Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Fault Detection and Data Analysis for Structure and Infrastructure Engineering
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Fault Detection and Data Analysis for Structure and Infrastructure Engineering

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

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 43857

Special Issue Editors

Prof. Dr. Haitao Yu

E-Mail Website
Guest Editor
College of Civil Engineering, Tongji University, Shanghai 200092, China
Interests: computational mechanics; multiscale method; inspection method; robot; image acquisition/processing/segmentation; target identification; photogrammetry; laser scanning; LiDAR; fault detection; induced seismicity
Prof. Dr. Yiming Zhang

E-Mail Website
Guest Editor
School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin, China
Interests: multifield analysis; continuous–discontinuous methods; neural network; geotechnical structures
Special Issues, Collections and Topics in MDPI journals
Dr. Qing Ai

E-Mail Website
Guest Editor
School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: intelligent inspection technology of infrastructures; data-driven diagnosis method; predictive maintenance; lifetime service performance control; state-oriented maintenance

Special Issue Information

Dear Colleagues,

During the lifetime of structures and infrastructure engineering, faults inevitably occur, which are a great threat to their safety and durability. At present, advanced fault-detecting devices and sensing technologies are booming with the rapid development of emerging technologies, such as smart materials, intelligent construction, Big Data, artificial intelligence (AI), and the Internet of Things (IoT). This provides access to high-quality data for analyzing the mechanism of faults occurring and, thus, their influence on the service performance of structure and infrastructure engineering. It is acknowledged that the fusion of detecting technologies and data analysis will be efficient tools for solving many scientific problems in structure and infrastructure engineering which were either unclear or unsolvable before.

In recent years, research has been connected to various fields, such as advanced sensor technologies, measurement techniques, data-driven computational mechanics of faults, and statistical decision-making algorithms, to improve the safety and durability of structure and infrastructure engineering.

This issue will include papers related to fault detection and data analysis for structure and infrastructure engineering. Original contributions that address both theoretical and experimental issues are welcome, and so are review articles on specific topics within the scope of this issue.

Prof. Dr. Haitao Yu
Prof. Dr. Yiming Zhang
Dr. Qing Ai
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

  • Fault-detecting method for infrastructure (cracks, deformation, leakages, etc.)
  • Innovate sensing technology and devices for infrastructure
  • Structural health monitoring (SHM)
  • Data-driven computing
  • Machine learning
  • Computational methods of modelling faults
  • Fault-induced safety assessment of infrastructure
  • Disaster prevention

Published Papers (21 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

28 pages, 17574 KiB  
Article
Effect of Spraying Polyurea on the Anti-Blast Performance of the Ultra-High Performance Concrete Slab
by Bin Gao, Jun Wu, Qinyi Chen, Jun Yu and Haitao Yu
Sensors 2022, 22(24), 9888; https://doi.org/10.3390/s22249888 - 15 Dec 2022
Cited by 1 | Viewed by 1507
Abstract
Recently, polyurea has been applied to improve the anti-blast performance of metal plates, masonry walls, and concrete structures. However, the strengthening effectiveness of polyurea on ultra-high performance concrete (UHPC) slabs with an overall response is still unclear. Hence, this paper examined the strengthening [...] Read more.
Recently, polyurea has been applied to improve the anti-blast performance of metal plates, masonry walls, and concrete structures. However, the strengthening effectiveness of polyurea on ultra-high performance concrete (UHPC) slabs with an overall response is still unclear. Hence, this paper examined the strengthening effectiveness of polyurea on the anti-blast performance of UHPC slabs under near-field explosion by the finite element (FE) method. First, a benchmark finite element model for UHPC and polyurea-UHPC (PUHPC) slabs under blast loading was established and validated by field blast tests, with scaled distances ranging from 0.4 m/kg1/3 to 0.8 m/kg1/3. After that, parametric analysis was conducted to fully understand the strengthening effectiveness of polyurea on the anti-blast performance of the UHPC slab. Factors including the scaled distance, polyurea thickness, span-to-depth ratio of the slab, and longitudinal reinforcement ratio were considered. The results showed that (1) spraying polyurea on the rear face of the UHPC slab can reduce the width of cracks and mitigate the damage of specimens; (2) the strengthening effectiveness of polyurea on the UHPC slab became prominent when the UHPC slab suffered a larger maximum deflection; (3) in terms of the deflection and energy absorption capacity of PUHPC slabs, the optimum thickness of sprayed polyurea was determined to be 8 mm to 12 mm; and (4) by adopting the multiple nonlinear regression method, a prediction formula was developed to quickly obtain the end rotation of the UHPC slab strengthened with polyurea under near-field explosions. Full article
(This article belongs to the Special Issue Fault Detection and Data Analysis for Structure and Infrastructure Engineering)
►▼ Show Figures

