Home›Search›Machine Learning and Data Science in Geotechnics

Machine Learning and Data Science in Geotechnics

Emerald Publishing · United Kingdom

eISSN3029-0422
✓ DOAJ✓ Open Access
52
/ 100
Established
PubScope credibility score from verifiable indexing & ethics signals
Score Breakdown
◆ DOAJ-listed52
Total52

Aims & Scope

Machine Learning and Data Science in Geotechnics (MLaG) aims to disseminate original contributions in the emerging fields of machine learning, artificial intelligence, big data analysis, and statistical approaches, with a focus on addressing various geotechnical engineering challenges. Submitted papers should explicitly or implicitly utilise and/or develop these themes to tackle specific geotechnical engineering scenarios or applications. The journal encourages contributions th

General Information

Country / RegionUnited Kingdom
Primary LanguageEnglish
1st Year Published—
StatusActive
Total Publications—
Visit Journal Website

Submission Info

APC Cost£1,250
Peer ReviewDouble anonymous peer review
Review Time—
Acceptance Rate54.5%
OA LicenseCC BY
OA Rate—

Ethics & Quality

COPE Member✗ No
OASPA Member✗ No
Not on Predatory Lists✓ Yes

Think.Check.Submit Compliance

9/12 · 75%
✅
Do you know the journal / publisher?
Emerald Publishing
✅
Does the journal have a website?
✓ Linked
✅
Is the ISSN verified?
3029-0422
✅
Indexed in a trusted database?
DOAJ
✅
Peer review process documented?
Double anonymous peer review
❌
Follows ethical publishing standards (COPE)?
N/A
✅
APC fees clearly disclosed?
£1,250
✅
Not on predatory/blacklists?
✓ Clean
❌
Long-term digital preservation?
N/A
❌
Plagiarism detection in place?
N/A
✅
Listed in DOAJ (verified OA)?
DOAJ verified
✅
Primary language documented?
English

Based on the Think.Check.Submit framework by DOAJ, COPE & OASPA. All data from verified open sources.

Subject Classification

Research Topics (OpenAlex)

Tunneling and Rock MechanicsGeotechnical Engineering and AnalysisGeotechnical Engineering and Soil MechanicsDrilling and Well EngineeringDam Engineering and SafetyLandslides and related hazardsSoil and Unsaturated FlowConcrete and Cement Materials ResearchRock Mechanics and ModelingProbabilistic and Robust Engineering Design

Frequently asked questions about Machine Learning and Data Science in Geotechnics

Is Machine Learning and Data Science in Geotechnics a predatory journal?

PubScope has no integrity flags on record for Machine Learning and Data Science in Geotechnics: it is indexed in DOAJ, and is not on DOAJ's withdrawn list. Its PubScope Trust Score is 52/100. Indexing is a transparency signal, not a guarantee — always confirm fit and policies before submitting.

What is the impact factor of Machine Learning and Data Science in Geotechnics?

Machine Learning and Data Science in Geotechnics is not in the Web of Science Core Collection, so it has no official Clarivate Journal Impact Factor.

Is Machine Learning and Data Science in Geotechnics indexed in Scopus and Web of Science?

Machine Learning and Data Science in Geotechnics is indexed in DOAJ.

What is the aims and scope of Machine Learning and Data Science in Geotechnics?

Machine Learning and Data Science in Geotechnics (MLaG) aims to disseminate original contributions in the emerging fields of machine learning, artificial intelligence, big data analysis, and statistical approaches, with a focus on addressing various geotechnical engineering challenges. Submitted papers should explicitly or implicitly utilise and/or develop these themes to tackle specific geotechnical engineering scenarios or applications. The journal encourages contributions th

What is the acceptance rate of Machine Learning and Data Science in Geotechnics?

Machine Learning and Data Science in Geotechnics lists a self-reported acceptance rate of approximately 54.5%. Journal-reported figures can be optimistic — treat it as a rough guide, not a guarantee.

How much does it cost to publish in Machine Learning and Data Science in Geotechnics?

Machine Learning and Data Science in Geotechnics charges an article processing charge (APC) of about £1,250 for open-access publication.

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Data updated: 2026-05-26 · Sources: SJR, DOAJ, OpenAlex, WoS, Crossref