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Advances in Sensors, Monitoring, and Intelligence Techniques for Geotechnical Engineering and Geology

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (30 May 2020) | Viewed by 12023

Special Issue Editors

Prof. Dr. Dieu Tien Bui

E-Mail Website
Guest Editor
Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, Notodden, Norway
Interests: sensors; LiDAR; GIS and geospatial technology; geo-hazards; artificial intelligence; soil engineering; marine geology; environmental managements
Special Issues, Collections and Topics in MDPI journals
Dr. Hossein Moayedi

E-Mail Website
Guest Editor
Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Interests: sensors; geotechnical engineering; landslide assessment; natural hazards; meta-heuristic optimization; machine learning; field monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Viet Ha Nhu

E-Mail Website
Guest Editor
Department of Geological‑Geotechnical Engineering, Hanoi University of Mining and Geology (HUMG), Hanoi, Vietnam
Interests: advanced sensors; geotechnical engineering; geo-hazards; land subsidence; marine geology; coastal engineering; artificial intelligence; data mining
Dr. Paraskevas Tsangaratos

E-Mail Website
Guest Editor
Department of Geological Studies, School of Mining and Metallurgical Engineering, National University of Athens, 15780 Athens, Greece
Interests: natural hazards; water resources; engineering geology; GIS; machine learning; soft computing; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The latest developments in sensors are providing undeniable influence on real-time monitoring techniques employed for geotechnical engineering and geology projects. Technologies such as particle image velocimetry, excavation, transparent soils and computed tomography scanning are now being applied to monitor the behavior of geo-based materials as well as engineering earth structures under different conditions in the field or small-scale laboratories. Computer-based vision methods, LiDAR, and field observation wireless-based sensor networks are just a few examples.

In addition, new satellite and UAV photogrammetric sensors and cameras provide new solutions for generating data of large spatial coverage and very high resolution at the field scale with relatively low cost for geotechnical engineering and geological exploration projects. Moreover, advanced artificial intelligence, metaheuristic optimization, and data-science can be a reliable methods for geotechnical engineering and geology approaches employed for exploration, risk assessment, construction, design, and maintenance at a higher level. This Special Issue invites scholars to share recently developed advances in sensors and intelligent techniques in sensors, monitoring, and intelligence techniques for geotechnical engineering and geology. 

We kindly invite scientists to contribute novel and original research to this Special Issue, attributing at least one of below topics:

  • Advances in sensors and intelligent methods for large-scale geotechnical engineering and geological exploration projects
  • Recent advances in real-time monitoring, early failure systems, and risk assessment
  • Recent advances in artificial intelligence and meta-heuristic optimization algorithms employed after small/large scale laboratory experiments
  • Real-life case studies of applied sensors, geospatial data science, and intelligence techniques in engineering projects with findings of clear interest to the scientific community
  • Finally, the authors are encouraged to share data and codes (if possible) for considering the reproducibility of their works as well as future improvements of research.

Prof. Dr. Dieu Tien Bui
Dr. Hossein Moayedi
Dr. Viet Ha Nhu
Dr. Paraskevas Tsangaratos
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • sensors
  • geotechnical engineering
  • geological exploration
  • geology
  • intelligent techniques
  • management

Published Papers (3 papers)