Figure 1

16 pages, 7661 KiB  
Article
Validation of Novel Ultrasonic Phased Array Borehole Probe by Using Simulation and Measurement
by Prathik Prabhakara, Frank Mielentz, Heiko Stolpe, Matthias Behrens, Vera Lay and Ernst Niederleithinger
Sensors 2022, 22(24), 9823; https://doi.org/10.3390/s22249823 - 14 Dec 2022
Cited by 4 | Viewed by 1570
Abstract
Low-frequency ultrasonic testing is a well-established non-destructive testing (NDT) method in civil engineering for material characterization and the localization of cracks, reinforcing bars and delamination. A novel ultrasonic borehole probe is developed for in situ quality assurance of sealing structures in radioactive waste [...] Read more.
Low-frequency ultrasonic testing is a well-established non-destructive testing (NDT) method in civil engineering for material characterization and the localization of cracks, reinforcing bars and delamination. A novel ultrasonic borehole probe is developed for in situ quality assurance of sealing structures in radioactive waste repositories using existing research boreholes. The aim is to examine the sealing structures made of salt concrete for any possible cracks and delamination and to localize built-in components. A prototype has been developed using 12 individual horizontal dry point contact (DPC) shear wave transducers separated by equidistant transmitter/receiver arrays. The probe is equipped with a commercially available portable ultrasonic flaw detector used in the NDT civil engineering industry. To increase the sound pressure generated, the number of transducers in the novel probe is increased to 32 transducers. In addition, the timed excitation of each transducer directs a focused beam of sound to a specific angle and distance based on the previously calculated delay time. This narrows the sensitivity of test volume and improves the signal-to-noise ratio of the received signals. In this paper, the newly designed phased array borehole probe is validated by beam computation in the CIVA software and experimental investigations on a half-cylindrical test specimen to investigate the directional characteristics. In combination with geophysical reconstruction methods, it is expected that an optimised radiation pattern of the probe will improve the signal quality and thus increase the reliability of the imaging results. This is an important consideration for the construction of safe sealing structures for the safe disposal of radioactive or toxic waste. Full article
(This article belongs to the Special Issue Fault Detection and Data Analysis for Structure and Infrastructure Engineering)
►▼ Show Figures

Figure 1

23 pages, 10458 KiB  
Article
Analysis on the Vulnerability of a Tunnel Entrance under Internal Explosion
by Zichao Liu, Jun Wu, Qinyi Chen, Shutao Li, Qiushi Yan and Haitao Yu
Sensors 2022, 22(24), 9727; https://doi.org/10.3390/s22249727 - 12 Dec 2022
Cited by 2 | Viewed by 1664
Abstract
Tunnels play an essential role in the transportation network. Tunnel entrances are usually buried at a shallow depth. In the event of an internal explosion, the blast pressure will cause severe damage or even collapse of the tunnel entrance, paralyzing the traffic system. [...] Read more.
Tunnels play an essential role in the transportation network. Tunnel entrances are usually buried at a shallow depth. In the event of an internal explosion, the blast pressure will cause severe damage or even collapse of the tunnel entrance, paralyzing the traffic system. Therefore, an accurate assessment of the damage level of tunnel entrances under internal blast loading can provide effective assistance for the anti-blast design of tunnels, post-disaster emergency response, and economic damage assessment. In this paper, four tunnel entrance specimens were designed and fabricated with a scale ratio of 1/5.5, and a series of field blast tests were carried out to examine the damage pattern of the tunnel entrances under internal explosion. Subsequently, static loading tests were conducted to obtain the maximum bearing capacity of the intact specimen and residual bearing capacities of the post-blast specimens. After that, an explicit non-linear analysis was carried out and a numerical finite element (FE) model of the tunnel entrance under internal blast loading was established by adopting the arbitrary Lagrangian–Eulerian (ALE) method and validated based on the data obtained from the field blast and static loading tests. A probabilistic vulnerability analysis of a typical tunnel entrance subjected to stochastic internal explosions (assuming various charge weights and detonation points) was then carried out with the validated FE model. For the purpose of damage assessment, the residual bearing capacity of the tunnel entrance was taken as the damage criterion. The vulnerability curves corresponding to various damage levels were further developed based on the stochastic data from the probabilistic vulnerability analysis. When the charge weight was 200 kg, the tunnel entrance exhibited slight or moderate damage, while the tunnel entrance suffered severe or even complete damage as the charge weight increased to 1000 kg. However, the tunnel entrance’s probability of complete damage was less than 10% when the TNT charge weight did not exceed 1000 kg. Full article
(This article belongs to the Special Issue Fault Detection and Data Analysis for Structure and Infrastructure Engineering)
►▼ Show Figures

Figure 1

17 pages, 3603 KiB  
Article
Unstable Object Points during Measurements—Deformation Analysis Based on Pseudo Epoch Approach
by Robert Duchnowski and Patrycja Wyszkowska
Sensors 2022, 22(23), 9030; https://doi.org/10.3390/s22239030 - 22 Nov 2022
Cited by 2 | Viewed by 923
Abstract
Deformation analysis or point movement checking is the basis for monitoring ground or engineering structures. There are several approaches to conducting deformation analysis, which differ from each other in measurement techniques or data processing. Usually, they are based on geodetic observables conducted in [...] Read more.
Deformation analysis or point movement checking is the basis for monitoring ground or engineering structures. There are several approaches to conducting deformation analysis, which differ from each other in measurement techniques or data processing. Usually, they are based on geodetic observables conducted in at least two epochs. As such measurements are not “immediate”, it might so happen that a point (or some points) displaces during measurement within one epoch. The point movements might be continuous or sudden. This study focuses on the latter case, where rockburst, mining damages, or newly formed construction faults might cause displacement. To study this, an observation set consisting of measurements performed before and after point displacements is needed. As the actual observation division stays unknown, this can be called pseudo epochs. Such a hypothetical observation set requires special estimation methods. In this work, we examined Msplit estimation and robust methods. The first approach’s advantage is that it provides two variants of the network point coordinates (before and after point movements), hence showing dynamic changes in the geodetic network. The presented empirical analyses confirm that Msplit estimation is a better choice that results in better and more realistic outcomes. Full article
(This article belongs to the Special Issue Fault Detection and Data Analysis for Structure and Infrastructure Engineering)
►▼ Show Figures

