Evidence map›Paper›PMID 39080333›Full record

ArticleScientific reports2024

Application of hybridized ensemble learning and equilibrium optimization in estimating damping ratios of municipal solid waste.

Hossein Moradi Moghaddam, Mohsen Keramati, Alireza Bahrami, Ali Reza Ghanizadeh, Amir Tavana Amlashi, Haytham F Isleem, Mohsen Navazani, Samer Dessouky

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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Hossein Moradi MoghaddamSchool of Engineering, University of British Columbia, Kelowna, Canada. hmoradi8@student.ubc.ca.
Mohsen KeramatiFaculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran.
Alireza BahramiDepartment of Building Engineering, Energy Systems and Sustainability Science, Faculty of Engineering and Sustainable Development, University of Gävle, 801 76, Gävle, Sweden. alireza.bahrami@hig.se.
Ali Reza GhanizadehDepartment of Civil Engineering, Sirjan University of Technology, Sirjan, Iran.
Amir Tavana AmlashiSchool of Civil and Environmental Engineering and Construction Management, University of Texas at San Antonio, San Antonio, USA.
Haytham F IsleemSchool of Applied Technologies, Qujing Normal University, Qujing, 655011, Yunnan, China.
Mohsen NavazaniSchool of Civil and Environmental Engineering and Construction Management, University of Texas at San Antonio, San Antonio, USA.
Samer DessoukySchool of Civil and Environmental Engineering and Construction Management, University of Texas at San Antonio, San Antonio, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The dynamic analysis of municipal solid waste (MSW) is essential for optimizing landfills and advancing sustainable development goals. Assessing damping ratio (D), a critical dynamic parameter, under laboratory conditions is costly and time-consuming, requiring specialized equipment and expertise. To streamline this process, this research leveraged several novel ensemble machine learning models integrated with the equilibrium optimizer algorithm (EOA) for the predictive analysis of damping characteristics. Data were gathered from 153 cyclic triaxial experiments on MSW, which examined the age, shear strain, weight, frequency, and percentage of plastic content. Analysis of a correlation heatmap indicated a significant dependence of D on shear strain within the collected MSW data. Subsequently, five advanced machine learning methods-adaptive boosting (AdaBoost), gradient boosting regression tree (GBRT), extreme gradient boosting (XGBoost), random forest (RF), and cubist regression-were employed to model D in landfill structures. Among these, the GBRT-EOA model demonstrated superior performance, with a coefficient of determination (R

Indexed as

DampingEnsemble learnersEquilibrium optimizer algorithmMachine learningMunicipal solid wasteTriaxial cyclic test

Identifiers

PMID39080333
PMCPMC11289416

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.