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

Deadline for manuscript submissions: 28 February 2023 | Viewed by 13129

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Special Issue Editors

Prof.Dr. Nicola Giaquinto
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Guest Editor

Department of Electrical and Information Engineering (DEI), Politecnico di Bari, Bari, Italy
Interests: sensors; measurement uncertainty; signal processing; statistical methods; instrumentation

Prof.Dr. Francesco Adamo
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Guest Editor

Department of Electrical and Information Engineering (DEI), Politecnico di Bari, Bari, Italy
Interests: Measurement Science

Dr. Maurizio Spadavecchia
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Guest Editor

Department of Electrical and Information Engineering, Politecnico di Bari, Bari, Italy
Interests: smart sensors; measurements on power systems; smart grid; wide area measurements; signal processing; distributed measurement system; energy harvesting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Distributed monitoring has progressively gained in popularity given the growing need for continuous measurements of large structures or regions, e.g., cultivated fields, pipelines, tunnels, and viaducts. The output of this kind of monitoring is a distribution of the physical quantity of the itme of interest (like temperature, strain, moisture, etc.) along the entire structure, or the detection and location of anomalous values of the quantity at any point of the structure.

There are basically two ways to perform distributed monitoring. The first is distributed sensing, which uses cable-like elements (e.g. ,optical fibers) sensitive at every point along their length. The second is distributed sensor networks, which use a large number of sensor nodes with wired or wireless communication to obtain the desired measurements.

This Special Issue is addressed to both types of distributed monitoring. A quality contribution should illustrate a particularly effective solution using one of the two methods and highlights the validity of the proposed methodology for general problems or for a specific application. The reason for preferring either menthod in the discussed case should be outlined.

Prof. Nicola Giaquinto
Prof. Francesco Adamo
Guest Editors

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Keywords

Distributed sensing
Reflectometric techniques
Sensors networks
Sensor swarms
IoT measurements

Published Papers (10 papers)

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Research

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Open AccessArticle

Pavement Distress Estimation via Signal on Graph Processing

and

Sensors 2022, 22(23), 9183; https://doi.org/10.3390/s22239183 - 25 Nov 2022

Viewed by 417

Abstract

A comprehensive representation of the road pavement state of health is of great interest. In recent years, automated data collection and processing technology has been used for pavement inspection. In this paper, a new signal on graph (SoG) model of road pavement distresses is presented with the aim of improving automatic pavement distress detection systems. A novel nonlinear Bayesian estimator in recovering distress metrics is also derived. The performance of the methodology was evaluated on a large dataset of pavement distress values collected in field tests conducted in Kazakhstan. The application of the proposed methodology is effective in recovering acquisition errors, improving road failure detection. Moreover, the output of the Bayesian estimator can be used to identify sections where the measurement acquired by the 3D laser technology is unreliable. Therefore, the presented model could be used to schedule road section maintenance in a better way. Full article

(This article belongs to the Special Issue Sensors for Distributed Monitoring)

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Open AccessArticle

Split Ring Resonator Network and Diffused Sensing Element Embedded in a Concrete Beam for Structural Health Monitoring

and

Sensors 2022, 22(17), 6398; https://doi.org/10.3390/s22176398 - 25 Aug 2022

Cited by 1 | Viewed by 535

Abstract

The aim of this work is to propose two different and integrated sensors for the structural health monitoring of concrete beams. In particular, a diffused sensing element and a split ring resonator network are presented. The first sensor is able to detect the variations in the dielectric properties of the concrete along the whole beam length, for a diffuse monitoring both during the important concrete curing phase and also for the entire life cycle of the concrete beams. The resonators instead work punctually, in their surroundings, allowing an accurate evaluation of the permittivity both during the drying phase and after. This allows the continuous monitoring of any presence of water both inside the concrete beam and at points that can be critical, in the case of beams in dams, bridges or in any case subject to a strong presence of water which could lead to deterioration, or worse, cause serious accidents. Moreover, the punctual sensors are able to detect the presence of cracks in the structure and to localize them. Full article