Graphical abstract

16 pages, 2767 KiB  
Article
Analysis of Structural Health Monitoring Data with Correlated Measurement Error by Bayesian System Identification: Theory and Application
by He-Qing Mu, Xin-Xiong Liang, Ji-Hui Shen and Feng-Liang Zhang
Sensors 2022, 22(20), 7981; https://doi.org/10.3390/s22207981 - 19 Oct 2022
Cited by 4 | Viewed by 1356
Abstract
Measurement error is non-negligible and crucial in SHM data analysis. In many applications of SHM, measurement errors are statistically correlated in space and/or in time for data from sensor networks. Existing works solely consider spatial correlation for measurement error. When both spatial and [...] Read more.
Measurement error is non-negligible and crucial in SHM data analysis. In many applications of SHM, measurement errors are statistically correlated in space and/or in time for data from sensor networks. Existing works solely consider spatial correlation for measurement error. When both spatial and temporal correlation are considered simultaneously, the existing works collapse, as they do not possess a suitable form describing spatially and temporally correlated measurement error. In order to tackle this burden, this paper generalizes the form of correlated measurement error from spatial correlation only or temporal correlation only to spatial-temporal correlation. A new form of spatial-temporal correlation and the corresponding likelihood function are proposed, and multiple candidate model classes for the measurement error are constructed, including no correlation, spatial correlation, temporal correlation, and the proposed spatial-temporal correlation. Bayesian system identification is conducted to achieve not only the posterior probability density function (PDF) for the model parameters, but also the posterior probability of each candidate model class for selecting the most suitable/plausible model class for the measurement error. Examples are presented with applications to model updating and modal frequency prediction under varying environmental conditions, ensuring the necessity of considering correlated measurement error and the capability of the proposed Bayesian system identification in the uncertainty quantification at the parameter and model levels. Full article
(This article belongs to the Special Issue Fault Detection and Data Analysis for Structure and Infrastructure Engineering)
►▼ Show Figures

Figure 1

16 pages, 5391 KiB  
Article
A Novel Detection and Assessment Method for Operational Defects of Pipe Jacking Tunnel Based on 3D Longitudinal Deformation Curve: A Case Study
by Wei Lin, Pan Li and Xiongyao Xie
Sensors 2022, 22(19), 7648; https://doi.org/10.3390/s22197648 - 09 Oct 2022
Cited by 4 | Viewed by 1500
Abstract
Adjacent tunnel construction and environmental disturbances can lead to longitudinal deformation in pipe-jacking tunnels. The longitudinal deformation of the tunnel is closely related to the occurrence of joint dislocation, joint opening, and other defects. In view of the difficulty of obtaining 3D longitudinal [...] Read more.
Adjacent tunnel construction and environmental disturbances can lead to longitudinal deformation in pipe-jacking tunnels. The longitudinal deformation of the tunnel is closely related to the occurrence of joint dislocation, joint opening, and other defects. In view of the difficulty of obtaining 3D longitudinal deformation curves, a method is proposed to obtain 3D longitudinal deformation curves based on a large number of 3D point cloud data with high spatial resolution and large spatial dimensions. Combined with the mechanism of defects occurrence, a theoretical basis for tunnel defects assessment based on tunnel longitudinal deformation is proposed. Taking one pipe jacking tunnel as an example, the longitudinal settlement curve and the 3D longitudinal deformation curve are compared. The correlation between the 3D longitudinal deformation curve and defects such as mud leakage, cracks, and differential deformation is illustrated from the perspective of three indexes: deformation amount, bending deformation, and shearing deformation. The accuracy and reliability of the 3D longitudinal deformation curve in tunnel defects detection and assessment are verified. Full article
(This article belongs to the Special Issue Fault Detection and Data Analysis for Structure and Infrastructure Engineering)
►▼ Show Figures

Figure 1

17 pages, 15636 KiB  
Article
Dynamic Warning Method for Structural Health Monitoring Data Based on ARIMA: Case Study of Hong Kong–Zhuhai–Macao Bridge Immersed Tunnel
by Jianzhong Chen, Xinghong Jiang, Yu Yan, Qing Lang, Hui Wang and Qing Ai
Sensors 2022, 22(16), 6185; https://doi.org/10.3390/s22166185 - 18 Aug 2022
Cited by 9 | Viewed by 1892
Abstract
Structural health monitoring (SHM) is gradually replacing traditional manual detection and is becoming a focus of the research devoted to the operation and maintenance of tunnel structures. However, in the face of massive SHM data, the autonomous early warning method is still required [...] Read more.
Structural health monitoring (SHM) is gradually replacing traditional manual detection and is becoming a focus of the research devoted to the operation and maintenance of tunnel structures. However, in the face of massive SHM data, the autonomous early warning method is still required to further reduce the burden of manual analysis. Thus, this study proposed a dynamic warning method for SHM data based on ARIMA and applied it to the concrete strain data of the Hong Kong–Zhuhai–Macao Bridge (HZMB) immersed tunnel. First, wavelet threshold denoising was applied to filter noise from the SHM data. Then, the feasibility and accuracy of establishing an ARIMA model were verified, and it was adopted to predict future time series of SHM data. After that, an anomaly detection scheme was proposed based on the dynamic model and dynamic threshold value, which set the confidence interval of detected anomalies based on the statistical characteristics of the historical series. Finally, a hierarchical warning system was defined to classify anomalies according to their detection threshold and enable hierarchical treatments. The illustrative example of the HZMB immersed tunnel verified that a three-level (5.5 σ, 6.5 σ, and 7.5 σ) dynamic warning schematic can give good results of anomalies detection and greatly improves the efficiency of SHM data management of the tunnel. Full article
(This article belongs to the Special Issue Fault Detection and Data Analysis for Structure and Infrastructure Engineering)
►▼ Show Figures

