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Öğe 2D discrete element analysis of the footing above excavated circle in soil(Nature Research, 2024) Sarfarazi, V.; Tabaroei, A.; Dias, D.; Abedi, M.; Vakili, A.H.; Zhao, Y.The bearing capacity and settlements of surface foundations located on a soil slope are the important issues that have to be considered by geotechnical engineers for the design. The presence of an underground void beneath the footing can affect the foundation stability and can lead to serious structure damages. In this study, the results of two-dimensional (2D) discrete element (DE) and finite element (FE) analyses of a surface footing on a soil slope above a void are presented. To validate the numerical model results, the DE results obtained have been compared with experimental test presented in the previous study. After validation of the DE numerical model, parametric studies were carried out to evaluate the effect of important factors on the surface footing performance. The studied parameters include the horizontal spacing of the void axis relative to the slope edge (SH), the vertical spacing of the void crown relative to the footing base (SV), the horizontal spacing of the footing edge relative to the slope edge (De) and the void diameter (Dv). The effects of these parameters on the pressure-settlement curves and the contact force distributions in the soil slope are presented and discussed. The results showed that the footing bearing pressure increases with an increase of SH, SV and De but decreases when Dv increases. The behavior of a surface footing on a soil slope above a void significantly depends on the SV value. © The Author(s) 2024.Öğe Navigating heavy metal removal: Insights into advanced treatment technologies for wastewater: A review(Global NEST, 2024) Abdullayev, E.; Vakili, A.H.; Abu, Amr, S.S.; Karaagaç, S.U.; Alazaiza, M.Y.D.This paper provides an overview of heavy metal removal technologies for wastewater treatment, with a focus on adsorption, chemical oxidation, ion exchange, and various coagulation processes. The review revolves around wastewater characterization as an essential first step in creating efficient treatment systems. The study examines the uses of different treatment technologies, emphasizing both their benefits and drawbacks. Although flocculation is a rapid and economical procedure, it produces high amounts of waste and needs further filtration and sedimentation. In addition, natural coagulants are found to be more environmentally friendly than synthetic ones, their effects on water quality may make disinfectants necessary. Despite their low toxicity, stability, and environmental advantages, hybrid coagulants have certain drawbacks that are related to operational variables. Despite its broad applicability and low cost, adsorption faces challenges with regeneration and sludge creation. Although it is acknowledged to have a high metal recovery rate, ion exchange is expensive and requires special maintenance. Chemical oxidation techniques, in particular advanced oxidation processes (AOPs), are useful for eliminating heavy metals and breaking down organic materials. The limitations and difficulties of each approach are discussed in the abstract's conclusion, which highlights the necessity of future study aimed at enhancing treatment efficacy for extremely low quantities of heavy metals. The AOP shows a high efficiency in heavy metals removal with 98% of copper and 99% of cadmium. Adsorption technologies, such as activated carbon and zeolites, demonstrate high metal recovery rates of up to 95%. Ion exchange processes effectively remove heavy metals like mercury and arsenic, achieving removal efficiencies exceeding 99%. © 2024 Global NEST Printed in Greece. All rights reserved.Öğe A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering(Springer Science and Business Media Deutschland GmbH, 2024) Yaghoubi, E.; Yaghoubi, E.; Khamees, A.; Vakili, A.H.Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble learning (EL) are four outstanding approaches that enable algorithms to extract information from data and make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, and EL models have found extensive application in predicting geotechnical and geoenvironmental parameters. This research aims to provide a comprehensive assessment of the applications of ANN, ML, DL, and EL in addressing forecasting within the field related to geotechnical engineering, including soil mechanics, foundation engineering, rock mechanics, environmental geotechnics, and transportation geotechnics. Previous studies have not collectively examined all four algorithms—ANN, ML, DL, and EL—and have not explored their advantages and disadvantages in the field of geotechnical engineering. This research aims to categorize and address this gap in the existing literature systematically. An extensive dataset of relevant research studies was gathered from the Web of Science and subjected to an analysis based on their approach, primary focus and objectives, year of publication, geographical distribution, and results. Additionally, this study included a co-occurrence keyword analysis that covered ANN, ML, DL, and EL techniques, systematic reviews, geotechnical engineering, and review articles that the data, sourced from the Scopus database through the Elsevier Journal, were then visualized using VOS Viewer for further examination. The results demonstrated that ANN is widely utilized despite the proven potential of ML, DL, and EL methods in geotechnical engineering due to the need for real-world laboratory data that civil and geotechnical engineers often encounter. However, when it comes to predicting behavior in geotechnical scenarios, EL techniques outperform all three other methods. Additionally, the techniques discussed here assist geotechnical engineering in understanding the benefits and disadvantages of ANN, ML, DL, and EL within the geo techniques area. This understanding enables geotechnical practitioners to select the most suitable techniques for creating a certainty and resilient ecosystem. © The Author(s) 2024.