(This article belongs to the Special Issue Sensors for Distributed Monitoring)

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Open AccessArticle

Quantifying the Surface Strain Field Induced by Active Sources with Distributed Acoustic Sensing: Theory and Practice

and

Sensors 2022, 22(12), 4589; https://doi.org/10.3390/s22124589 - 17 Jun 2022

Viewed by 1238

Abstract

Quantitative dynamic strain measurements of the ground would be useful for engineering scale problems such as monitoring for natural hazards, soil-structure interaction studies, and non-invasive site investigation using full waveform inversion (FWI). Distributed acoustic sensing (DAS), a promising technology for these purposes, needs to be better understood in terms of its directional sensitivity, spatial position, and amplitude for application to engineering-scale problems. This study investigates whether the physical measurements made using DAS are consistent with the theoretical transfer function, reception patterns, and experimental measurements of ground strain made by geophones. Results show that DAS and geophone measurements are consistent in both phase and amplitude for broadband (10 s of Hz), high amplitude (10 s of microstrain), and complex wavefields originating from different positions around the array when: (1) the DAS channels and geophone locations are properly aligned, (2) the DAS cable provides good deformation coupling to the internal optical fiber, (3) the cable is coupled to the ground through direct burial and compaction, and (4) laser frequency drift is mitigated in the DAS measurements. The transfer function of DAS arrays is presented considering the gauge length, pulse shape, and cable design. The theoretical relationship between DAS-measured and pointwise strain for vertical and horizontal active sources is introduced using 3D elastic finite-difference simulations. The implications of using DAS strain measurements are discussed including directionality and magnitude differences between the actual and DAS-measured strain fields. Estimating measurement quality based on the wavelength-to-gauge length ratio for field data is demonstrated. A method for spatially aligning the DAS channels with the geophone locations at tolerances less than the spatial resolution of a DAS system is proposed. Full article

(This article belongs to the Special Issue Sensors for Distributed Monitoring)

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Open AccessArticle

Solid-Phase Reference Baths for Fiber-Optic Distributed Sensing

and

Sensors 2022, 22(11), 4244; https://doi.org/10.3390/s22114244 - 02 Jun 2022

Viewed by 754

Abstract

Observations from Raman backscatter-based Fiber-Optic Distributed Sensing (FODS) require reference sections of the fiber-optic cable sensor of known temperature to translate the primary measured intensities of Stokes and anti-Stokes photons to the secondary desired temperature signal, which also commonly forms the basis for other derived quantities. Here, we present the design and the results from laboratory and field evaluations of a novel Solid-Phase Bath (SoPhaB) using ultrafine copper instead of the traditional mechanically stirred liquid-phase water bath. This novel type is suitable for all FODS applications in geosciences and industry when high accuracy and precision are needed. The SoPhaB fully encloses the fiber-optic cable which is coiled around the inner core and surrounded by tightly interlocking parts with a total weight of 22 kg. The SoPhaB is thermoelectrically heated and/or cooled using Peltier elements to control the copper body temperature within ±0.04 K using commercially available electronic components. It features two built-in reference platinum wire thermometers which can be connected to the distributed temperature sensing instrument and/or external measurement and logging devices. The SoPhaB is enclosed in an insulated carrying case, which limits the heat loss to or gains from the outside environment and allows for mobile applications. For thermally stationary outside conditions the measured spatial temperature differences across SoPhaB parts touching the fiber-optic cable are <0.05 K even for stark contrasting temperatures of

Δ T >

40 K between the SoPhaB’s setpoint and outside conditions. The uniform, stationary known temperature of the SoPhaB allows for substantially shorter sections of the fiber-optic cable sensors of less than <5 bins at spatial measurement resolution to achieve an even much reduced calibration bias and spatiotemporal uncertainty compared to traditional water baths. Field evaluations include deployments in contrasting environments including the Arctic polar night as well as peak summertime conditions to showcase the wide range of the SoPhaB’s applicability. Full article

(This article belongs to the Special Issue Sensors for Distributed Monitoring)