Figure 1

23 pages, 6439 KiB  
Article
An Optimization Method for the Station Layout of a Microseismic Monitoring System in Underground Mine Engineering
by Zilong Zhou, Congcong Zhao and Yinghua Huang
Sensors 2022, 22(13), 4775; https://doi.org/10.3390/s22134775 - 24 Jun 2022
Cited by 3 | Viewed by 1492
Abstract
The layout of microseismic monitoring (MSM) station networks is very important to ensure the effectiveness of source location inversion; however, it is difficult to meet the complexity and mobility requirements of the technology in this new era. This paper proposes a network optimization [...] Read more.
The layout of microseismic monitoring (MSM) station networks is very important to ensure the effectiveness of source location inversion; however, it is difficult to meet the complexity and mobility requirements of the technology in this new era. This paper proposes a network optimization method based on the geometric parameters of the proposed sensor-point database. First, according to the monitoring requirements and mine-working conditions, the overall proposed point database and model are built. Second, through the developed model, the proposed coverage area, envelope volume, effective coverage radius, and minimum energy level induction value are comprehensively calculated, and the evaluation reference index is constructed. Third, the effective maximum envelope volume is determined by taking the analyzed limit of monitoring induction energy level as the limit. Finally, the optimal design method is identified and applied to provide a sensor station layout network with the maximum energy efficiency. The method, defined as the S-V-E-R-V model, is verified by a comparison with the existing layout scheme and numerical simulation. The results show that the optimization method has strong practicability and efficiency, compared with the mine’s layout following the current method. Simulation experiments show that the optimization effect of this method meets the mine’s engineering requirements for the variability, intelligence, and high efficiency of the microseismic monitoring station network layout, and satisfies the needs of event identification and location dependent on the station network. Full article
(This article belongs to the Special Issue Fault Detection and Data Analysis for Structure and Infrastructure Engineering)
►▼ Show Figures

Figure 1

22 pages, 8793 KiB  
Article
Scanning of Bridge Surface Roughness from Two-Axle Vehicle Response by EKF-UI and Contact Residual: Theoretical Study
by Y. B. Yang, Baoquan Wang, Zhilu Wang, Kang Shi and Hao Xu
Sensors 2022, 22(9), 3410; https://doi.org/10.3390/s22093410 - 29 Apr 2022
Cited by 12 | Viewed by 2067
Abstract
The scanning of bridge surface roughness by the test vehicle is a coupled and non-stationary problem since the bridge deflection caused by vehicles will inevitably enter into the vehicle response. To this end, a two-step procedure is proposed to retrieve the bridge surface [...] Read more.
The scanning of bridge surface roughness by the test vehicle is a coupled and non-stationary problem since the bridge deflection caused by vehicles will inevitably enter into the vehicle response. To this end, a two-step procedure is proposed to retrieve the bridge surface profile from the noise-contaminated responses of a two-axle vehicle moving over bridges. Central to this is the elimination of the bridge deflection from the estimated unknown input to the test vehicle system. First, the extended Kalman filter with unknown inputs (EKF-UI) algorithm is extended to formulating the state-space equations for the moving vehicle over the bridge. Analytical recursive solutions are derived for the improved vehicle states and the unknown input vector consisting of the vehicle–bridge contact displacement and surface profile. Second, the correlation between the cumulated contact residuals and contact displacements for the two axles is approximately defined by using the vehicle’s parameters and location on the bridge. Then, the surface profile is retrieved from the unknown input by removing the roughness-free contact (bridge) displacement, calculated with no prior knowledge of bridge properties. The efficacy of the proposed procedure was validated by the finite element method and demonstrated in the parametric study for various properties of the system. It is confirmed that the retrieved bridge surface profile is in excellent agreement with the original (assumed). For practical use, the vehicle is suggested to run at a not-too-high speed or in a too noisy environment. The proposed technique is robust with regard to vehicle mass and bridge damping. Full article
(This article belongs to the Special Issue Fault Detection and Data Analysis for Structure and Infrastructure Engineering)
►▼ Show Figures

Figure 1

16 pages, 6964 KiB  
Article
SMART SKY EYE System for Preliminary Structural Safety Assessment of Buildings Using Unmanned Aerial Vehicles
by Jaehoon Bae, Jonghoon Lee, Arum Jang, Young K. Ju and Min Jae Park
Sensors 2022, 22(7), 2762; https://doi.org/10.3390/s22072762 - 03 Apr 2022
Cited by 12 | Viewed by 2862
Abstract
The development of unmanned aerial vehicles (UAVs) is expected to become one of the most commercialized research areas in the world over the next decade. Globally, unmanned aircraft have been increasingly used for safety surveillance in the construction industry and civil engineering fields. [...] Read more.
The development of unmanned aerial vehicles (UAVs) is expected to become one of the most commercialized research areas in the world over the next decade. Globally, unmanned aircraft have been increasingly used for safety surveillance in the construction industry and civil engineering fields. This paper presents an aerial image-based approach using UAVs to inspect cracks and deformations in buildings. A state-of-the-art safety evaluation method termed SMART SKY EYE (Smart building safety assessment system using UAV) is introduced; this system utilizes an unmanned airplane equipped with a thermal camera and programmed with various surveying efficiency improvement methods, such as thermography, machine-learning algorithms, and 3D point cloud modeling. Using this method, crack maps, crack depths, and the deformations of structures can be obtained. Error rates are compared between the proposed and conventional methods. Full article
(This article belongs to the Special Issue Fault Detection and Data Analysis for Structure and Infrastructure Engineering)
►▼ Show Figures

