GeoHazards

24 pages, 3766 KiB

Open AccessArticle

Comprehensive Evaluation of Sliding and Overturning Failure in Mechanically Stabilized Earth (MSE) Retaining Walls Considering the Effect of Hydrostatic Pressure

by Arash K. Pour, Amir Shirkhani and Ehsan Noroozinejad Farsangi

GeoHazards 2025, 6(3), 35; https://doi.org/10.3390/geohazards6030035 - 10 Jul 2025

Abstract

Mechanically stabilized earth (MSE) retaining walls have become a favored substitute for traditional poured concrete walls due to their affordability, minimal site preparation needs, and practical construction advantages. However, using backfill material with too many small particles and poor drainage qualities may cause the wall to rotate and shift a lot or collapse completely, especially when water pressure is present. This study examines an MSE wall considering different variables, such as water pressure, the type of soil materials in the backfill materials, external load, and the type of analysis. To this aim, both PLAXIS V20 and SLOPE/W (GeoStudio 2019 Suite) software were employed, and after the verification, further investigations were carried out. These numerical analyses aligned with the real-world failure reported by previous researchers, departments, and companies. The findings suggest that the elevated presence of fine particles likely contributed to the wall’s excessive shift. Also, hydrostatic pressure behind a wall, especially in the rainy season, plays a crucial role in the factor of safety reduction by 45% and wall failure, which leads us to consider it an appropriate factor of safety for the MSE wall. Full article

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21 pages, 14023 KiB

Open AccessArticle

Geomatic Techniques for the Mitigation of Hydrogeological Risk: The Modeling of Three Watercourses in Southern Italy

by Serena Artese and Giuseppe Artese

GeoHazards 2025, 6(3), 34; https://doi.org/10.3390/geohazards6030034 - 2 Jul 2025

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Abstract

In recent decades, climate change has led to more frequent episodes of extreme rainfall, increasing the risk of river flooding. Streams and rivers characterized by short flow times are subject to rapid and impressive floods; for this reason, the modeling of their beds is of fundamental importance for the execution of hydraulic calculations capable of predicting the flow rates and identifying the points where floods may occur. In the context of studies conducted on three watercourses in Calabria (Italy), different survey and restitution techniques were used (aerial LiDAR, terrestrial laser scanner, GNSS, photogrammetry). By integrating these methodologies, multi-resolution models were generated, featuring a horizontal accuracy of ±16 cm and a vertical accuracy of ±15 cm. These models form the basis for the hydraulic calculations performed. The results demonstrate the feasibility of producing accurate models that are compatible with the memory and processing capabilities of modern computers. Furthermore, the technique set up and implemented for the refined representation of both the models and the effects predicted by hydraulic calculations in the event of exceptional rainfall (such as flow, speed, flooded areas, and critical points along riverbanks) serves as a valuable tool for improving hydrogeological planning, designing appropriate defense works, and preparing evacuation plans in case of emergency, all with the goal of mitigating hydrogeological risk. Full article

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20 pages, 1496 KiB

Open AccessArticle

Utilizing LLMs and ML Algorithms in Disaster-Related Social Media Content

by Vasileios Linardos, Maria Drakaki and Panagiotis Tzionas

GeoHazards 2025, 6(3), 33; https://doi.org/10.3390/geohazards6030033 - 2 Jul 2025

Viewed by 251

Abstract

In this research, we explore the use of Large Language Models (LLMs) and clustering techniques to automate the structuring and labeling of disaster-related social media content. With a gathered dataset comprising millions of tweets related to various disasters, our approach aims to transform unstructured and unlabeled data into a structured and labeled format that can be readily used for training machine learning algorithms and enhancing disaster response efforts. We leverage LLMs to preprocess and understand the semantic content of the tweets, applying several semantic properties to the data. Subsequently, we apply clustering techniques to identify emerging themes and patterns that may not be captured by predefined categories, with these patterns surfaced through topic extraction of the clusters. We proceed with manual labeling and evaluation of 10,000 examples to evaluate the LLMs’ ability to understand tweet features. Our methodology is applied to real-world data for disaster events, with results directly applicable to actual crisis situations. Full article

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GeoHazards, Volume 6, Issue 3 (September 2025) – 3 articles

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