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Open AccessArticle

Low-Cost and High-Performance Solution for Positioning and Monitoring of Large Structures

and

Rosario Schiano Lo Moriello

Sensors 2022, 22(5), 1788; https://doi.org/10.3390/s22051788 - 24 Feb 2022

Cited by 2 | Viewed by 886

Abstract

Systems for accurate attitude and position monitoring of large structures, such as bridges, tunnels, and offshore platforms are changing in recent years thanks to the exploitation of sensors based on Micro-ElectroMechanical Systems (MEMS) as an Inertial Measurement Unit (IMU). Currently adopted solutions are, in fact, mainly based on fiber optic sensors (characterized by high performance in attitude estimation to the detriment of relevant costs large volumes and heavy weights) and integrated with a Global Position System (GPS) capable of providing low-frequency or single-update information about the position. To provide a cost-effective alternative and overcome the limitations in terms of dimensions and position update frequency, a suitable solution and a corresponding prototype, exhibiting performance very close to those of the traditional solutions, are presented and described hereinafter. The solution leverages a real-time Kalman filter that, along with the proper features of the MEMS inertial sensor and Real-Time Kinematic (RTK) GPS, allows achieving performance in terms of attitude and position estimates suitable for this kind of application. The results obtained in a number of tests underline the promising reliability and effectiveness of the solution in estimating the attitude and position of large structures. In particular, several tests carried out in the laboratory highlighted high system stability; standard deviations of attitude estimates as low as 0.04° were, in fact, experienced in tests conducted in static conditions. Moreover, the prototype performance was also compared with a fiber optic sensor in tests emulating actual operating conditions; differences in the order of a few hundredths of a degree were found in the attitude measurements. Full article

(This article belongs to the Special Issue Sensors for Distributed Monitoring)

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Open AccessArticle

Detection and Characterization of Multiple Discontinuities in Cables with Time-Domain Reflectometry and Convolutional Neural Networks

Marco Scarpetta

Maurizio Spadavecchia

Francesco Adamo

Mattia Alessandro Ragolia

and

Nicola Giaquinto

Sensors 2021, 21(23), 8032; https://doi.org/10.3390/s21238032 - 01 Dec 2021

Cited by 6 | Viewed by 1117

Abstract

In this paper, a convolutional neural network for the detection and characterization of impedance discontinuity points in cables is presented. The neural network analyzes time-domain reflectometry signals and produces a set of estimated discontinuity points, each of them characterized by a class describing the type of discontinuity, a position, and a value quantifying the entity of the impedance discontinuity. The neural network was trained using a great number of simulated signals, obtained with a transmission line simulator. The transmission line model used in simulations was calibrated using data obtained from stepped-frequency waveform reflectometry measurements, following a novel procedure presented in the paper. After the training process, the neural network model was tested on both simulated signals and measured signals, and its detection and accuracy performances were assessed. In experimental tests, where the discontinuity points were capacitive faults, the proposed method was able to correctly identify 100% of the discontinuity points, and to estimate their position and entity with a root-mean-squared error of 13 cm and 14 pF, respectively. Full article

(This article belongs to the Special Issue Sensors for Distributed Monitoring)

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Open AccessArticle

Image Processing Technique for Improving the Sensitivity of Mechanical Register Water Meters to Very Small Leaks

Marco Carratù

Salvatore Dello Iacono

Giuseppe Di Leo

Consolatina Liguori

and

Antonio Pietrosanto

Sensors 2021, 21(21), 7251; https://doi.org/10.3390/s21217251 - 30 Oct 2021

Cited by 1 | Viewed by 1053

Abstract

Discovering very small water leaks at the household level is one of the most challenging goals of smart metering. While many solutions for sudden leakage detection have been proposed to date, the small leaks are still giving researchers a hard time. Even if some devices can be found on the market, their capability to detect a water leakage barely reaches the sensitivity of the employed mechanical water meter, which was not designed for detecting small water leakages. This paper proposes a technique for improving the sensitivity of the mechanical register water meters. By implementing this technique in a suitable electronic add-on device, the improved sensitivity could detect very small leaks. This add-on device continuously acquires the mechanical register’s digital images and, thanks to suitable image processing techniques and metrics, allows very small flows to be detected even if lower than the meter starting flow rate. Experimental tests were performed on two types of mechanical water meters, multijet and piston, whose starting flow rates are 8 L/h and 1 L/h, respectively. Results were very interesting in the leakage range of [1.0, 10.0] L/h for the multijet and even in the range [0.25, 1.00] L/h for the piston meter. Full article