Figure 1

25 pages, 6582 KiB  
Article
Structural Health Monitoring of Dams Based on Acoustic Monitoring, Deep Neural Networks, Fuzzy Logic and a CUSUM Control Algorithm
by Luan Carlos de Sena Monteiro Ozelim, Lucas Parreira de Faria Borges, André Luís Brasil Cavalcante, Enzo Aldo Cunha Albuquerque, Mariana dos Santos Diniz, Manuelle Santos Góis, Katherin Rocio Cano Bezerra da Costa, Patrícia Figuereido de Sousa, Ana Paola do Nascimento Dantas, Rafael Mendes Jorge, Gabriela Rodrigues Moreira, Matheus Lima de Barros and Fernando Rodrigo de Aquino
Sensors 2022, 22(7), 2482; https://doi.org/10.3390/s22072482 - 24 Mar 2022
Cited by 8 | Viewed by 3483
Abstract
Internal erosion is the most important failure mechanism of earth and rockfill dams. Since this type of erosion develops internally and silently, methodologies of data acquisition and processing for dam monitoring are crucial to guarantee a safe operation during the lifespan of these [...] Read more.
Internal erosion is the most important failure mechanism of earth and rockfill dams. Since this type of erosion develops internally and silently, methodologies of data acquisition and processing for dam monitoring are crucial to guarantee a safe operation during the lifespan of these structures. In this context, artificial intelligence techniques show up as tools that can simplify the analysis and verification process not of the internal erosion itself, but of the effects that this pathology causes in the response of the dam to external stimuli. Therefore, within the scope of this paper, a methodological framework for monitoring internal erosion in the body of earth and rockfill dams will be proposed. For that, artificial intelligence methods, especially deep neural autoencoders, will be used to treat the acoustic data collected by geophones installed on a dam. The sensor data is processed to identify patterns and anomalies as well as to classify the dam’s structural health status. In short, the acoustic dataset is preprocessed to reduce its dimensionality. In this process, for each second of acquired data, three parameters are calculated (Hjorth parameters). For each parameter, the data from all the available sensors are used to calibrate an autoencoder. Then, the reconstruction error of each autoencoder is used to monitor how far from the original (normal) state the acoustic signature of the dam is. The time series of reconstruction errors are combined with a cumulative sum (CUSUM) algorithm, which indicates changes in the sequential data collected. Additionally, the outputs of the CUSUM algorithms are treated by a fuzzy logic framework to predict the status of the structure. A scale model is built and monitored to check the effectiveness of the methodology hereby developed, showing that the existence of anomalies is promptly detected by the algorithm. The framework introduced in the present paper aims to detect internal erosion inside dams by combining different techniques in a novel context and methodological workflow. Therefore, this paper seeks to close gaps in prior studies, which mostly treated just parts of the data acquisition–processing workflow. Full article
(This article belongs to the Special Issue Fault Detection and Data Analysis for Structure and Infrastructure Engineering)
►▼ Show Figures

Figure 1

23 pages, 7105 KiB  
Article
Identification Method for Internal Forces of Segmental Tunnel Linings via the Combination of Laser Scanning and Hybrid Structural Analysis
by Yumeng Zhang, Jurij Karlovšek and Xian Liu
Sensors 2022, 22(6), 2421; https://doi.org/10.3390/s22062421 - 21 Mar 2022
Cited by 4 | Viewed by 2115
Abstract
This paper provides a new solution to identify the internal forces of segmental tunnel linings by combining laser scanning and hybrid structural analysis. First, a hybrid structural analysis method for quantifying the internal forces based on displacement monitoring is established, which requires comprehensive [...] Read more.
This paper provides a new solution to identify the internal forces of segmental tunnel linings by combining laser scanning and hybrid structural analysis. First, a hybrid structural analysis method for quantifying the internal forces based on displacement monitoring is established, which requires comprehensive displacement monitoring with high precision and a complete trace history. Motivated by the development of laser scanning, two remedial solutions are proposed for typically insufficient engineering conditions, i.e., lack of displacement developing process and poor accuracy of measurements, which is highlighted in this paper. Therefore, with the help of remedial solutions, the structural analysis is able to be adopted with the application of laser scanning. The tool for developing remedial solutions is the first-order theory of slender circular arches. Virtual tests, based on a calibrated finite element model, were performed to verify the feasibility of the presented hybrid analysis and remedial solutions. In addition, parametric analyses were conducted to study the error propagation from laser scanning to the results of hybrid analysis. The resolution and measurement noise of laser scanning were investigated and discussed. On this basis, advice on combining laser scanning and hybrid structural analysis is proposed. Finally, on-site application of the hybrid analysis on an actual tunnel is presented and discussed. Full article
(This article belongs to the Special Issue Fault Detection and Data Analysis for Structure and Infrastructure Engineering)
►▼ Show Figures

