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Processes
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Intelligent Computational Modeling and Processes Optimization Techniques in Geo-Engineering
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Intelligent Computational Modeling and Processes Optimization Techniques in Geo-Engineering

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Advanced Digital and Other Processes".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 2005

Special Issue Editors

Dr. Abbas Abbaszadeh Shahri

E-Mail Website
Guest Editor
Facurlty of Engineering and Technology, BIU, Madrid, Spain
Interests: data mining (modeling/development); evolutionary algorithms; computer vision; hybrid modeling; complex computational approaches; optimization techniques
Special Issues, Collections and Topics in MDPI journals
Dr. Mahjoub Himi

E-Mail Website
Guest Editor
Department of Mineralogy, Petrology and Applied Geology, Faculty of Earth Science, University of Barcelona, 08028 Barcelona, Spain
Interests: environmental geophysics; optimization; modeling; site selection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In parallel with technical advancements, producing big data in geo-engineering applications/purposes cannot be neglected. AI is closely intertwined with big data to drive analytical and meaningful interpretation. This perspective outlines the usefulness of AI modeling and big data analysis in geo-engineering applications for the purposes of decision making, enhances modeling capabilities, and improves the understanding of complex environmental systems. Accordingly, the reliable interpretation of such big data in comparison with existing models allows furnishing a picture of site formation, rock mass characterization, blasting applications, subsurface rock mass geological modeling, support design, layer identification, and features analysis for a better understanding of the subsurface conditions needed for geo-engineering studies, i.e., underground spaces creations, and developing geotechnical designs and energy resource characterization, such as oil, gas, and geothermal. This implies the considerable importance of such models because they empower researchers and practitioners to explore more effective and sustainable solutions to address the challenges of geo-engineering problems. 

This Special Issue of Processes aims to address critical challenges in geo-engineering applications and pave the way for more efficient and resilient infrastructure projects. The results of such studies then assist the engineers make real adjustments when necessary, thus minimizing risk, e.g., by providing more comprehensive information.

In this point of view, a wide variety of suitable topics exist, such as:

  • Improving design accuracy and efficiency using AI in geo-engineering analysis;
  • Enhanced construction processes, resource allocation, and cost optimization;
  • Advanced prediction and risk assessment of geo-engineering hazards;
  • Optimized ground improvement techniques and foundation design;
  • Real-time monitoring and early warning systems for geo-engineering failures;
  • Big data analysis in geoengineering applications, such as measurement while drilling data;
  • Sensor networks and Internet of Things (IoT) in geospatial analysis and mapping/remote sensing and monitoring/modeling combined geo-data/site characterization;
  • Developing 3D computer vision models for rock mass or geological formations/automated and hybrid modeling;
  • Characterizing data processing methods/normalizing a unification of databases for modeling;
  • Appropriate filtering methods;
  • Sensitivity analysis/model calibration using AI in geo-engineering data;
  • The modeling and scale-up of big data from a geo-engineering perspective.

Dr. Abbas Abbaszadeh Shahri
Dr. Mahjoub Himi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • geo-engineering application
  • big data
  • computer vision
  • data processing
  • geospatial analysis
  • real-time monitoring
  • optimization techniques
  • site characterization

Published Papers (2 papers)

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Research

24 pages, 28675 KiB  
Article
Mine Surface Settlement Prediction Based on Optimized VMD and Multi-Model Combination
by Liyu Shen and Weicai Lv
Processes 2023, 11(12), 3309; https://doi.org/10.3390/pr11123309 - 28 Nov 2023
Viewed by 943
Abstract
The accurate prediction of mining area surface deformation is essential to preventing large-scale coal mining-related surface collapse and ensure safety and daily life continuity. Monitoring subsidence in mining areas is challenged by environmental interference, causing data noise. This paper employs the Sparrow Search [...] Read more.
The accurate prediction of mining area surface deformation is essential to preventing large-scale coal mining-related surface collapse and ensure safety and daily life continuity. Monitoring subsidence in mining areas is challenged by environmental interference, causing data noise. This paper employs the Sparrow Search Algorithm, which integrates Sine Cosine and Cauchy mutation (SCSSA), to optimize variational mode decomposition (VMD) and combine multi-models for prediction. Firstly, SCSSA is employed to adaptively determine the parameters of VMD using envelope entropy as the fitness value. Subsequently, the VMD method optimized using SCSSA adaptively decomposes the original mining area subsidence data sequence into various sub-sequences. Then, SCSSA-VMD is applied to adaptively decompose the original mining subsidence data sequence into multiple sub-sequences. Meanwhile, using sample entropy, the sub-sequences are categorized into trend sequences and fluctuation sequences, and different models are employed to predict sub-sequences at different frequencies. Finally, the prediction results from different sub-sequences are integrated to obtain the final prediction of mining area subsidence. To validate the predictive performance of the established model, experiments are conducted using GNSS monitoring data from the 110801 working face of Banji Coal Mine in Bozhou. The results demonstrate the following: (1) The hybrid model enhanced the prediction accuracy and trends by decomposing the data and optimizing the parameters with VMD. It outperformed single models, reducing errors and improving predictive trends. (2) The hybrid model significantly improved the prediction accuracy for subsidence data at work surface monitoring stations. It is particularly effective at critical subsidence points, making it a valuable reference for safety in mining operations. Full article
(This article belongs to the Special Issue Intelligent Computational Modeling and Processes Optimization Techniques in Geo-Engineering)
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17 pages, 3018 KiB  
Article
A Laboratory-Scale Numerical Investigation of the Effect of Confinement Conditions on the Mechanical Responses of Coal under Various Saturation Conditions
by Huping Wang, Zhao Wang, Sanqing Ding, Chao Jin, Xiaogang Zhang and Langtao Liu
Processes 2023, 11(11), 3224; https://doi.org/10.3390/pr11113224 - 14 Nov 2023
Viewed by 665
Abstract
Deep coal seams are generally preferred for CO2 sequestration, during which the saturation fluids and high-stress condition involved can significantly alter the mechanical attributes of coal. To understand the effect of stress conditions on the mechanical properties of coal during CO2 [...] Read more.
Deep coal seams are generally preferred for CO2 sequestration, during which the saturation fluids and high-stress condition involved can significantly alter the mechanical attributes of coal. To understand the effect of stress conditions on the mechanical properties of coal during CO2 sequestration, a finite element model was developed and subsequently validated using experimental data. The results indicate that coal strength increases from 10.35% for a 5 MPa CO2-saturated sample to 114.54% for an 8 MPa CO2 + water-saturated sample as the confining pressure rises from 0 to 30 MPa, due to reduced porosity. However, this effect diminishes with higher confining pressures as dilation decreases. The critical confining pressure determined in this study is approximately 20 MPa, at which all samples exhibit similar failure strength (around 48.50 MPa). Moreover, the strengthening effect caused by applied stress is especially pronounced in CO2-saturated samples, particularly in those saturated with super-critical CO2 and CO2 + water. This suggests that the reduction in coal strength resulting from the adsorption of saturation fluids can be counterbalanced by the strength gain resulting from applied stress. The aforementioned results highlight the effectiveness of injecting high-pressure super-critical CO2 into deep coal seams for carbon sequestration purposes. Full article
(This article belongs to the Special Issue Intelligent Computational Modeling and Processes Optimization Techniques in Geo-Engineering)
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