Submit to Sensors Review for Sensors Propose a Special Issue

Journal Browser

► Journal Browser

Smart Sensor Networks for Civil Infrastructure Monitoring

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 10471

Share This Special Issue

Special Issue Editors

Dr. Jian Li

E-Mail Website
Guest Editor

Associate Professor, Department of Civil, Environmental and Architectural Engineering, The University of Kansas, Lawrence, KS 66045, USA
Interests: data assimilation in SHM; wireless smart sensor networks; innovative sensing techniques; computer vision; uncertainty quantification; risk assessment and mitigation
Special Issues, Collections and Topics in MDPI journals

Dr. Simon Laflamme

E-Mail Website
Guest Editor

Department of Civil, Construction, and Environmental Engineering, Iowa State University, Ames, IA 50011, USA
Interests: smart systems; smart structures; sensors; structural health monitoring; real-time learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Filippo Ubertini

E-Mail Website
Guest Editor

Department of Civil and Environmental Engineering, University of Perugia, 06125 Perugia, Italy
Interests: smart concretes; smart bricks; bridge SHM; earthquake-induced damage detection; vibration-based SHM
Special Issues, Collections and Topics in MDPI journals

Dr. Hao Wang

E-Mail Website
Guest Editor

School of Civil Engineering, Southeast University, Nanjing 211189, China
Interests: wind engineering; bridge engineering; vibration control; structural health monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Civil infrastructures that make up urban and rural communities, such as bridges, buildings, roads, railways, dams and levees, ports, transmission towers, etc., serve under continuous operational and environmental stresses and, sometimes, extreme hazardous loads, leading to deterioration and damage. Smart sensor networks, wired or wireless, play a critical role in monitoring these civil infrastructures through intelligent sensing, processing, data transmission, and storage to support essential decision makings aimed at detection, diagnosis, and prognosis of infrastructure health conditions.

In recent years, we have witnessed significant advancements in the development of smart sensor networks for infrastructure monitoring. Examples include smart sensor networks that integrate a variety of advanced sensing technologies to achieve more direct monitoring of structural deterioration with simplified data processing, ones that are able to respond to transient events, and those developed to facilitate easy deployment and maintenance by practicing engineers, among many others. In the meantime, through the continued development of traditional model-based and model-free methods, as well as the emerging machine learning and big data analytics, innovative algorithms have been created to improve our ability to extract useful information from the data collected by smart sensor networks.

The objective of this Special Issue is to generate discussions on the latest advances in research on smart sensor networks for civil infrastructure health monitoring, algorithm development, and related applications. Research works that include real-world smart sensor network applications are especially welcome. Topics of interest include, but are not limited to:

Novel-sensor-empowered sensor networks;
Intelligent networking and operations;
Signal processing and algorithms for damage diagnosis and health prognosis;
Smart SHM system integration;
Control-oriented smart sensor networks;
Adaptive sampling and interrogation;
Integration of sensor network data within digital twins;
Dense and very dense sensor networks;
Field demonstration and applications.

Dr. Jian Li
Dr. Simon Laflamme
Prof. Dr. Filippo Ubertini
Dr. Hao Wang
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

Smart sensor networks
Smart sensors
Structural health monitoring
Intelligent systems
Condition assessment

Published Papers (4 papers)

Download All Papers

Research

22 pages, 12082 KiB

Open AccessArticle

Structural Health Monitoring of Fatigue Cracks for Steel Bridges with Wireless Large-Area Strain Sensors

by Sdiq Anwar Taher, Jian Li, Jong-Hyun Jeong, Simon Laflamme, Hongki Jo, Caroline Bennett, William N. Collins and Austin R. J. Downey

Sensors 2022, 22(14), 5076; https://0-doi-org.brum.beds.ac.uk/10.3390/s22145076 - 06 Jul 2022

Cited by 9 | Viewed by 3558

Abstract

This paper presents a field implementation of the structural health monitoring (SHM) of fatigue cracks for steel bridge structures. Steel bridges experience fatigue cracks under repetitive traffic loading, which pose great threats to their structural integrity and can lead to catastrophic failures. Currently, accurate and reliable fatigue crack monitoring for the safety assessment of bridges is still a difficult task. On the other hand, wireless smart sensors have achieved great success in global SHM by enabling long-term modal identifications of civil structures. However, long-term field monitoring of localized damage such as fatigue cracks has been limited due to the lack of effective sensors and the associated algorithms specifically designed for fatigue crack monitoring. To fill this gap, this paper proposes a wireless large-area strain sensor (WLASS) to measure large-area strain fatigue cracks and develops an effective algorithm to process the measured large-area strain data into actionable information. The proposed WLASS consists of a soft elastomeric capacitor (SEC) used to measure large-area structural surface strain, a capacitive sensor board to convert the signal from SEC to a measurable change in voltage, and a commercial wireless smart sensor platform for triggered-based wireless data acquisition, remote data retrieval, and cloud storage. Meanwhile, the developed algorithm for fatigue crack monitoring processes the data obtained from the WLASS under traffic loading through three automated steps, including (1) traffic event detection, (2) time-frequency analysis using a generalized Morse wavelet (GM-CWT) and peak identification, and (3) a modified crack growth index (CGI) that tracks potential fatigue crack growth. The developed WLASS and the algorithm present a complete system for long-term fatigue crack monitoring in the field. The effectiveness of the proposed time-frequency analysis algorithm based on GM-CWT to reliably extract the impulsive traffic events is validated using a numerical investigation. Subsequently, the developed WLASS and algorithm are validated through a field deployment on a steel highway bridge in Kansas City, KS, USA. Full article

