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The paper also outlines soft computing as a set of human-like methods such as fuzzy logic and neural networks for difficult engineering problems.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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Hariri-Ardebili 1,2, *, Fernando Salazar 3, Farhad Pourkamali-Anaraki 4, Guido Mazzà 5 and Juan Mata 6  \n1 Department of Civil Environmental and Architectural Engineering, University of Colorado Boulder, Boulder, CO 80309, USA  \n2 College of Computer, Mathematical and Natural Sciences, University of Maryland, College Park, MD 20742, USA  \n3 International Centre For Numerical Methods In Engineering (CIMNE), P. Gral. Martinez Campos, 41, 28010 Madrid, Spain  \n4 Department of Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80204, USA  \n5 Italian National Committee on Large Dams, 2300198 Roma, Italy  \n6 National Laboratory for Civil Engineering (LNEC), Avenida do Brasil, 101, 1700-066 Lisbon, Portugal  \n* [Correspondence: mohammad.haririardebili@colorado.edu](Correspondence: mohammad.haririardebili@colorado.edu); Tel.: +1-303-990-2451  \nCitation: 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](https://doi.org/10.3390/w15050917)  \nReceived: 31 January 2023  \nAccepted: 7 February 2023  \nPublished: 27 February 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1. Introduction and Overview  \nDams 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. The scale and complexity of dam projects have increased in recent years, making their safety evaluation even more challenging. Therefore, it is crucial that dam engineers consider all potential risks and take appropriate measures to ensure the safety and stability of these structures [1] . The nature and existence of dams are highly coupled with concepts such as population growth, climate change, global warming, and water security [2] . According to the International Commission on Large Dams's (ICOLD) [3] most recent update in April 2020, there are about 58,700 registered large dams in the world. Figure 1 illustrates the global distribution of these large dams.  \nFigure 1. Global distribution of large dams as of 2020 .  \nTraditional dam safety methods, based on visual inspections and manual monitoring, have long been the standard for ensuring the stability and safety of dams. However, asthe scale and complexity of dam projects have increased, these methods have become increasingly insufficient. Major limitations of traditional dam safety methods are the existence of deficient observation plans and the potential for human error. Inspectors may miss crucial signs of deterioration or failure, and manual monitoring can be prone to  \nWater 2023, 15, 917. [https://doi.org/10.3390/w15050917](https://doi.org/10.3390/w15050917) [https://www.mdpi.com/journal/water](https://www.mdpi.com/journal/water)  \ninaccuracies. In addition, as the number of (aged and new) dams continues to increase, it becomes increasingly difficult and resource-intensive to manually inspect and monitor each one. Another limitation of traditional dam safety methods is that they are typically reactive rather than proactive. They focus on identifying and addressing problems after they have already occurred, rather than predicting and preventing them.  \nIn contrast, modern techniques such as remote sensing, drones, and sensor networks can provide more accurate, real-time data on dam conditions. They can also be used to continuously monitor dams, providing an early warning of potential problems. Artificial Intelligence (AI) can be ","cbCailZye06zieBN","https://ap.wps.com/l/cbCailZye06zieBN","pdf",1043128,"English","# 1. Introduction and Overview\n# 2. Soft Computing in Dam Engineering","[{\"question\":\"Why are traditional dam safety methods becoming insufficient?\",\"answer\":\"They rely on visual inspections and manual monitoring, which suffer from deficient observation plans, potential human error, resource intensity, and a reactive focus after problems occur.\"},{\"question\":\"What modern monitoring approaches improve dam safety assessment?\",\"answer\":\"Remote sensing, drones, and sensor networks provide more accurate, real-time data and enable continuous monitoring for earlier detection of potential issues.\"},{\"question\":\"How does AI contribute to dam engineering safety decisions?\",\"answer\":\"AI analyzes collected data to identify patterns and anomalies, supporting accurate and updated risk analysis within decision-making processes.\"}]","Soft Computing and Machine Learning in Dam Engineering - Editorial | PDF"]