-PCE Hybrid Surrogate Model for Sensitivity Analysis of Dams

by Mohammad Amin Hariri-Ardebili, Golsa Mahdavi, Azam Abdollahi and Ali Amini

Water 2021, 13(3), 302; https://0-doi-org.brum.beds.ac.uk/10.3390/w13030302 - 26 Jan 2021

Cited by 31 | Viewed by 3791

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 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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