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MEMS Sensors for Monitoring in Earth Management
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MEMS Sensors for Monitoring in Earth Management

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 20154

Special Issue Editors

Prof. Dr. Rafig Azzam

E-Mail Website
Guest Editor
Rheinisch-Westfälische Technische Hochschule Aachen, 52056 Aachen, Germany
Interests: remedeation and management of brownfields; risk prognosis; environmental geotechnics
Special Issues, Collections and Topics in MDPI journals
Dr. John Singer

E-Mail Website
Co-Guest Editor
AlpGeorisk, 85716 Unterschleißheim, Germany
Interests: hazard management of alpine natural hazards (especially landslides); geotechnical monitoring; geosensor data management and analysis; landslide early warning systems

Special Issue Information

Dear Colleagues,

Advanced wireless sensor networks and MEMS are suitable for application as real-time wireless monitoring systems in earth management. Successful management of large engineering constructions underground or on the ground, as well as geohazards, needs effective monitoring of various parameters depending on the challenge to be addressed and an appropriate safety evaluation method. The reliability of the collected data can be increased by using sensor and data fusion techniques. Data processing and data infrastructure must fit the problem statement. After all, geodata shall be interpreted in terms of geoinformation to be able to address the challenge. In the case of geohazards, it is often important to issue an early warning. This is only possible if the safety status and future development of the acquired data and, thus, geoinformation can be evaluated.

List of topics:

  1. Monitoring in earth management
  2. Smart sensor networks
  3. Strategies for sensor and data fusion
  4. Data processing and data infrastructure
  5. From geodata to geoinformation
  6. From monitoring to early warning

Dr. Rafig Azzam
Dr. John Singer
Guest Editors

Manuscript Submission Information

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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

  • sensor networks
  • geotechnical monitoring
  • early warning
  • sensor fusion
  • data fusion
  • geohazards

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Published Papers (4 papers)

