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| − | ==1 Title, abstract and keywords<!-- Your document should start with a concise and informative title. Titles are often used in information-retrieval systems. Avoid abbreviations and formulae where possible. Capitalize the first word of the title. | + | ==Abstract== |
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| − | Provide a maximum of 6 keywords, and avoiding general and plural terms and multiple concepts (avoid, for example, 'and', 'of'). Be sparing with abbreviations: only abbreviations firmly established in the field should be used. These keywords will be used for indexing purposes.
| + | Many countries are implementing new dam safety regulations that often include more restrictive standards. This, together with the increasing average age of dams, results in a greater need for dam control and maintenance works. The advances in information and communications technologies improved the performance of dam monitoring systems, so a large amount of information on the dam behaviour can be collected. This has led to the use of more powerful tools for its analysis, many of which were first developed in the field of machine learning (e. g. neural networks). They offer some advantages over the conventional statistical methods. However, their capacity for early detection of anomalies has seldom been studied. As a result, they are far from being fully accepted by practitioners, whose analyses are often restricted to the interpretation of simple plots of time series data, together with basic statistical models. The present work describes a methodology for anomaly detection in dam behaviour, with the following features: a) The prediction model is based on boosted regression trees (BRTs). b) Causal and auto-regressive models are combined to detect different types of anomalies. c) It is checked whether the values of the external variables fall within the range of the training data. The performance of the proposed methodology was assessed through its application to a test case corresponding to an actual 100-m height arch dam, in operation since 1980. Artificial data were generated by means of a finite element model. Different anomalies were later added in order to test the anomaly detection capability. The method can be applied to other response variables and dam typologies, due to the great flexibility of BRTs, which automatically select the most relevant inputs. |
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| − | An abstract is required for every document; it should succinctly summarize the reason for the work, the main findings, and the conclusions of the study. Abstract is often presented separately from the article, so it must be able to stand alone. For this reason, references and hyperlinks should be avoided. If references are essential, then cite the author(s) and year(s). Also, non-standard or uncommon abbreviations should be avoided, but if essential they must be defined at their first mention in the abstract itself. -->==
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| − | ==2 The main text<!-- You can enter and format the text of this document by selecting the ‘Edit’ option in the menu at the top of this frame or next to the title of every section of the document. This will give access to the visual editor. Alternatively, you can edit the source of this document (Wiki markup format) by selecting the ‘Edit source’ option.
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| − | Most of the documents in Scipedia are written in English (write your manuscript in American or British English, but not a mixture of these). Anyhow, specific publications in other languages can be published in Scipedia. In any case, the documents published in other languages must have an abstract written in English.
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| − | 2.1 Subsections
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| − | Divide your article into clearly defined and numbered sections. Subsections should be numbered 1.1, 1.2, etc. and then 1.1.1, 1.1.2, ... Use this numbering also for internal cross-referencing: do not just refer to 'the text'. Any subsection may be given a brief heading. Capitalize the first word of the headings.
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| − | 2.2 General guidelines
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| − | Some general guidelines that should be followed in your manuscripts are:
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| − | * Avoid hyphenation at the end of a line.
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| − | * Symbols denoting vectors and matrices should be indicated in bold type. Scalar variable names should normally be expressed using italics.
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| − | * Use decimal points (not commas); use a space for thousands (10 000 and above).
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| − | * Follow internationally accepted rules and conventions. In particular use the international system of units (SI). If other quantities are mentioned, give their equivalent in SI.
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| − | 2.3 Tables, figures, lists and equations
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| − | Please insert tables as editable text and not as images. Tables should be placed next to the relevant text in the article. Number tables consecutively in accordance with their appearance in the text and place any table notes below the table body. Be sparing in the use of tables and ensure that the data presented in them do not duplicate results described elsewhere in the article.
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| − | Graphics may be inserted directly in the document and positioned as they should appear in the final manuscript.
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| − | Number the figures according to their sequence in the text. Ensure that each illustration has a caption. A caption should comprise a brief title. Keep text in the illustrations themselves to a minimum but explain all symbols and abbreviations used. Try to keep the resolution of the figures to a minimum of 300 dpi. If a finer resolution is required, the figure can be inserted as supplementary material
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| − | For tabular summations that do not deserve to be presented as a table, lists are often used. Lists may be either numbered or bulleted. Below you see examples of both.
