Evidence map›Paper›PMID 41605977›Full record

ArticleScientific reports2026

Predicting the mechanical behavior of municipal solid waste layers in the Barmshour Landfill stability analysis.

Mohammad Mehdi Mokhtari, Amin Falamaki, Mahmood Vafaeian

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Mohammad Mehdi MokhtariDepartment of Civil Engineering, Isf. C., Islamic Azad University, Isfahan, Iran.
Amin FalamakiDepartment of Civil Engineering, Payame Noor University, P.O. Box 19395-3697, Tehran, Iran. a_falamaki@pnu.ac.ir.
Mahmood VafaeianDepartment of Civil Engineering, Isfahan University of Technology, Isfahan, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Slope stability is a crucial aspect of geotechnical engineering, particularly for landfills where municipal solid waste (MSW) layers are subjected to both static and seismic forces. This study represents the first application of hybrid metaheuristic-neural models to the Barmshour Landfill, introducing an innovative predictive framework capable of guiding real-world design, stability evaluation, and decision-making processes in waste management engineering. Four hybrid models-BBO-MLP, MVO-MLP, VS-MLP, and BSA-MLP-were developed and evaluated using real data from the Barmshour Landfill in Shiraz, Iran. The MVO-MLP model achieved the best performance, with coefficient of determination (R

Indexed as

Geotechnical engineeringHybrid neural networksMetaheuristic optimizationSlope stability

Identifiers

PMID41605977
PMCPMC12917201

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Registered trials

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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.