Akış, Ebru

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Profile URL
https://hdl.handle.net/20.500.14411/6690
Name Variants
A., Ebru
E., Akis
E., Akış
Akis E.
Akış,E.
Ebru Akış
E.,Akış
A.,Ebru
E.,Akis
Akis,E.
Akiş E.
Akış, Ebru
Akis, Ebru
Akis,Ebru
Ebru, Akış
Ebru, Akis
Job Title
Doktor Öğretim Üyesi
Email Address
ebru.akis@atilim.edu.tr
Main Affiliation
Civil Engineering
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Website
ORCID ID
0000-0001-8417-2405
Scopus Author ID
8240634500
Turkish CoHE Profile ID
19FD36C6692BD0F9
Google Scholar ID
hCCagyUAAAAJ
WoS Researcher ID
DVV-1883-2022 KOQ-4325-2024

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NO POVERTY1
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ZERO HUNGER2
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GOOD HEALTH AND WELL-BEING3
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QUALITY EDUCATION4
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GENDER EQUALITY5
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CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
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AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
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DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
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INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
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REDUCED INEQUALITIES10
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SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
2
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RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
1
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CLIMATE ACTION13
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LIFE BELOW WATER14
LIFE BELOW WATER
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LIFE ON LAND15
LIFE ON LAND
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PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
1
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PARTNERSHIPS FOR THE GOALS17
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19

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13

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6

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23

Scopus Citation Count

35

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1.21

Scopus Citations per Publication

1.84

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8

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6

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Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi1
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Author: Cigdem,O.Y.Subject: Regression analysis

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    Article
    Citation - Scopus: 6
    Predictive Models for Treated Clayey Soils Using Waste Powdered Glass and Expanded Polystyrene Beads Using Regression Analysis and Artificial Neural Network
    (Springer Science and Business Media Deutschland GmbH, 2024) Akis,E.; Akış, Ebru; Cigdem,O.Y.; Akış, Ebru; Civil Engineering; Civil Engineering
    Waste materials contribute to a wide range of environmental and economic problems. To minimize their effects, a safe strategy for reducing such negative impact is required. Recycling and reusing waste materials have proved to be effective measures in this respect. In this study, an eco-friendly treatment is investigated based on using waste powdered glass (WGP) and EPS beads (EPSb) as mechanical and chemical admixers in soils. For this purpose, Atterberg limit, standard proctor, free swell, and unconfined compression tests are performed on soil samples with different ratios of waste materials at their optimum moisture contents. The obtained test results indicate that adding WGP to cohesive soils increases the unconfined compressive strength (UCS) and reduces free swell (FS). In contrast, using EPSb reduces both FS and UCS of the treated soil samples. An optimum combination of both waste materials is determined for the improvement of the properties of high plasticity clay used in this study. Furthermore, multiple linear regression (MLR) and artificial neural network (ANN) methods are used to predict the FS and UCS of the clayey soils based on the data obtained here and the experimental test results reported in the literature. Once the FS and UCS values of untreated soil and additive percentages are defined as independent variables, both methods are shown to predict the FS and UCS values of the treated soil samples on a satisfactory level with the coefficient of correlation (R2) values greater than 0.926. Additionally, when only the index properties (liquid limit, plastic limit, and plasticity index) of the soil samples with waste materials are used as dependent variables, the R2 values obtained by the ANN method are 0.968 and 0.974 for FS and UCS, respectively. The results of the untreated soil samples' FS and UCS tests are known, and the linear regression and ANN techniques yield similar results. Lastly, the ANN method is used to predict the FS and UCS of the treated samples in accordance to the limited predictors (e.g., only the Atterberg limits of the soil sample). © The Author(s) 2024.