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Research

20 pages, 11800 KiB  
Article
A Technological System for Post-Earthquake Damage Scenarios Based on the Monitoring by Means of an Urban Seismic Network
by Antonio Costanzo, Sergio Falcone, Antonino D’Alessandro, Giovanni Vitale, Sonia Giovinazzi, Michele Morici, Andrea Dall’Asta and Maria Fabrizia Buongiorno
Sensors 2021, 21(23), 7887; https://doi.org/10.3390/s21237887 - 26 Nov 2021
Cited by 11 | Viewed by 2814
Abstract
A technological system capable of automatically producing damage scenarios at an urban scale, as soon as an earthquake occurs, can help the decision-makers in planning the first post-disaster response, i.e., to prioritize the field activities for checking damage, making a building safe, and [...] Read more.
A technological system capable of automatically producing damage scenarios at an urban scale, as soon as an earthquake occurs, can help the decision-makers in planning the first post-disaster response, i.e., to prioritize the field activities for checking damage, making a building safe, and supporting rescue and recovery. This system can be even more useful when it works on densely populated areas, as well as on historic urban centers. In the paper, we propose a processing chain on a GIS platform to generate post-earthquake damage scenarios, which are based: (1) on the near real-time processing of the ground motion, that is recorded in different sites by MEMS accelerometric sensor network in order to take into account the local effects, and (2) the current structural characteristics of the built heritage, that can be managed through an information system from the local public administration authority. In the framework of the EU-funded H2020-ARCH project, the components of the system have been developed for the historic area of Camerino (Italy). Currently, some experimental fragility curves in the scientific literature, which are based on the damage observations after Italian earthquakes, are implemented in the platform. These curves allow relating the acceleration peaks obtained by the recordings of the ground motion with the probability to reach a certain damage level, depending on the structural typology. An operational test of the system was performed with reference to an ML3.3 earthquake that occurred 13 km south of Camerino. Acceleration peaks between 1.3 and 4.5 cm/s2 were recorded by the network, and probabilities lower than 35% for negligible damage (and then about 10% for moderate damage) were calculated for the historical buildings given this low-energy earthquake. Full article
(This article belongs to the Special Issue MEMS Sensors for Monitoring in Earth Management)
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23 pages, 7878 KiB  
Article
Application of Laser-Induced, Deep UV Raman Spectroscopy and Artificial Intelligence in Real-Time Environmental Monitoring—Solutions and First Results
by Claudia Post, Simon Brülisauer, Kryss Waldschläger, William Hug, Luis Grüneis, Niklas Heyden, Sebastian Schmor, Aaron Förderer, Ray Reid, Michael Reid, Rohit Bhartia, Quoc Nguyen, Holger Schüttrumpf and Florian Amann
Sensors 2021, 21(11), 3911; https://doi.org/10.3390/s21113911 - 5 Jun 2021
Cited by 26 | Viewed by 5336
Abstract
Environmental monitoring of aquatic systems is the key requirement for sustainable environmental protection and future drinking water supply. The quality of water resources depends on the effectiveness of water treatment plants to reduce chemical pollutants, such as nitrates, pharmaceuticals, or microplastics. Changes in [...] Read more.
Environmental monitoring of aquatic systems is the key requirement for sustainable environmental protection and future drinking water supply. The quality of water resources depends on the effectiveness of water treatment plants to reduce chemical pollutants, such as nitrates, pharmaceuticals, or microplastics. Changes in water quality can vary rapidly and must be monitored in real-time, enabling immediate action. In this study, we test the feasibility of a deep UV Raman spectrometer for the detection of nitrate/nitrite, selected pharmaceuticals and the most widespread microplastic polymers. Software utilizing artificial intelligence, such as a convolutional neural network, is trained for recognizing typical spectral patterns of individual pollutants, once processed by mathematical filters and machine learning algorithms. The results of an initial experimental study show that nitrates and nitrites can be detected and quantified. The detection of nitrates poses some challenges due to the noise-to-signal ratio and background and related noise due to water or other materials. Selected pharmaceutical substances could be detected via Raman spectroscopy, but not at concentrations in the µg/l or ng/l range. Microplastic particles are non-soluble substances and can be detected and identified, but the measurements suffer from the heterogeneous distribution of the microparticles in flow experiments. Full article
(This article belongs to the Special Issue MEMS Sensors for Monitoring in Earth Management)
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23 pages, 12647 KiB  
Article
Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
by Moritz Gamperl, John Singer and Kurosch Thuro
Sensors 2021, 21(8), 2609; https://doi.org/10.3390/s21082609 - 8 Apr 2021
Cited by 36 | Viewed by 6022
Abstract
Worldwide, cities with mountainous areas struggle with an increasing landslide risk as a consequence of global warming and population growth, especially in low-income informal settlements. Landslide Early Warning Systems (LEWS) are an effective measure to quickly reduce these risks until long-term risk mitigation [...] Read more.
Worldwide, cities with mountainous areas struggle with an increasing landslide risk as a consequence of global warming and population growth, especially in low-income informal settlements. Landslide Early Warning Systems (LEWS) are an effective measure to quickly reduce these risks until long-term risk mitigation measures can be realized. To date however, LEWS have only rarely been implemented in informal settlements due to their high costs and complex operation. Based on modern Internet of Things (IoT) technologies such as micro-electro-mechanical systems (MEMS) sensors and the LoRa (Long Range) communication protocol, the Inform@Risk research project is developing a cost-effective geosensor network specifically designed for use in a LEWS for informal settlements. It is currently being implemented in an informal settlement in the outskirts of Medellin, Colombia for the first time. The system, whose hardware and firmware is open source and can be replicated freely, consists of versatile LoRa sensor nodes which have a set of MEMS sensors (e.g., tilt sensor) on board and can be connected to various different sensors including a newly developed low cost subsurface sensor probe for the detection of ground movements and groundwater level measurements. Complemented with further innovative measurement systems such as the Continuous Shear Monitor (CSM) and a flexible data management and analysis system, the newly developed LEWS offers a good benefit-cost ratio and in the future can hopefully find application in other parts of the world. Full article
(This article belongs to the Special Issue MEMS Sensors for Monitoring in Earth Management)
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18 pages, 6963 KiB  
Article
Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model
by Mei Dong, Hongyu Wu, Hui Hu, Rafig Azzam, Liang Zhang, Zengrong Zheng and Xiaonan Gong
Sensors 2021, 21(1), 14; https://doi.org/10.3390/s21010014 - 22 Dec 2020
Cited by 31 | Viewed by 4749
Abstract
With increased urbanization, accidents related to slope instability are frequently encountered in construction sites. The deformation and failure mechanism of a landslide is a complex dynamic process, which seriously threatens people’s lives and property. Currently, prediction and early warning of a landslide can [...] Read more.
With increased urbanization, accidents related to slope instability are frequently encountered in construction sites. The deformation and failure mechanism of a landslide is a complex dynamic process, which seriously threatens people’s lives and property. Currently, prediction and early warning of a landslide can be effectively performed by using Internet of Things (IoT) technology to monitor the landslide deformation in real time and an artificial intelligence algorithm to predict the deformation trend. However, if a slope failure occurs during the construction period, the builders and decision-makers find it challenging to effectively apply IoT technology to monitor the emergency and assist in proposing treatment measures. Moreover, for projects during operation (e.g., a motorway in a mountainous area), no recognized artificial intelligence algorithm exists that can forecast the deformation of steep slopes using the huge data obtained from monitoring devices. In this context, this paper introduces a real-time wireless monitoring system with multiple sensors for retrieving high-frequency overall data that can describe the deformation feature of steep slopes. The system was installed in the Qili connecting line of a motorway in Zhejiang Province, China, to provide a technical support for the design and implementation of safety solutions for the steep slopes. Most of the devices were retained to monitor the slopes even after construction. The machine learning Probabilistic Forecasting with Autoregressive Recurrent Networks (DeepAR) model based on time series and probabilistic forecasting was introduced into the project to predict the slope displacement. The predictive accuracy of the DeepAR model was verified by the mean absolute error, the root mean square error and the goodness of fit. This study demonstrates that the presented monitoring system and the introduced predictive model had good safety control ability during construction and good prediction accuracy during operation. The proposed approach will be helpful to assess the safety of excavated slopes before constructing new infrastructures. Full article
(This article belongs to the Special Issue MEMS Sensors for Monitoring in Earth Management)
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