Figure 1

21 pages, 53239 KiB  
Article
ANN and LEFM-Based Fatigue Reliability Analysis and Truck Weight Limits of Steel Bridges after Crack Detection
by Lei Nie, Wei Wang, Lu Deng and Wei He
Sensors 2022, 22(4), 1580; https://doi.org/10.3390/s22041580 - 17 Feb 2022
Cited by 4 | Viewed by 2040
Abstract
Fatigue of steel bridges is a major concern for bridge engineers. Previous fatigue-based weight-limiting method of steel bridges is founded on the Palmgren–Miner’s rule and S-N curves, which overlook the effect of existing cracks on the fatigue life of in-service steel bridges. In [...] Read more.
Fatigue of steel bridges is a major concern for bridge engineers. Previous fatigue-based weight-limiting method of steel bridges is founded on the Palmgren–Miner’s rule and S-N curves, which overlook the effect of existing cracks on the fatigue life of in-service steel bridges. In the present study, based on the theory of linear elastic fracture mechanics, a framework combining the artificial neural networks and Monte Carlo simulations is proposed to analyze the fatigue reliability of steel bridges with the effects of cracks and truck weight limits considered. Using the framework, a new method of setting the gross vehicle weight limit for in-service steel bridges with cracks is proposed. The influences of four key parameters, including the average daily truck traffic, the gross vehicle weight limit, the violation rate, and the detected crack size, on the fatigue reliability of a steel bridge are analyzed quantitatively with the new framework. Results show that the suggested framework can enhance the fatigue reliability assessment process in terms of accuracy and efficiency. The method of setting gross vehicle weight limits can effectively control the fatigue failure probability to be within 2.3% according to the desired remaining service time and the detected crack size. Full article
(This article belongs to the Special Issue Fault Detection and Data Analysis for Structure and Infrastructure Engineering)
►▼ Show Figures

Figure 1

18 pages, 7752 KiB  
Article
Study on Mechanical Characteristics of Segmental Joints of a Large-Diameter Shield Tunnel under Ultrahigh Water Pressure
by Lei Kou, Zhihui Xiong, Hao Cui and Jinjie Zhao
Sensors 2021, 21(24), 8392; https://doi.org/10.3390/s21248392 - 16 Dec 2021
Cited by 4 | Viewed by 2450
Abstract
At present, there is no clear design standard for segmental joints of large-diameter shield tunnels under high water pressure. In this paper, a theoretical calculation model for the bending stiffness of segmental joints under high water pressure is proposed. The numerical simulation method [...] Read more.
At present, there is no clear design standard for segmental joints of large-diameter shield tunnels under high water pressure. In this paper, a theoretical calculation model for the bending stiffness of segmental joints under high water pressure is proposed. The numerical simulation method is used to investigate the failure and crack formation processes of single-layer and double-layer lining segments under large axial forces. The effects of axial force, bolt strength, and concrete strength on the bending stiffness of joints are then studied using a theoretical calculation model of segmental joints. The results show that under extremely high water pressure, the influence of double lining on joint stiffness is limited. It is more rational and safe to compute the bending stiffness of segmental joints using this theoretical model rather than the numerical simulation method. The parameter analysis reveals that increasing the bolt strength has a minor impact on bending stiffness and deformation, whereas increasing the concrete strength has the opposite effect. The influence of ultimate bearing capacity and deformation decreases non-linearly as the axial force increases. Full article
(This article belongs to the Special Issue Fault Detection and Data Analysis for Structure and Infrastructure Engineering)
►▼ Show Figures

Figure 1

15 pages, 4645 KiB  
Article
A Scheme with Acoustic Emission Hit Removal for the Remaining Useful Life Prediction of Concrete Structures
by Tuan-Khai Nguyen, Zahoor Ahmad and Jong-Myon Kim
Sensors 2021, 21(22), 7761; https://doi.org/10.3390/s21227761 - 22 Nov 2021
Cited by 10 | Viewed by 1797
Abstract
In this study, a scheme of remaining useful lifetime (RUL) prognosis from raw acoustic emission (AE) data is presented to predict the concrete structure’s failure before its occurrence, thus possibly prolong its service life and minimizing the risk of accidental damage. The deterioration [...] Read more.
In this study, a scheme of remaining useful lifetime (RUL) prognosis from raw acoustic emission (AE) data is presented to predict the concrete structure’s failure before its occurrence, thus possibly prolong its service life and minimizing the risk of accidental damage. The deterioration process is portrayed by the health indicator (HI), which is automatically constructed from raw AE data with a deep neural network pretrained and fine-tuned by a stacked autoencoder deep neural network (SAE-DNN). For the deep neural network structure to perform a more accurate construction of health indicator lines, a hit removal process with a one-class support vector machine (OC-SVM), which has not been investigated in previous studies, is proposed to extract only the hits which matter the most to the portrait of deterioration. The new set of hits is then harnessed as the training labels for the deep neural network. After the completion of the health indicator line construction, health indicators are forwarded to a long short-term memory recurrent neural network (LSTM-RNN) for the training and validation of the remaining useful life prediction, as this structure is capable of capturing the long-term dependencies, even with a limited set of data. Our prediction result shows a significant improvement in comparison with a similar scheme but without the hit removal process and other methods, such as the gated recurrent unit recurrent neural network (GRU-RNN) and the simple recurrent neural network. Full article
(This article belongs to the Special Issue Fault Detection and Data Analysis for Structure and Infrastructure Engineering)
►▼ Show Figures

