Advanced Risk Assessment in Geotechnical Engineering

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

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
GeoHazards geohazards	-	2.6	2020	20.7 Days	CHF 1000	Submit
Geosciences geosciences	2.7	5.3	2011	23.6 Days	CHF 1800	Submit
Geotechnics geotechnics	-	-	2021	15.6 Days	CHF 1000	Submit
Remote Sensing remotesensing	5.0	8.3	2009	23 Days	CHF 2700	Submit
Sensors sensors	3.9	7.3	2001	17 Days	CHF 2600	Submit
Standards standards	-	-	2021	45.6 Days	CHF 1000	Submit

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Published Papers (1 paper)

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All GeoHazards Geosciences Geotechnics Remote Sensing Sensors Standards

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32 pages, 17404 KiB  

Open AccessArticle

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

Viewed by 291

Abstract

The high-speed railway subgrade compaction quality is controlled by the compaction degree (K), with the maximum dry density (ρ_dmax) serving as a crucial indicator for its calculation. The current mechanisms and methods for determining the ρ_dmax [...] Read more.

The high-speed railway subgrade compaction quality is controlled by the compaction degree (K), with the maximum dry density (ρ_dmax) serving as a crucial indicator for its calculation. The current mechanisms and methods for determining the ρ_dmax 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 ρ_dmax based on the dynamic stiffness K_rb turning point is proposed. Secondly, the Pso-OptimalML-Adaboost (POA) model for predicting ρ_dmax 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 ρ_dmax prediction. Finally, based on the Bootstrap-POA-ANN interval prediction model and spatial interpolation algorithms, the interval distribution of ρ_dmax 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 ρ_dmax. (2) The Bootstrap-POA-ANN interval prediction model for ρ_dmax 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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