Artificial neural network model for ground vibration amplitudes prediction due to light railway traffic in urban areas
The growth of density and circulation speed of railway transportation systems in urban areas increases the importance of the research issues of the produced environmental impacts. This study presents a field data analysis, obtained during monitoring campaigns of ground vibration, due to light railwa...
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| Veröffentlicht in: | Neural computing & applications 2018-06, Vol.29 (11), p.1045-1057 |
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| creator | Paneiro, G. Durão, F. O. Costa e Silva, M. Falcão Neves, P. |
| description | The growth of density and circulation speed of railway transportation systems in urban areas increases the importance of the research issues of the produced environmental impacts. This study presents a field data analysis, obtained during monitoring campaigns of ground vibration, due to light railway traffic in urban areas, based on the artificial neural network (ANN) approach, using quantitative and qualitative predictors. Different ANN-based models, using those predictors, were evaluated/trained and validated. Using several criteria, including those that measures the possibility of ANN overfitting (RR
2
) and complexity (AIC), the best ANN model was successfully obtained for Lisbon area. This model, with 16 input elements (quantitative and qualitative predictors), 2 neurons on the hidden layer with a hyperbolic tangent sigmoid transfer function, and 1 neuron on the output layer considering a linear transfer function, has 0.9720 for the coefficient of determination and 0.5293 for the sum squared error. |
| doi_str_mv | 10.1007/s00521-016-2625-9 |
| format | Article |
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2
) and complexity (AIC), the best ANN model was successfully obtained for Lisbon area. This model, with 16 input elements (quantitative and qualitative predictors), 2 neurons on the hidden layer with a hyperbolic tangent sigmoid transfer function, and 1 neuron on the output layer considering a linear transfer function, has 0.9720 for the coefficient of determination and 0.5293 for the sum squared error.</description><identifier>ISSN: 0941-0643</identifier><identifier>EISSN: 1433-3058</identifier><identifier>DOI: 10.1007/s00521-016-2625-9</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Artificial Intelligence ; Artificial neural networks ; Computational Biology/Bioinformatics ; Computational Science and Engineering ; Computer Science ; Data analysis ; Data Mining and Knowledge Discovery ; Environmental impact ; Ground motion ; Hyperbolic functions ; Image Processing and Computer Vision ; Neural networks ; Original Article ; Probability and Statistics in Computer Science ; Rail transportation ; Traffic models ; Traffic speed ; Transfer functions ; Transportation systems ; Urban areas ; Vibration analysis ; Vibration monitoring</subject><ispartof>Neural computing & applications, 2018-06, Vol.29 (11), p.1045-1057</ispartof><rights>The Natural Computing Applications Forum 2016</rights><rights>Copyright Springer Science & Business Media 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c355t-586ccdd52c0bdccc6391173dc66537e0e0fecd63e8bcfcef204ce8ccbbc347673</citedby><cites>FETCH-LOGICAL-c355t-586ccdd52c0bdccc6391173dc66537e0e0fecd63e8bcfcef204ce8ccbbc347673</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00521-016-2625-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00521-016-2625-9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Paneiro, G.</creatorcontrib><creatorcontrib>Durão, F. O.</creatorcontrib><creatorcontrib>Costa e Silva, M.</creatorcontrib><creatorcontrib>Falcão Neves, P.</creatorcontrib><title>Artificial neural network model for ground vibration amplitudes prediction due to light railway traffic in urban areas</title><title>Neural computing & applications</title><addtitle>Neural Comput & Applic</addtitle><description>The growth of density and circulation speed of railway transportation systems in urban areas increases the importance of the research issues of the produced environmental impacts. This study presents a field data analysis, obtained during monitoring campaigns of ground vibration, due to light railway traffic in urban areas, based on the artificial neural network (ANN) approach, using quantitative and qualitative predictors. Different ANN-based models, using those predictors, were evaluated/trained and validated. Using several criteria, including those that measures the possibility of ANN overfitting (RR
2
) and complexity (AIC), the best ANN model was successfully obtained for Lisbon area. 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O. ; Costa e Silva, M. ; Falcão Neves, P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c355t-586ccdd52c0bdccc6391173dc66537e0e0fecd63e8bcfcef204ce8ccbbc347673</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Artificial Intelligence</topic><topic>Artificial neural networks</topic><topic>Computational Biology/Bioinformatics</topic><topic>Computational Science and Engineering</topic><topic>Computer Science</topic><topic>Data analysis</topic><topic>Data Mining and Knowledge Discovery</topic><topic>Environmental impact</topic><topic>Ground motion</topic><topic>Hyperbolic functions</topic><topic>Image Processing and Computer Vision</topic><topic>Neural networks</topic><topic>Original Article</topic><topic>Probability and Statistics in Computer Science</topic><topic>Rail transportation</topic><topic>Traffic models</topic><topic>Traffic speed</topic><topic>Transfer functions</topic><topic>Transportation systems</topic><topic>Urban areas</topic><topic>Vibration analysis</topic><topic>Vibration monitoring</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Paneiro, G.</creatorcontrib><creatorcontrib>Durão, F. 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2
) and complexity (AIC), the best ANN model was successfully obtained for Lisbon area. This model, with 16 input elements (quantitative and qualitative predictors), 2 neurons on the hidden layer with a hyperbolic tangent sigmoid transfer function, and 1 neuron on the output layer considering a linear transfer function, has 0.9720 for the coefficient of determination and 0.5293 for the sum squared error.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00521-016-2625-9</doi><tpages>13</tpages></addata></record> |
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| subjects | Artificial Intelligence Artificial neural networks Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data analysis Data Mining and Knowledge Discovery Environmental impact Ground motion Hyperbolic functions Image Processing and Computer Vision Neural networks Original Article Probability and Statistics in Computer Science Rail transportation Traffic models Traffic speed Transfer functions Transportation systems Urban areas Vibration analysis Vibration monitoring |
| title | Artificial neural network model for ground vibration amplitudes prediction due to light railway traffic in urban areas |
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