Figure 1

25 pages, 8085 KiB  
Article
Theoretical, Numerical, and Experimental Study on the Identification of Subway Tunnel Structural Damage Based on the Moving Train Dynamic Response
by Hongqiao Li, Xiongyao Xie, Yonglai Zhang and Qiang Wang
Sensors 2021, 21(21), 7197; https://doi.org/10.3390/s21217197 - 29 Oct 2021
Cited by 6 | Viewed by 1892
Abstract
As an important part of urban rail transit, subway tunnels play an important role in alleviating traffic pressure in mega-cities. Identifying and locating damage to the tunnel structure as early as possible has important practical significance for maintaining the long-term safe operation of [...] Read more.
As an important part of urban rail transit, subway tunnels play an important role in alleviating traffic pressure in mega-cities. Identifying and locating damage to the tunnel structure as early as possible has important practical significance for maintaining the long-term safe operation of subway tunnels. Summarizing the current status and shortcomings of the structural health monitoring of subway tunnels, a very economical and effective monitoring program is proposed, which is to use the train vibration response to identify and locate the damage of the tunnel structure. Firstly, the control equation of vehicle–tunnel coupling vibration is established and its analytical solution is given as the theoretical basis of this paper. Then, a damage index based on the cumulative sum of wavelet packet energy change rate (TDISC) is proposed, and its process algorithm is given. Through the joint simulation of VI-Rail and ANSYS, a refined 3D train-tunnel coupled vibration model is established. In this model, different combined conditions of single damage and double damage verify the validity of the damage index. The effectiveness of this damage index was further verified through model tests, and the influence of vehicle speed and load on the algorithm was discussed. Numerical simulation and experimental results show that the TDISC can effectively locate the damage of the tunnel structure and has good robustness. Full article
(This article belongs to the Special Issue Fault Detection and Data Analysis for Structure and Infrastructure Engineering)
►▼ Show Figures

Figure 1

20 pages, 5163 KiB  
Article
Quality Evaluation of Rock Mass Using RMR14 Based on Multi-Source Data Fusion
by Qi Zhang, Qing Jiang, Yuanhai Li, Ning Wang and Lei He
Sensors 2021, 21(21), 7108; https://doi.org/10.3390/s21217108 - 26 Oct 2021
Cited by 1 | Viewed by 1481
Abstract
The uncertainties in quality evaluations of rock mass are embedded in the underlying multi-source data composed by a variety of testing methods and some specialized sensors. To mitigate this issue, a proper method of data-driven computing for quality evaluation of rock mass based [...] Read more.
The uncertainties in quality evaluations of rock mass are embedded in the underlying multi-source data composed by a variety of testing methods and some specialized sensors. To mitigate this issue, a proper method of data-driven computing for quality evaluation of rock mass based on the theory of multi-source data fusion is required. As the theory of multi-source data fusion, Dempster–Shafer (D-S) evidence theory is applied to the quality evaluation of rock mass. As the correlation between different rock mass indices is too large to be ignored, belief reinforcement and Murphy’s average belief theory are introduced to process the multi-source data of rock mass. The proposed method is designed based on RMR14, one of the most widely used quality-evaluating methods for rock mass in the world. To validate the proposed method, the data of rock mass is generated randomly to realize the data fusion based on the proposed method and the conventional D-S theory. The fusion results based on these two methods are compared. The result of the comparison shows the proposed method amplifies the distance between the possibilities at different ratings from 0.0666 to 0.5882, which makes the exact decision more accurate than the other. A case study is carried out in Daxiagu tunnel in China to prove the practical value of the proposed method. The result shows the rock mass rating of the studied section of the tunnel is in level III with the maximum possibility of 0.9838, which agrees with the geological survey report. Full article
(This article belongs to the Special Issue Fault Detection and Data Analysis for Structure and Infrastructure Engineering)
►▼ Show Figures

Figure 1

32 pages, 16551 KiB  
Article
Methods of Hidden Periodicity Discovering for Gearbox Fault Detection
by Ihor Javorskyj, Ivan Matsko, Roman Yuzefovych, Oleh Lychak and Roman Lys
Sensors 2021, 21(18), 6138; https://doi.org/10.3390/s21186138 - 13 Sep 2021
Cited by 12 | Viewed by 1804
Abstract
It is shown that the models of gear pair vibration, proposed in literature, are particular cases of the bi-periodically correlated random processes (BPCRPs), which describe its stochastic recurrence with two periods. The possibility of vibration and analysis within the framework of BPCRP approximation, [...] Read more.
It is shown that the models of gear pair vibration, proposed in literature, are particular cases of the bi-periodically correlated random processes (BPCRPs), which describe its stochastic recurrence with two periods. The possibility of vibration and analysis within the framework of BPCRP approximation, in the form of periodically correlated random processes (PCRPs), is grounded and the implementation of vibration processing procedures using PCRP techniques, which are worked out by the authors, is given. Searching for hidden periodicities of the first and the second orders was considered as the main issue of this approach. The estimation of the non-stationary period (basic frequency) allowed us to carry out a detailed analysis of the deterministic part, the covariance structure of the stochastic part, and to form, using their parameters, the sensitive indicators for fault detection. The results of the processing of the wind turbine gearbox vibration signals are presented. The amplitude spectra of the deterministic oscillations and the time changes of the stochastic part power for different fault stages are analyzed. The most efficient indicators, which are formed using the amplitude spectra for practical applications, are proposed. The presented approach was compared with known in literature cyclostationary analysis and envelope techniques, and its advantages are shown. Full article
(This article belongs to the Special Issue Fault Detection and Data Analysis for Structure and Infrastructure Engineering)
►▼ Show Figures