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| − | 1. The first entry in this list
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| − | 2. The second entry
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| − | 2.1. A subentry
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| − | 3. The last entry
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| − | * A bulleted list item
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| − | * Another one
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| − | You may choose to number equations for easy referencing. In that case they must be numbered consecutively with Arabic numerals in parentheses on the right hand side of the page. Below is an example of formulae that should be referenced as eq. (1].
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| − | 2.4 Supplementary material
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| − | Supplementary material can be inserted to support and enhance your article. This includes video material, animation sequences, background datasets, computational models, sound clips and more. In order to ensure that your material is directly usable, please provide the files with a preferred maximum size of 50 MB. Please supply a concise and descriptive caption for each file. -->==
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| − | ==3 Bibliography<!--
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| − | Citations in text will follow a citation-sequence system (i.e. sources are numbered by order of reference so that the first reference cited in the document is [1], the second [2], and so on) with the number of the reference in square brackets. Once a source has been cited, the same number is used in all subsequent references. If the numbers are not in a continuous sequence, use commas (with no spaces) between numbers. If you have more than two numbers in a continuous sequence, use the first and last number of the sequence joined by a hyphen
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| − | You should ensure that all references are cited in the text and that the reference list. References should preferably refer to documents published in Scipedia. Unpublished results should not be included in the reference list, but can be mentioned in the text. The reference data must be updated once publication is ready. Complete bibliographic information for all cited references must be given following the standards in the field (IEEE and ISO 690 standards are recommended). If possible, a hyperlink to the referenced publication should be given. See examples for Scipedia’s articles [1], other publication articles [2], books [3], book chapter [4], conference proceedings [5], and online documents [6], shown in references section below. -->==
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| − | ==4 Acknowledgments<!-- Acknowledgments should be inserted at the end of the document, before the references section. -->==
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| − | ==5 References<!--[1] Author, A. and Author, B. (Year) Title of the article. Title of the Publication. Article code. Available: http://www.scipedia.com/ucode.
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| − | [2] Author, A. and Author, B. (Year) Title of the article. Title of the Publication. Volume number, first page-last page.
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| − | [3] Author, C. (Year). Title of work: Subtitle (edition.). Volume(s). Place of publication: Publisher.
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| − | [4] Author of Part, D. (Year). Title of chapter or part. In A. Editor & B. Editor (Eds.), Title: Subtitle of book (edition, inclusive page numbers). Place of publication: Publisher.
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| − | [5] Author, E. (Year, Month date). Title of the article. In A. Editor, B. Editor, and C. Editor. Title of published proceedings. Paper presented at title of conference, Volume number, first page-last page. Place of publication.
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| − | [6] Institution or author. Title of the document. Year. [Online] (Date consulted: day, month and year). Available: http://www.scipedia.com/document.pdf.
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Many countries are implementing new dam safety regulations that often include more restrictive standards. This, together with the increasing average age of dams, results in a greater need for dam control and maintenance works. The advances in information and communications technologies improved the performance of dam monitoring systems, so a large amount of information on the dam behaviour can be collected. This has led to the use of more powerful tools for its analysis, many of which were first developed in the field of machine learning (e. g. neural networks). They offer some advantages over the conventional statistical methods. However, their capacity for early detection of anomalies has seldom been studied. As a result, they are far from being fully accepted by practitioners, whose analyses are often restricted to the interpretation of simple plots of time series data, together with basic statistical models. The present work describes a methodology for anomaly detection in dam behaviour, with the following features: a) The prediction model is based on boosted regression trees (BRTs). b) Causal and auto-regressive models are combined to detect different types of anomalies. c) It is checked whether the values of the external variables fall within the range of the training data. The performance of the proposed methodology was assessed through its application to a test case corresponding to an actual 100-m height arch dam, in operation since 1980. Artificial data were generated by means of a finite element model. Different anomalies were later added in order to test the anomaly detection capability. The method can be applied to other response variables and dam typologies, due to the great flexibility of BRTs, which automatically select the most relevant inputs.