(This article belongs to the Special Issue Smart Sensor Networks for Civil Infrastructure Monitoring)

► Show Figures

Figure 1

14 pages, 2523 KiB

Open AccessArticle

Learning Damage Representations with Sequence-to-Sequence Models

by Qun Yang and Dejian Shen

Sensors 2022, 22(2), 452; https://0-doi-org.brum.beds.ac.uk/10.3390/s22020452 - 07 Jan 2022

Cited by 2 | Viewed by 1489

Abstract

Natural hazards have caused damages to structures and economic losses worldwide. Post-hazard responses require accurate and fast damage detection and assessment. In many studies, the development of data-driven damage detection within the research community of structural health monitoring has emerged due to the advances in deep learning models. Most data-driven models for damage detection focus on classifying different damage states and hence damage states cannot be effectively quantified. To address such a deficiency in data-driven damage detection, we propose a sequence-to-sequence (Seq2Seq) model to quantify a probability of damage. The model was trained to learn damage representations with only undamaged signals and then quantify the probability of damage by feeding damaged signals into models. We tested the validity of our proposed Seq2Seq model with a signal dataset which was collected from a two-story timber building subjected to shake table tests. Our results show that our Seq2Seq model has a strong capability of distinguishing damage representations and quantifying the probability of damage in terms of highlighting the regions of interest. Full article

(This article belongs to the Special Issue Smart Sensor Networks for Civil Infrastructure Monitoring)

► Show Figures

Figure 1

16 pages, 3424 KiB

Open AccessArticle

Flaw Detection in Highly Scattering Materials Using a Simple Ultrasonic Sensor Employing Adaptive Template Matching

by Biao Wu and Yong Huang

Sensors 2022, 22(1), 268; https://0-doi-org.brum.beds.ac.uk/10.3390/s22010268 - 30 Dec 2021

Cited by 2 | Viewed by 1917

Abstract

Ultrasonic sensors have been extensively used in the nondestructive testing of materials for flaw detection. For polycrystalline materials, however, due to the scattering nature of the material, which results in strong grain noise and attenuation of the ultrasonic signal, accurate detection of flaws is particularly difficult. In this paper, a novel flaw-detection method using a simple ultrasonic sensor is proposed by exploiting time-frequency features of an ultrasonic signal. Since grain scattering mostly happens in the Rayleigh scattering region, it is possible to separate grain-scattered noise from flaw echoes in the frequency domain employing their spectral difference. We start with the spectral modeling of grain noise and flaw echo, and how the two spectra evolve with time is established. Then, a time-adaptive spectrum model for flaw echo is proposed, which serves as a template for the flaw-detection procedure. Next, a specially designed similarity measure is proposed, based on which the similarity between the template spectrum and the spectrum of the signal at each time point is evaluated sequentially, producing a series of matching coefficients termed moving window spectrum similarity (MWSS). The time-delay information of flaws is directly indicated by the peaks of MWSSs. Finally, the performance of the proposed method is validated by both simulated and experimental signals, showing satisfactory accuracy and efficiency. Full article

(This article belongs to the Special Issue Smart Sensor Networks for Civil Infrastructure Monitoring)

► Show Figures

Figure 1

14 pages, 32934 KiB

Open AccessArticle

Soft Elastomeric Capacitor for Angular Rotation Sensing in Steel Components

by Han Liu, Simon Laflamme, Jian Li, Caroline Bennett, William N. Collins, Austin Downey, Paul Ziehl and Hongki Jo

Sensors 2021, 21(21), 7017; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217017 - 23 Oct 2021

Cited by 6 | Viewed by 1932

Abstract

The authors have previously proposed corrugated soft elastomeric capacitors (cSEC) to create ultra compliant scalable strain gauges. The cSEC technology has been successfully demonstrated in engineering and biomechanical applications for in-plane strain measurements. This study extends work on the cSEC to evaluate its performance at measuring angular rotation when installed folded at the junction of two plates. The objective is to characterize the sensor’s electromechanical behavior anticipating applications to the monitoring of welded connections in steel components. To do so, an electromechanical model that maps the cSEC signal to bending strain induced by angular rotation is derived and adjusted using a validated finite element model. Given the difficulty in mapping strain measurements to rotation, an algorithm termed angular rotation index (ARI) is formulated to link measurements to angular rotation directly. Experimental work is conducted on a hollow structural section (HSS) steel specimen equipped with cSECs subjected to compression to generate angular rotations at the corners within the cross-section. Results confirm that the cSEC is capable of tracking angular rotation-induced bending strain linearly, however with accuracy levels significantly lower than found over flat configurations. Nevertheless, measurements were mapped to angular rotations using the ARI, and it was found that the ARI mapped linearly to the angle of rotation, with an accuracy of 0.416