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Research

20 pages, 14676 KiB  
Article
Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis
by Ying Cao, Kunlong Yin, Chao Zhou and Bayes Ahmed
Sensors 2020, 20(3), 845; https://0-doi-org.brum.beds.ac.uk/10.3390/s20030845 - 05 Feb 2020
Cited by 38 | Viewed by 4129
Abstract
The monitoring and prediction of the landslide groundwater level is a crucial part of landslide early warning systems. In this study, Tangjiao landslide in the Three Gorges Reservoir area (TGRA) in China was taken as a case study. Three groundwater level monitoring sensors [...] Read more.
The monitoring and prediction of the landslide groundwater level is a crucial part of landslide early warning systems. In this study, Tangjiao landslide in the Three Gorges Reservoir area (TGRA) in China was taken as a case study. Three groundwater level monitoring sensors were installed in different locations of the landslide. The monitoring data indicated that the fluctuation of groundwater level is significantly consistent with rainfall and reservoir level in time, but there is a lag. In addition, there is a spatial difference in the impact of reservoir levels on the landslide groundwater level. The data of two monitoring locations were selected for establishing the prediction model of groundwater. Combined with the qualitative and quantitative analysis, the influencing factors were selected, respectively, to establish the hybrid Genetic Algorithm-Support Vector Machine (GA-SVM) prediction model. The single-factor GA-SVM without considering influencing factors and the backpropagation neural network (BPNN) model were adopted to make comparisons. The results showed that the multi-factor GA-SVM performed the best, followed by multi-factor BPNN and single-factor GA-SVM. We found that the prediction accuracy can be improved by considering the influencing factor. The proposed GA-SVM model combines the advantages of each algorithm; it can effectively construct the response relationship between groundwater level fluctuations and influencing factors. Above all, the multi-factor GA-SVM is an effective method for the prediction of landslides groundwater in the TGRA. Full article
(This article belongs to the Special Issue Advances in Sensors, Monitoring, and Intelligence Techniques for Geotechnical Engineering and Geology)
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21 pages, 9742 KiB  
Article
Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Vibration Sensors and Support Vector Regression-Based Optimization Algorithms
by Hoang Nguyen, Yosoon Choi, Xuan-Nam Bui and Trung Nguyen-Thoi
Sensors 2020, 20(1), 132; https://0-doi-org.brum.beds.ac.uk/10.3390/s20010132 - 24 Dec 2019
Cited by 60 | Viewed by 4894
Abstract
In this study, vibration sensors were used to measure blast-induced ground vibration (PPV). Different evolutionary algorithms were assessed for predicting PPV, including the particle swarm optimization (PSO) algorithm, genetic algorithm (GA), imperialist competitive algorithm (ICA), and artificial bee colony (ABC). These evolutionary algorithms [...] Read more.
In this study, vibration sensors were used to measure blast-induced ground vibration (PPV). Different evolutionary algorithms were assessed for predicting PPV, including the particle swarm optimization (PSO) algorithm, genetic algorithm (GA), imperialist competitive algorithm (ICA), and artificial bee colony (ABC). These evolutionary algorithms were used to optimize the support vector regression (SVR) model. They were abbreviated as the PSO-SVR, GA-SVR, ICA-SVR, and ABC-SVR models. For each evolutionary algorithm, three forms of kernel function, linear (L), radial basis function (RBF), and polynomial (P), were investigated and developed. In total, 12 new hybrid models were developed for predicting PPV in this study, named ABC-SVR-P, ABC-SVR-L, ABC-SVR-RBF, PSO-SVR-P, PSO-SVR-L, PSO-SVR-RBF, ICA-SVR-P, ICA-SVR-L, ICA-SVR-RBF, GA-SVR-P, GA-SVR-L and GA-SVR-RBF. There were 125 blasting results gathered and analyzed at a limestone quarry in Vietnam. Statistical criteria like R2, RMSE, and MAE were used to compare and evaluate the developed models. Ranking and color intensity methods were also applied to enable a more complete evaluation. The results revealed that GA was the most dominant evolutionary algorithm for the current problem when combined with the SVR model. The RBF was confirmed as the best kernel function for the GA-SVR model. The GA-SVR-RBF model was proposed as the best technique for PPV estimation. Full article
(This article belongs to the Special Issue Advances in Sensors, Monitoring, and Intelligence Techniques for Geotechnical Engineering and Geology)
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11 pages, 1230 KiB  
Article
Constrained MLAMBDA Method for Multi-GNSS Structural Health Monitoring
by Haiyang Li, Guigen Nie, Dezhong Chen, Shuguang Wu and Kezhi Wang
Sensors 2019, 19(20), 4462; https://0-doi-org.brum.beds.ac.uk/10.3390/s19204462 - 15 Oct 2019
Cited by 8 | Viewed by 1997
Abstract
Deformation monitoring of engineering structures using the advanced Global Navigation Satellite System (GNSS) has attracted research interest due to its high-precision, constant availability and global coverage. However, GNSS application requires precise coordinates of points of interest through quick and reliable resolution of integer [...] Read more.
Deformation monitoring of engineering structures using the advanced Global Navigation Satellite System (GNSS) has attracted research interest due to its high-precision, constant availability and global coverage. However, GNSS application requires precise coordinates of points of interest through quick and reliable resolution of integer ambiguities in carrier phase measurements. Conventional integer ambiguity resolution algorithms have been extensively researched indeed in the past few decades, although the application of GNSS to structural health monitoring is still limited. In particular, known a priori information related to the structure of a body of interest is not normally considered. This study proposes a composite strategy that incorporates modified least-squares ambiguity decorrelation adjustment (MLAMBDA) method with priori information of the structural deformation. Data from the observation sites of Baishazhou Bridge are used to test method performance. Compared to MLAMBDA methods that do not consider priori information, the ambiguity success rate (ASR) improves by 20% for global navigation satellite system (GLONASS) and 10% for Multi-GNSS, while running time is reduced by 60 s for a single system and 180 s for Multi-GNSS system. Experimental results of Teaching Experiment Building indicate that our constrained MLAMBDA method improves positioning accuracy and meets the requirements of structural health monitoring, suggesting that the proposed strategy presents an improved integer ambiguity resolution algorithm. Full article
(This article belongs to the Special Issue Advances in Sensors, Monitoring, and Intelligence Techniques for Geotechnical Engineering and Geology)
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