Journal Information

Computers and Geotechnics

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Impact Factor:
6.2
Publisher:
Elsevier
ISSN:
0266-352X
Viewed:
19362
Tracked:
0

Call For Papers

Computers and Geotechnics is an academic journal published by Elsevier. (ISSN 0266-352X, impact factor 6.2).

The use of computers is firmly established in geotechnical engineering and continues to grow rapidly in both engineering practice and academe. The development of advanced numerical techniques and constitutive modeling, in conjunction with rapid developments in computer hardware, enables problems to be tackled that were unthinkable even a few years ago. Computers and Geotechnics provides an up-to-date reference for engineers and researchers engaged in computer aided analysis and research in geotechnical engineering. The journal is intended for an expeditious dissemination of advanced computer applications across a broad range of geotechnical topics. Contributions on advances in numerical algorithms, computer implementation of new constitutive models and probabilistic methods are especially encouraged. The application of newly proposed numerical methods and techniques to complex geotechnical engineering problems or to well-documented case studies are of interest. However, submissions that predominantly report results from proprietary codes, describe computer modelling of laboratory tests, field monitoring, or case histories, or develop new design approaches are only welcome if they demonstrate novel user-implemented computational methods. Mining, petroleum, or transportation engineering topics are usually discouraged as they align more closely with other journals. Since the journal is willing to accept longer papers if justified, authors are asked to avoid two-part submissions. Original contributions in the emerging areas of Machine Learning and Data Science are now welcome. Submissions should have a focus on geotechnical engineering problems and should provide either i) advances in foundational algorithms and computational frameworks or ii) innovative applications of physics-informed AI/ML techniques. Research results are sought that leverage the integration of observational data, fundamental physical laws and our domain knowledge in geomechanics and geotechnical engineering to offer new physical insights, uncover hidden intrinsic physical laws, and create new knowledge for both geotechnical researchers and practitioners.
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