Topic Editors

Faculty of Civil Engineering, University of Zagreb, 10000 Zagreb, Croatia
Geotechnical Department, School of Civil Engineering, National Technical University of Athens (NTUA), 157 80 Athens, Greece‎

Advanced Risk Assessment in Geotechnical Engineering

Abstract submission deadline
31 May 2025
Manuscript submission deadline
31 July 2025
Viewed by
2678

Topic Information

Dear Colleagues,

Aim:

Risk assessment in geotechnical engineering is essential for the overall success of civil engineering projects, as it plays a fundamental role in ensuring their safety and reliability. Advanced risk assessment methods aim to enhance the understanding and mitigation of risks associated with the uncertain subsurface conditions, relevant for a range of geotechnical structures such as foundations, tunnels, foundation pits, retaining walls, reinforced soil, earthen structures (dams, levees), etc. A number of innovative and sophisticated methodologies and tools have been employed and developed in recent years, with the overall aim of assessing and managing the soil and rock-related uncertainties. Since the risk assessment in geotechnical engineering requires a multidisciplinary approach, this topic considers theoretical aspects and experimental work in domain of geology, hydrogeology, engineering-geology, geotechnics, civil engineering, environmental engineering, as well in other relevant branches of science. In addition to addressing the mentioned uncertainties in subsurface conditions, effective risk assessment ensures the safety of structures and human lives, helps in the identification and mitigation of geo-hazards, provides optimization in the design and construction of geotechnical structures, and ensures compliance with regulations and standards as well as long-term performance and sustainability.

Scope:

The scope of this topic includes a range of innovative aspects which could boost up practitioners' and researchers' awareness of risk assessment importance in geotechnical engineering. These aspects include the following:

  • advanced methods for soil and rock characterization and subsurface data collection;
  • advanced risk modelling and analysis with focus on probabilistic methods;
  • geo-hazard identification and management;
  • monitoring (with focus on advanced geotechnical and remote sensing methods) with development of early warning systems;
  • identification and incorporation of climate and environmental factors into the risk assessment procedures;
  • adherence to relevant industry standards, codes, and regulations in conducting risk assessments;
  • development of risk-informed decision support tools for the relevant stakeholders in field of geotechnical engineering.

Prof. Dr. Meho-Saša Kovačević
Dr. Vassilis Marinos
Topic Editors

Keywords

  • risk assessment
  • risk modelling
  • geohazards
  • geotechnical engineering
  • monitoring
  • safety
  • reliability
  • uncertainties
  • standards

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
GeoHazards
geohazards
- 2.6 2020 20.4 Days CHF 1000 Submit
Geosciences
geosciences
2.4 5.3 2011 26.2 Days CHF 1800 Submit
Geotechnics
geotechnics
- - 2021 16.9 Days CHF 1000 Submit
Remote Sensing
remotesensing
4.2 8.3 2009 24.7 Days CHF 2700 Submit
Sensors
sensors
3.4 7.3 2001 16.8 Days CHF 2600 Submit
Standards
standards
- - 2021 37.9 Days CHF 1000 Submit

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Published Papers (2 papers)