(This article belongs to the Special Issue Sensors for Distributed Monitoring)

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Open AccessArticle

Long-Term Performance of Distributed Optical Fiber Sensors Embedded in Reinforced Concrete Beams under Sustained Deflection and Cyclic Loading

Ignasi Fernandez

Carlos G. Berrocal

and

Rasmus Rempling

Sensors 2021, 21(19), 6338; https://doi.org/10.3390/s21196338 - 22 Sep 2021

Cited by 5 | Viewed by 1149

Abstract

This paper explores the performance of distributed optical fiber sensors based on Rayleigh backscattering for the monitoring of strains in reinforced concrete elements subjected to different types of long-term external loading. In particular, the reliability and accuracy of robust fiber optic cables with an inner steel tube and an external protective polymeric cladding were investigated through a series of laboratory experiments involving large-scale reinforced concrete beams subjected to either sustained deflection or cyclic loading for 96 days. The unmatched spatial resolution of the strain measurements provided by the sensors allows for a level of detail that leads to new insights in the understanding of the structural behavior of reinforced concrete specimens. Moreover, the accuracy and stability of the sensors enabled the monitoring of subtle strain variations, both in the short-term due to changes of the external load and in the long-term due to time-dependent effects such as creep. Moreover, a comparison with Digital Image Correlation measurements revealed that the strain measurements and the calculation of deflection and crack widths derived thereof remain accurate over time. Therefore, the study concluded that this type of fiber optic has great potential to be used in real long-term monitoring applications in reinforced concrete structures. Full article

(This article belongs to the Special Issue Sensors for Distributed Monitoring)

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Review

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Open AccessReview

LiDAR-Based Structural Health Monitoring: Applications in Civil Infrastructure Systems

Elise Kaartinen

Kyle Dunphy

and

Ayan Sadhu

Sensors 2022, 22(12), 4610; https://doi.org/10.3390/s22124610 - 18 Jun 2022

Cited by 9 | Viewed by 1709

Abstract

As innovative technologies emerge, extensive research has been undertaken to develop new structural health monitoring procedures. The current methods, involving on-site visual inspections, have proven to be costly, time-consuming, labor-intensive, and highly subjective for assessing the safety and integrity of civil infrastructures. Mobile and stationary LiDAR (Light Detection and Ranging) devices have significant potential for damage detection, as the scans provide detailed geometric information about the structures being evaluated. This paper reviews the recent developments for LiDAR-based structural health monitoring, in particular, for detecting cracks, deformation, defects, or changes to structures over time. In this regard, mobile laser scanning (MLS) and terrestrial laser scanning (TLS), specific to structural health monitoring, were reviewed for a wide range of civil infrastructure systems, including bridges, roads and pavements, tunnels and arch structures, post-disaster reconnaissance, historical and heritage structures, roofs, and retaining walls. Finally, the existing limitations and future research directions of LiDAR technology for structural health monitoring are discussed in detail. Full article

(This article belongs to the Special Issue Sensors for Distributed Monitoring)

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Open AccessReview

Road Condition Monitoring Using Smart Sensing and Artificial Intelligence: A Review

