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Disaster Prevention and Environmental Protection of Geotechnical Engineering-Monitoring, Modelling and Simulation

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 3458

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Prof. Dr. Shinya Inazumi

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Guest Editor

Department of Civil Engineering, Shibaura Institute of Technology, Tokyo 1358548, Japan
Interests: geotechnical engineering, environmental protection; disaster prevention; waste recycle
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Dr. Jim S. Shiau

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Guest Editor

School of Engineering, University of Southern Queensland, Toowoomba, QLD 4350, Australia
Interests: numerical modelling for geotechnical construction, geotechnical engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The goal of the geotechnical engineering is to manage and maintain geo-structures and social infrastructures in harmony with the natural and social environments, thereby allowing future human beings to inherit and develop sustainable urban infrastructures and urban environments.

State-of-the-art geotechnical engineering is the process of conducting research activities that can contribute to the creation and development of geo-structures and social infrastructures that can deal with various problems that may occur today or in the future of our living earth.

This Special Issue calls papers about various challenging aspects of research related to geotechnical engineering that are being implemented with the goals of environmental protection and disaster prevention, as described above.

Prof. Dr. Shinya Inazumi
Dr. Jim S. Shiau
Guest Editors

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Keywords

geotechnical engineering
dnvironmental protection
disaster prevention
waste recycle
monitoring
modeling
simulation
management

Published Papers (3 papers)

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Research

12 pages, 1201 KiB

Open AccessArticle

Analysis of Ground Subsidence Vulnerability in Urban Areas Using Spatial Regression Analysis

by Sungyeol Lee, Jaemo Kang and Jinyoung Kim

Appl. Sci. 2023, 13(15), 8603; https://0-doi-org.brum.beds.ac.uk/10.3390/app13158603 - 26 Jul 2023

Cited by 1 | Viewed by 745

Abstract

The main cause of ground subsidence accidents in urban areas is cavities formed by damage to underground utilities. For this reason, the attribute information of underground utilities should be used to prepare against ground subsidence accidents. In this study, attribute information (pipe age, diameter, burial depth, and density) of six types of underground utilities (water, sewer, gas, power, heating, and communication) and history information of ground subsidence were collected. A correlation analysis was conducted using the collected data, and a prediction model of vulnerability to ground subsidence was developed through the ordinary least squares (OLS) method and spatial regression analysis (spatial lag model (SLM) and spatial error model (SEM)). To do this, the target area was divided into a grid of 100 m × 100 m. Datasets were constructed using the attribute information of underground utilities included in the divided grid and the number of ground subsidence occurrences. To analyze the OLS of the constructed data, the variance inflation factor (VIF) of the attribute information of underground utilities was studied. An OLS analysis was conducted using the appropriate factors, and the results show that the spatial data were autocorrelated. Subsequently, SEM and SLM analyses, which were spatial regression analyses, were conducted. As a result, the model using SLM was selected as suitable for analyzing the vulnerability of ground subsidence, and the density of six types of underground utilities was found to be the highest influencing factor. In addition, a vulnerability map of ground subsidence in the target area was prepared using the model. The vulnerability map demonstrates that regions with frequent ground subsidence can be predicted to be highly vulnerable. Full article

(This article belongs to the Special Issue Disaster Prevention and Environmental Protection of Geotechnical Engineering-Monitoring, Modelling and Simulation)

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12 pages, 6411 KiB

Open AccessArticle

Neutralization Treatment for Recycling Construction-Generated Soils

by Koki Nakao, Sudip Shakya, Tetsuya Nozaki and Shinya Inazumi

Appl. Sci. 2023, 13(11), 6622; https://doi.org/10.3390/app13116622 - 30 May 2023

Cited by 1 | Viewed by 1366

Abstract

The promotion of the SDGs (Sustainable Development Goals) for a sustainable and better society has been actively implemented in all sectors, including the construction industry. In contrast to other industries, however, the construction industry produces a great amount of waste while using a vast amount of resources. The effective utilization of resources is a hot topic of discussion in the construction industry, and sectors remain wherein the effective management of resource utilization is still in progress. Proper disposal of construction-generated soil is one of the issues that needs attention. This type of soil is primarily alkaline and cannot be disposed of without appropriate treatment. The traditional method of treating construction-generated soil before its disposal is the neutralization titration method, where the soil is mixed with a weak acid until the pH value has been lowered to 7. However, this process is performed according to empirical rules, and the exact amount of acid required has yet to be discovered. Therefore, the aim of this study was to derive a theoretical equation to determine the necessary amount of neutralizer. The theoretical equation was successfully derived, and the consistency of the output results was verified for three different neutralizers. Finally, vegetation tests were conducted on each neutralized soil sample to check its suitability as a vegetation substrate. Full article

(This article belongs to the Special Issue Disaster Prevention and Environmental Protection of Geotechnical Engineering-Monitoring, Modelling and Simulation)

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12 pages, 3279 KiB

Open AccessArticle

Time-Series Prediction of Long-Term Sustainability of Grounds Improved by Chemical Grouting

by Shinya Inazumi, Sudip Shakya, Chifong Chio, Hideki Kobayashi and Supakij Nontananandh

Appl. Sci. 2023, 13(3), 1333; https://0-doi-org.brum.beds.ac.uk/10.3390/app13031333 - 19 Jan 2023

Viewed by 1096

Abstract

In the field of geotechnical engineering, the problems of liquefaction and land subsidence are of major concern. In order to mitigate or prevent damage from liquefaction, the chemical injection method is actively used as one of the countermeasures for ground improvement. However, a complete understanding of the long-term sustainability of improved grounds is still unavailable due to a lack of knowledge of the influencing parameters. Thus, the chances of chemical injection accidents cannot be ruled out. In this study, the compressive strength of improved grounds by the granulated blast furnace slag (GBFS), one of the grouting materials used in the chemical injection method, was evaluated and used for a time-series prediction of long-term sustainability. The objective of this study was to evaluate the accuracy and validity of the prediction method by comparing the prediction results with the test results. The study was conducted for three different models, namely, the autoregressive integrated moving average (ARIMA) model, the state-space representation (SSR) model, and the machine learning predictive (MLP) model. The MLP model produced the most reliable results for the prediction of long-term data when the input information was sufficient. However, when the input data were scarce, the SSR model produced more reliable results overall. Meanwhile, the ARIMA model generated the highest degree of errors, although it produced the best results compared to the other models depending on the criteria. It is advised that studies should be continued in order to identify the parameters that can affect the long-term sustainability of improved grounds and to simulate various other models to determine the best model to be used in all situations. However, this study can be used as a reference for the selection of the best prediction model for similar patterned input data, in which remarkable changes are observed only at the beginning and become negligible at the end. Full article

(This article belongs to the Special Issue Disaster Prevention and Environmental Protection of Geotechnical Engineering-Monitoring, Modelling and Simulation)

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