Figure 1

24 pages, 9201 KiB  
Article
Site Investigations of the Lacustrine Clay in Taihu Lake, China, Using Self-Boring Pressuremeter Test
by Bin Wang, Kang Liu, Yong Wang and Quan Jiang
Sensors 2021, 21(18), 6026; https://doi.org/10.3390/s21186026 - 09 Sep 2021
Cited by 1 | Viewed by 1947
Abstract
Site investigations of the soils are considered very important for evaluation of the site conditions, as well as the design and construction for the project built in it. Taihu tunnel is thus far the longest tunnel constructed in the lake in China, with [...] Read more.
Site investigations of the soils are considered very important for evaluation of the site conditions, as well as the design and construction for the project built in it. Taihu tunnel is thus far the longest tunnel constructed in the lake in China, with an entire length of over 10 km. However, due to the very insufficient site data obtained for the lacustrine clay in the Taihu lake area, a series of self-boring pressuremeter (SBPM) field tests was therefore carried out. Undrained shear strengths were deduced from the SBPM test, with the results showing generally higher than those obtained from the laboratory tests, which may be attributed to the disturbance to the soil mass during the sampling process. Degradation characteristics of the soil shear modulus (Gs) were mainly investigated, via a thorough comparison between different soil layers, and generally, the shear modulus would cease its decreasing trends and become stable when the shear strain reaches over 1%. Meanwhile, it was found that a linear relationship between the plasticity index and the shear modulus, and between the decay rate of the shear modulus and the plasticity index as well, could be developed. Further statistical analysis over the undrained shear strength and shear modulus distribution of the soils shows that the undrained shear strength of the soils follows a normal distribution, while the shear modulus follows a log-normal distribution. More importantly, the spatial correlation length of the shear modulus is found much smaller than that of the undrained strength. Full article
(This article belongs to the Special Issue Fault Detection and Data Analysis for Structure and Infrastructure Engineering)
►▼ Show Figures

Figure 1

24 pages, 13026 KiB  
Article
A Novel Approach to Automated 3D Spalling Defects Inspection in Railway Tunnel Linings Using Laser Intensity and Depth Information
by Mingliang Zhou, Wen Cheng, Hongwei Huang and Jiayao Chen
Sensors 2021, 21(17), 5725; https://doi.org/10.3390/s21175725 - 25 Aug 2021
Cited by 26 | Viewed by 3238
Abstract
The detection of concrete spalling is critical for tunnel inspectors to assess structural risks and guarantee the daily operation of the railway tunnel. However, traditional spalling detection methods mostly rely on visual inspection or camera images taken manually, which are inefficient and unreliable. [...] Read more.
The detection of concrete spalling is critical for tunnel inspectors to assess structural risks and guarantee the daily operation of the railway tunnel. However, traditional spalling detection methods mostly rely on visual inspection or camera images taken manually, which are inefficient and unreliable. In this study, an integrated approach based on laser intensity and depth features is proposed for the automated detection and quantification of concrete spalling. The Railway Tunnel Spalling Defects (RTSD) database, containing intensity images and depth images of the tunnel linings, is established via mobile laser scanning (MLS), and the Spalling Intensity Depurator Network (SIDNet) model is proposed for automatic extraction of the concrete spalling features. The proposed model is trained, validated and tested on the established RSTD dataset with impressive results. Comparison with several other spalling detection models shows that the proposed model performs better in terms of various indicators such as MPA (0.985) and MIoU (0.925). The extra depth information obtained from MLS allows for the accurate evaluation of the volume of detected spalling defects, which is beyond the reach of traditional methods. In addition, a triangulation mesh method is implemented to reconstruct the 3D tunnel lining model and visualize the 3D inspection results. As a result, a 3D inspection report can be outputted automatically containing quantified spalling defect information along with relevant spatial coordinates. The proposed approach has been conducted on several railway tunnels in Yunnan province, China and the experimental results have proved its validity and feasibility. Full article
(This article belongs to the Special Issue Fault Detection and Data Analysis for Structure and Infrastructure Engineering)
►▼ Show Figures

Figure 1

15 pages, 4195 KiB  
Article
The Effect of Chemical Corrosion on Mechanics and Failure Behaviour of Limestone Containing a Single Kinked Fissure
by Yulin Wu, Qianqian Dong and Jian He
Sensors 2021, 21(16), 5641; https://doi.org/10.3390/s21165641 - 21 Aug 2021
Cited by 2 | Viewed by 1775
Abstract
This study concerns the influence of chemical corrosion and geometric parameters on the macroscopic damage characteristics of brittle limestone containing a kinked fissure under uniaxial compression. The specimens are prepared in chemical solutions with different NaCl concentrations and pH values. The acoustic emission [...] Read more.
This study concerns the influence of chemical corrosion and geometric parameters on the macroscopic damage characteristics of brittle limestone containing a kinked fissure under uniaxial compression. The specimens are prepared in chemical solutions with different NaCl concentrations and pH values. The acoustic emission (AE) technique is adopted to detect the inner distortion of the failure behaviour. The physical process of the crack coalescence of kinked fissures is synchronously captured by a high-speed camera. Seven failure patterns are identified based on the final failure mode and the failure process. Furthermore, the stress intensity factor of kinked cracks under chemical corrosion is obtained by a theoretical analysis. Chemical erosion with an acidic solution has a relatively strong effect on the compressive strength of the tested specimen, while the initial crack angle is not affected by short-term chemical corrosion. Full article
(This article belongs to the Special Issue Fault Detection and Data Analysis for Structure and Infrastructure Engineering)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-21
Back to TopTop