Eshta Ranyal

Ayan Sadhu

and

Kamal Jain

Sensors 2022, 22(8), 3044; https://doi.org/10.3390/s22083044 - 15 Apr 2022

Cited by 16 | Viewed by 3167

Abstract

Road condition monitoring (RCM) has been a demanding strategic research area in maintaining a large network of transport infrastructures. With advancements in computer vision and data mining techniques along with high computing resources, several innovative pavement distress evaluation systems have been developed in recent years. The majority of these technologies employ next-generation distributed sensors and vision-based artificial intelligence (AI) methodologies to evaluate, classify and localize pavement distresses using the measured data. This paper presents an exhaustive and systematic literature review of these technologies in RCM that have been published from 2017–2022 by utilizing next-generation sensors, including contact and noncontact measurements. The various methodologies and innovative contributions of the existing literature reviewed in this paper, together with their limitations, promise a futuristic insight for researchers and transport infrastructure owners. The decisive role played by smart sensors and data acquisition platforms, such as smartphones, drones, vehicles integrated with non-intrusive sensors, such as RGB, and thermal cameras, lasers and GPR sensors in the performance of the system are also highlighted. In addition to sensing, a discussion on the prevalent challenges in the development of AI technologies as well as potential areas for further exploration paves the way for an all-inclusive and well-directed futuristic research on RCM. Full article

(This article belongs to the Special Issue Sensors for Distributed Monitoring)

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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Miniaturized sensors network for the environmental measurement in cultural heritage
Authors: Livio D'Alvia, Eduardo Palermo, Zaccaria Del Prete
Affiliation: Department of Mechanical and Aerospace Engineering University of Rome
Abstract: In the last years, environmental monitoring has represented a challenging research topic thanks to its significant impact on different applications, including cultural heritage conservation. Over the past decade, both technical and technological limits related to the bulky and costly traditional fixed-site stations for monitoring have limited their applicability in the cultural heritage field. Nowadays, wireless sensors networks play an increasingly central role thanks to their low cost, small size, and high portability. Valuable results have been achieved through electrochemical cells and digital or Analog sensors for a wide range of parameters. This study aims to provide information regarding current knowledge about miniaturized sensors networks in cultural heritage. A rational review was performed – to identify, screen, select and categorize the available technologies for environmental monitoring. Finally, we comparatively examine the performance of the different sensors their applicability in the cultural heritage field and provide an analysis of the advantages and disadvantages of each solution. The selected articles showed that sensor networks are particularly versatile can be used to supply strategy plans, exposure assessments, and risk analysis in a particular field as cultural heritage

Title: Next-generation Sensing and Artificial Intelligence-based Pavement Distress Evaluation: A Systematic Review
Authors: Eshta Ranyal1, Ayan Sadhu2, and Kamal Jain3
Affiliation: 1PhD Student, Department of Civil Engineering, IIT Roorkee, India. 2Corresponding Author: Associate Professor, Department of Civil and Environmental Engineering, Western University, London, ON, Canada, Email: [email protected] 3Professor, Department of Civil Engineering, IIT Roorkee, India.
Abstract: Pavement distress evaluation (PDE) has been a demanding strategic research area supporting a large network of transport infrastructure. With advancements in sensors, computer vision and data mining techniques along with high computing resources, several innovative pavement distress evaluation systems have been developed in recent years. The majority of these technologies employ next-generation distributed sensors and vision-based artificial intelligence (AI) methodologies to evaluate, classify and localize pavement distresses using the measured data. This paper presents, first-ever, an exhaustive and systematic literature review of these technologies in PDE and pavement monitoring by utilizing contact and noncontact sensor data. The decisive role played by smart sensors and data acquisition platforms such as smartphones, drones, vehicles integrated with non-intrusive sensors such as RGB and thermal cameras, laser and GPR sensors in the performance of the system are emphasized. A comparative evaluation of the next-generation sensors gives an insight into the evolution of the entire data acquisition, classification, segmentation, and distress localization process in terms of scalability and employability with AI methodologies. The paper also highlights the limitations of each method and their usability to complement each other’s shortcomings. The various methodologies and innovative contributions of the existing literature reviewed in this paper together with their limitations, promise a futuristic insight to researchers, practitioners, and infrastructure owners. Apart from sensing-based PDE, discussion on prevalent challenges in the development of AI technologies as well as potential areas for further exploration paves the way for an all-inclusive and well-directed futuristic research in PDE.

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