In pipeline management the accurate prediction of weak displacements is a crucial factor in drawing up a prevention policy since the accumulation of these displacements over a period of several years can lead to situations of high risk. This work addresses the specific problem related to the prediction of displacements induced by rainfall in unstable areas, of known geology, and crossed by underground pipelines. A neural model has been configured which learns of displacements from instrumented sites (where inclinometric measurements are available) and is able to generalise to other sites not equipped with inclinometers.
|Data di pubblicazione:||2004|
|Titolo:||Prediction of Displacements in Unstable Areas Using a Neural Model|
|Digital Object Identifier (DOI):||10.1023/B:NHAZ.0000026796.59079.1a|
|Codice identificativo ISI:||WOS:000221247800007|
|Codice identificativo Scopus:||2-s2.0-2942608844|
|Parole Chiave:||unstable areas; lifeline; multilayer perceptron neural network; prediction; multisource data analysis; rainfall|
|Appare nelle tipologie:||Articolo su Rivista|