Write in a clean editor, then format for Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards in one click — DocuGuru applies the official Taylor & Francis template with author–year references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards is a peer-reviewed journal published by Taylor & Francis, covering Geotechnical Engineering and Analysis, Landslides and related hazards, Geotechnical Engineering and Underground Structures. Over its lifetime it has published 694 papers, which have received 10,637 citations.
| Publisher | Taylor & Francis |
|---|---|
| Reference style | Author–year (Chicago, T&F) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 12 (3): 45–58.
Formats any DOI in Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards style. No sign-up. |
| Publishes research in | Geotechnical Engineering and Analysis Landslides and related hazards Geotechnical Engineering and Underground Structures Geotechnical Engineering and Soil Stabilization Dam Engineering and Safety |
| ISSN | 1749-9518 |
| Citation impact (2-yr) | 3.52 |
| h-index | 47 |
| i10-index | 304 |
| Top institutions publishing here | Delft University of Technology |
| Journal website | www.tandfonline.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Machine learning (ML) is widely used in many industries, resulting in recent interests to explore ML in geotechnical engineering. Past review papers focus mainly on ML algorithms while this paper advocates an agenda to put data at the core, to develop novel algorithms that are effective for geotechnical data (existing and new), to address the…
Describing how soil properties vary spatially is of particular importance in stochastic analyses of geotechnical problems, because spatial variability has a significant influence on local material and global geotechnical response. In particular, the scale of fluctuation θ is a key parameter in the correlation model used to represent the spatial variability of a site through…
A dam may fail when the loading exceeds the resistance against overtopping, internal erosion, slope instability, sliding/overturning, excessive deformation etc. To investigate the causes of failures, it is necessary to study characteristics of the dams which have experienced failures. In this work, more than 1600 dam failure cases throughout the world excluding China are compiled…
Driven pile is widely used as an effective and convenient structural component to transfer superstructure loads to deep stiffer soils. Nevertheless, during the design process of piles, due to the intrinsic complexity as well as various design variables, the internal stress state related to pile drivability remains unclear, which makes the analysis imprecise. Thus, the…
Site characterisation is a cornerstone of geotechnical and rock engineering. “Data-driven site characterisation” refers to any site characterisation methodology that relies solely on measured data, both site-specific data collected for the current project and existing data of any type collected from past stages of the same project or past projects at the same site, neighbouring…
