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estimation of groundwater seepage risks into tunnel using radial basis function networks
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نویسنده
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farhadian h ,eslaminezhad a
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منبع
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علوم و مهندسي آبياري - 2022 - دوره : 45 - شماره : 2 - صفحه:109 -124
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چکیده
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In this study, site groundwater rating (sgr) in the amirkabir tunnel has been estimated using radial basis function networks (rbfns). sgr is the first rating method that by considering the parameters like joint frequency, joint aperture, schistosity, crashed zones, karstification, soil permeability coefficient, tunnel location in the water table or piezometric surface, and the amount and intensity of annual raining in the area, classifies the tunnel path from the risk of groundwater seepage point of view. in this article, using an rbfn, an estimation of sgr along the amirkabir tunnel path was performed. field data obtained from primary studies in the tunnel was used to train and test the prepared network. for the testing set, modeling results showed that sgr could be predicted with the mean error of 3.57% and 4.76% using radial basis network and exact radial basis network functions, respectively. a high correlation between the sgr of the tunnel path and the network answers, confirmed the prepared rbfn.
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کلیدواژه
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sgr ,radial basis function networks ,groundwater seepage ,tunnel
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آدرس
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university of birjand, faculty of engineering, department of mining engineering, iran, university of tehran, college of engineering, department of surveying and geomatics engineering, iran
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پست الکترونیکی
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ahmad.eslami73@ut.ac.ir
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estimation of groundwater seepage risks into tunnel using radial basis function networks
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Authors
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Farhadian H ,Eslaminezhad A
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Abstract
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in this study, site groundwater rating (sgr) in the amirkabir tunnel has been estimated using radial basis function networks (rbfns). sgr is the first rating method that by considering the parameters like joint frequency, joint aperture, schistosity, crashed zones, karstification, soil permeability coefficient, tunnel location in the water table or piezometric surface, and the amount and intensity of annual raining in the area, classifies the tunnel path from the risk of groundwater seepage point of view. in this article, using an rbfn, an estimation of sgr along the amirkabir tunnel path was performed. field data obtained from primary studies in the tunnel was used to train and test the prepared network. for the testing set, modeling results showed that sgr could be predicted with the mean error of 3.57% and 4.76% using radial basis network and exact radial basis network functions, respectively. a high correlation between the sgr of the tunnel path and the network answers, confirmed the prepared rbfn.
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Keywords
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sgr ,radial basis function networks ,groundwater seepage ,tunnel
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