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18 pages, 9240 KiB  
Article
Identification and Analysis of the Geohazards Located in an Alpine Valley Based on Multi-Source Remote Sensing Data
by Yonglin Yang, Zhifang Zhao, Dingyi Zhou, Zhibin Lai, Kangtai Chang, Tao Fu and Lei Niu
Sensors 2024, 24(13), 4057; https://doi.org/10.3390/s24134057 - 21 Jun 2024
Viewed by 892
Abstract
Geohazards that have developed in densely vegetated alpine gorges exhibit characteristics such as remote occurrence, high concealment, and cascading effects. Utilizing a single remote sensing datum for their identification has limitations, while utilizing multiple remote sensing data obtained based on different sensors can [...] Read more.
Geohazards that have developed in densely vegetated alpine gorges exhibit characteristics such as remote occurrence, high concealment, and cascading effects. Utilizing a single remote sensing datum for their identification has limitations, while utilizing multiple remote sensing data obtained based on different sensors can allow comprehensive and accurate identification of geohazards in such areas. This study takes the Latudi River valley, a tributary of the Nujiang River in the Hengduan Mountains, as the research area, and comprehensively uses three techniques of remote sensing: unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR), Small Baseline Subset interferometric synthetic aperture radar (SBAS-InSAR), and UAV optical remote sensing. These techniques are applied to comprehensively identify and analyze landslides, rockfalls, and debris flows in the valley. The results show that a total of 32 geohazards were identified, including 18 landslides, 8 rockfalls, and 6 debris flows. These hazards are distributed along the banks of the Latudi River, significantly influenced by rainfall and distribution of water systems, with deformation variables fluctuating with rainfall. The three types of geohazards cause cascading disasters, and exhibit different characteristics in the 0.5 m resolution hillshade map extracted from LiDAR data. UAV LiDAR has advantages in densely vegetated alpine gorges: after the selection of suitable filtering algorithms and parameters of the point cloud, it can obtain detailed terrain and geomorphological information on geohazards. The different remote sensing technologies used in this study can mutually confirm and complement each other, enhancing the capability to identify geohazards and their associated hazard cascades in densely vegetated alpine gorges, thereby providing valuable references for government departments in disaster prevention and reduction work. Full article
(This article belongs to the Topic Advanced Risk Assessment in Geotechnical Engineering)
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32 pages, 17404 KiB  
Article
A Novel Method for Full-Section Assessment of High-Speed Railway Subgrade Compaction Quality Based on ML-Interval Prediction Theory
by Zhixing Deng, Wubin Wang, Linrong Xu, Hao Bai and Hao Tang
Sensors 2024, 24(11), 3661; https://doi.org/10.3390/s24113661 - 5 Jun 2024
Cited by 1 | Viewed by 908
Abstract
The high-speed railway subgrade compaction quality is controlled by the compaction degree (K), with the maximum dry density (ρdmax) serving as a crucial indicator for its calculation. The current mechanisms and methods for determining the ρdmax [...] Read more.
The high-speed railway subgrade compaction quality is controlled by the compaction degree (K), with the maximum dry density (ρdmax) serving as a crucial indicator for its calculation. The current mechanisms and methods for determining the ρdmax still suffer from uncertainties, inefficiencies, and lack of intelligence. These deficiencies can lead to insufficient assessments for the high-speed railway subgrade compaction quality, further impacting the operational safety of high-speed railways. In this paper, a novel method for full-section assessment of high-speed railway subgrade compaction quality based on ML-interval prediction theory is proposed. Firstly, based on indoor vibration compaction tests, a method for determining the ρdmax based on the dynamic stiffness Krb turning point is proposed. Secondly, the Pso-OptimalML-Adaboost (POA) model for predicting ρdmax is determined based on three typical machine learning (ML) algorithms, which are back propagation neural network (BPNN), support vector regression (SVR), and random forest (RF). Thirdly, the interval prediction theory is introduced to quantify the uncertainty in ρdmax prediction. Finally, based on the Bootstrap-POA-ANN interval prediction model and spatial interpolation algorithms, the interval distribution of ρdmax across the full-section can be determined, and a model for full-section assessment of compaction quality is developed based on the compaction standard (95%). Moreover, the proposed method is applied to determine the optimal compaction thicknesses (H0), within the station subgrade test section in the southwest region. The results indicate that: (1) The PSO-BPNN-AdaBoost model performs better in the accuracy and error metrics, which is selected as the POA model for predicting ρdmax. (2) The Bootstrap-POA-ANN interval prediction model for ρdmax can construct clear and reliable prediction intervals. (3) The model for full-section assessment of compaction quality can provide the full-section distribution interval for K. Comparing the H0 of 50~60 cm and 60~70 cm, the compaction quality is better with the H0 of 40~50 cm. The research findings can provide effective techniques for assessing the compaction quality of high-speed railway subgrades. Full article
(This article belongs to the Topic Advanced Risk Assessment in Geotechnical Engineering)
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