Evidence map›Paper›PMID 42225787›Full record

ArticleScientific reports2026

Predicting seismic-induced liquefaction potential of gravelly soils using dynamic penetration case histories.

Mike Onyia, Siddharth Siddharth, Ahmed Ebid, Shadi Hanandeh, Nakkeeran Ganasen, Krishna Prakash Arunachalam, Ahmed Elsheikh, Abdelmalek H Aljarah, Viroon Kamchoom

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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

9 authors.

Mike OnyiaDepartment of Computer Science Technology and Innovation, Central University Sierra Leone, Tonkolili, Sierra Leone. onyiamike614@gmail.com.
Siddharth SiddharthDepartment of Civil Engineering, Poornima College of Engineering, Jaipur, India. siddharth.choudhary@poornima.org.
Ahmed EbidDepartment of Civil Engineering, Future University in Egypt, New Cairo, Egypt. ahmed.abdelkhaleq@fue.edu.eg.
Shadi HanandehDepartment of Civil Engineering, Al-Balqa Applied University, As-Salt, Jordan.
Nakkeeran GanasenDepartment of Civil Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India.
Krishna Prakash ArunachalamDepartamento de Ciencias de la Construcción, Facultad de Ciencias de la Construcción Ordenamiento Territorial, Universidad Tecnológica Metropolitana, Santiago, Chile.
Ahmed ElsheikhCollege of Aerospace and Civil Engineering, Harbin Engineering University, Harbin, 150001, China.
Abdelmalek H AljarahDepartment of Civil, Architectural and Environmental Engineering, Illinois Institute of Technology, Chicago, USA.
Viroon KamchoomExcellent Center for Green and Sustainable Infrastructure, Department of Civil Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang (KMITL), Bangkok, 10520, Thailand. viroon.ka@kmitl.ac.th.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As demonstrated by numerous environmental disasters worldwide, many sites have suffered from seismically induced liquefaction, resulting in substantial economic losses. Consequently, there is an urgent need for reliable prediction methods to assess vulnerability to liquefaction. In this study, the liquefaction potential (LP) of gravelly soil sites is predicted using available seismological parameters (M

Indexed as

Dynamic penetration testsEarthquakeGravelly soilLiquefactionSeismic precursorSymbolic machine learning

Identifiers

PMID42225787
PMCPMC13462127

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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.