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Title:New computational models for better predictions of the soil-compression index
Authors:ID Demir, Ahmet (Author)
ID Fakulteta za gradbeništvo, prometno inženirstvo in arhitekturo Univerze v Mariboru (Copyright holder)
Files:.pdf Acta_geotechnica_Slovenica_2015_Demir_New_computational_models_for_better_predictions_of_the_soil-compression_index.pdf (274,65 KB)
MD5: 534ACE34C47905C41DDA0A284A76910F
PID: 20.500.12556/dkum/bee92470-49a3-4e46-a841-171e8b5372f2
 
URL http://fgserver3.fg.um.si/journal-ags/2015-1/article-6.asp
 
Language:English
Work type:Scientific work
Typology:1.01 - Original Scientific Article
Organization:FGPA - Faculty of Civil Engineering, Transportation Engineering and Architecture
Abstract:The compression index is one of the important soil parameters that are essential for geotechnical designs. Because laboratory and in-situ tests for determining the compression index (Cc) value are laborious, time consuming and costly, empirical formulas based on soil parameters are commonly used. Over the years a number of empirical formulas have been proposed to relate the compressibility to other soil parameters, such as the natural water content, the liquid limit, the plasticity index, the specific gravity. These empirical formulas provide good results for a specific test set, but cannot accurately or reliably predict the compression index from various test sets. The other disadvantage is that they tend to use a single parameter to estimate the compression index (Cc), even though Cc exhibits spatial characteristics depending on several soil parameters. This study presents the potential for Genetic Expression Programming (GEP) and the Adaptive Neuro-Fuzzy (ANFIS) computing paradigm to predict the compression index from soil parameters such as the natural water content, the liquid limit, the plastic index, the specific gravity and the void ratio. A total of 299 data sets collected from the literature were used to develop the models. The performance of the models was comprehensively evaluated using several statistical verification tools. The predicted results showed that the GEP and ANFIS models provided fairly promising approaches to the prediction of the compression index of soils and could provide a better performance than the empirical formulas.
Keywords:compression index, statistical analysis, genetic expression programming, adaptive neuro-fuzzy, empirical equations
Publication status:Published
Publication version:Version of Record
Year of publishing:2015
Number of pages:str. 59-69
Numbering:Letn. 12, št. 1
PID:20.500.12556/DKUM-70832 New window
ISSN:1854-0171
UDC:624.13
ISSN on article:1854-0171
COBISS.SI-ID:284359168 New window
NUK URN:URN:SI:UM:DK:EOAM828I
Copyright:Fakulteta za gradbeništvo, prometno inženirstvo in arhitekturo Univerze v Mariboru
Publication date in DKUM:14.06.2018
Views:1369
Downloads:80
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Record is a part of a journal

Title:Acta geotechnica Slovenica
Shortened title:Acta geotech. Slov.
Publisher:Fakulteta za gradbeništvo, prometno inženirstvo in arhitekturo Univerze v Mariboru
ISSN:1854-0171
COBISS.SI-ID:215987712 New window

Secondary language

Language:Slovenian
Title:Novi računski model za boljšo napoved kompresijskega indeksa zemljine
Abstract:Kompresijski indeks je eden od pomembnejših parametrov zemljine, ki je bistven za geotehnično projektiranje. Ker so laboratorijski in terenski preizkusi za določitev vrednosti kompresijskega indeksa (Cc) težavni, dolgotrajni in dragi, se za ta namen pogosto uporabljajo empirične enačbe na osnovi parametrov zemljin. V preteklih letih so bile predlagane številne empirične enačbe, ki podajajo relacijo med stisljivostjo in drugimi parametri zemljine, kot so naravna vlažnost, meja židkosti, indeks plastičnosti in specifična gravitacija. Te empirične enačbe zagotavljajo dobre rezultate za posamezne preizkusne nize, vendar ne morejo natančno ali zanesljivo napovedati vrednosti kompresijskega indeksa iz različnih preizkusnih nizov. Druga pomanjkljivost teh empiričnih enačb je, da uporabljajo en parameter za ocenitev kompresijskega indeksa (Cc), čeprav kaže Cc prostorske značilnosti, odvisne od več parametrov zemljin. Prispevek predstavlja možnost za genetsko programiranje (GEP) in Adaptive Neuro-Fuzzy (ANFIS) računski zgled za oceno kompresijskega indeksa iz parametrov zemljine kot so naravna vlažnost, meja židkosti, indeks plastičnosti, specifična gravitacija in količnik por. Skupno je bilo za razvoj modelov uporabljenih 299 podatkovnih nizov zbranih iz literature. Učinkovitost tako izdelanih modelov je bila celovito ocenjena z uporabo različnih statističnih verifikacijskih orodij. Napovedani rezultati so pokazali, da modela GEP in ANFIS omogočata dokaj obetavne pristope za napoved kompresijskega indeksa zemljin in sta lahko bolj učinkovita kot empirične enačbe.
Keywords:kompresijski indeks, statistična analiza, genetsko programiranje, ANFIS, empirične enačbe, geotehnika, zemljina


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  1. Acta geotechnica Slovenica

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