Evidence map›Paper›PMID 42062407›Full record

ArticleScientific reports2026

Performance evaluation of machine learning algorithms for predicting liquefaction-induced lateral displacement.

Mahmood Ahmad, Mohammad Al Zubi, Shaikat Biswas, Abdur Rahman Rakib, Abdullah Alzlfawi, Sabahat Hussan, Shay Haq, Rohayu Che Omar, Zia Ullah, Zsolt Tóth

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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.

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0citing papers in PubMed
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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

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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

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5 · Who and what money

Authors and funding

10 authors.

Mahmood AhmadInstitute of Energy Infrastructure, Universiti Tenaga Nasional, Kajang, 43000, Malaysia.
Mohammad Al ZubiDepartment of Mechanical Engineering, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid, Jordan.
Shaikat BiswasDeparment of Computer Science, Troy University, 600 University Ave, Troy, AL, 36082, USA.
Abdur Rahman RakibDeparment of Computer Science, Troy University, 600 University Ave, Troy, AL, 36082, USA.
Abdullah AlzlfawiDepartment of Civil and Environmental Engineering, College of Engineering, Majmaah University, Al Majmaah, 11952, Saudi Arabia.
Sabahat HussanDepartment of Civil Engineering, HITEC University, Taxila, 47040, Pakistan.
Shay HaqDepartment of Civil Engineering, National University of Sciences and Technology, Balochistan Campus, Quetta, 87300, Pakistan.
Rohayu Che OmarInstitute of Energy Infrastructure, Universiti Tenaga Nasional, Kajang, 43000, Malaysia.
Zia UllahBroadridge Financial Solutions Inc, 51 Mercedes Way, Brentwood, NY, 11717, USA.
Zsolt TóthFaculty of Wood Engineering and Creative Industries, University of Sopron, Sopron, Hungary. toth.zsolt@uni-sopron.hu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate prediction of liquefaction-induced lateral displacement is essential for seismic risk assessment, resilient infrastructure design, and cost-effective mitigation. Such predictions are complex and cannot be reliably addressed using conventional analytical approaches. This research utilizes XGBoost, CatBoost and AdaBoost algorithms with 247 post-liquefaction in-situ free-face ground condition case studies to model and investigate liquefaction induced lateral displacements. The models are assessed according to the coefficient of determination (R

Indexed as

Lateral displacementMachine learningPerformance measureSoil liquefaction

Identifiers

PMID42062407
PMCPMC13324425

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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.