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Intelligent Sensing Technologies in Structural Health Monitoring

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 22741

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Special Issue Editors

Prof. Dr. Xiangyang Xu

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Guest Editor

School of Rail Transit, Soochow University, Suzhou 215006, China
Interests: finite element analysis; structural health monitoring; tunnel structures; deformation analysis; underground space; damage detection; vision-based measurement
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hao Yang

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Guest Editor

School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China
Interests: intelligent monitoring; visual measurement; machine learning; structural health monitoring; AI
Special Issues, Collections and Topics in MDPI journals

Dr. Yi Zhang

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Guest Editor

Department of Civil Engineering, Tsinghua University, Beijing 100084, China
Interests: applications of reliability analysis to engineering design; applications of risk theory to urban infrastructure condition assessment; application of economic evaluation and loss estimation for infrastructure system; developing digitalization techniques for urban system planning

Dr. Vittorio Memmolo

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Guest Editor

Department of Industrial Engineering, University of Naples “Federico II”, Via Claudio 21, 80125 Naples, Italy
Interests: structural health monitoring; nondestructive testing; load monitoring; smart structures; ultrasounds and material testing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent sensing technologies have been widely adopted in the field of structural health monitoring of various constructions, providing adequate information of objects and powerful computational and analytical methods for the monitoring of tunnels, bridges, subways, and many other kinds of constructions. Different scientific communities have paid great attention to this topic because it promotes a deep understanding of structure monitoring and, thus, plays an important role in the development of intelligent monitoring technologies.

This Special Issue aims to synthesize the state of the art in intelligent sensing, multisensor measurements, artificial intelligence, structural health monitoring, laser-based measurements, machine vision, deep learning, and deformation analyses. We hope to showcase the impact of the intelligent monitoring of modern constructions and strengthen the academic exchange in the field of intelligent monitoring methods and applications.

Research articles regarding new developments in intelligent monitoring with respect to the theoretical, computational, models, experiments, and methods, as well as their applications in the engineering fields will be considered.

We encourage submissions on a broad range of issues, including, but not limited to:

Intelligent sensing;
Multisensor measurement;
Structural health monitoring;
Deformation analysis;
Artificial intelligence;
Laser-based measurement;
Machine vision.

Prof. Dr. Xiangyang Xu
Prof. Dr. Hao Yang
Dr. Yi Zhang
Dr. Vittorio Memmolo
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.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

intelligent sensing
multisensor measurement
structural health monitoring
artificial intelligence
laser-based measurement
machine vision
deformation analysis

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Published Papers (11 papers)

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Research

24 pages, 9966 KiB

Open AccessArticle

Enhancing Sensing Performance of Capacitive Sensors Using Kirigami Structures

by Chor-Kheng Lim

Sensors 2024, 24(21), 6930; https://doi.org/10.3390/s24216930 - 29 Oct 2024

Viewed by 557

Abstract

Capacitive sensors have widespread applications in human-machine interaction, Internet of Things, and smart home systems due to their low cost, high sensitivity, and ease of integration. However, improving the sensitivity and sensing distance of capacitive sensors remains a challenging issue. This study proposes a novel capacitive sensor design method based on Kirigami structures, which enhances sensor performance by introducing specific cutting patterns into the conductive layer to leverage edge effects. Through experimental testing and statistical analysis, we systematically investigated the influence of Kirigami geometric parameters on sensor sensitivity and sensing distance. We designed and fabricated 12 different Kirigami structures, including circular flower patterns, array patterns, layered pointed flower patterns, and circular strip structures, and compared them with traditional non-cut structures. The results show that Kirigami structures significantly improved sensor performance. Compared to traditional sensors without Kirigami structures, optimally designed Kirigami capacitive sensors demonstrated approximately a 3-fold increase in sensitivity and up to 170 percent extension in sensing distance. Multivariate regression analysis and nonlinear models revealed complex relationships between Kirigami structural parameters and sensor performance. Notably, the circular strip (three-layer) structure exhibited the best performance, possibly due to its maximization of edge effects and optimization of electric field distribution. This study provides new design insights for developing high-performance capacitive sensors, with potential applications in improving smart home systems and indoor activity monitoring for solitary elderly individuals. Full article

(This article belongs to the Special Issue Intelligent Sensing Technologies in Structural Health Monitoring)

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19 pages, 6915 KiB

Open AccessArticle

Automated Crack Detection in Monolithic Zirconia Crowns Using Acoustic Emission and Deep Learning Techniques

by Kuson Tuntiwong, Supan Tungjitkusolmun and Pattarapong Phasukkit

Sensors 2024, 24(17), 5682; https://doi.org/10.3390/s24175682 - 31 Aug 2024

Viewed by 984

Abstract

Monolithic zirconia (MZ) crowns are widely utilized in dental restorations, particularly for substantial tooth structure loss. Inspection, tactile, and radiographic examinations can be time-consuming and error-prone, which may delay diagnosis. Consequently, an objective, automatic, and reliable process is required for identifying dental crown defects. This study aimed to explore the potential of transforming acoustic emission (AE) signals to continuous wavelet transform (CWT), combined with Conventional Neural Network (CNN) to assist in crack detection. A new CNN image segmentation model, based on multi-class semantic segmentation using Inception-ResNet-v2, was developed. Real-time detection of AE signals under loads, which induce cracking, provided significant insights into crack formation in MZ crowns. Pencil lead breaking (PLB) was used to simulate crack propagation. The CWT and CNN models were used to automate the crack classification process. The Inception-ResNet-v2 architecture with transfer learning categorized the cracks in MZ crowns into five groups: labial, palatal, incisal, left, and right. After 2000 epochs, with a learning rate of 0.0001, the model achieved an accuracy of 99.4667%, demonstrating that deep learning significantly improved the localization of cracks in MZ crowns. This development can potentially aid dentists in clinical decision-making by facilitating the early detection and prevention of crack failures. Full article

(This article belongs to the Special Issue Intelligent Sensing Technologies in Structural Health Monitoring)

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16 pages, 5021 KiB

Open AccessArticle

An Adaptive Radon-Transform-Based Marker Detection and Localization Method for Displacement Measurements Using Unmanned Aerial Vehicles

by Jianlin Liu, Wujiao Dai, Yunsheng Zhang, Lei Xing and Deyong Pan

Sensors 2024, 24(6), 1930; https://doi.org/10.3390/s24061930 - 18 Mar 2024

Viewed by 1039

Abstract

UAVs have been widely used in deformation monitoring because of their high availability and flexibility. However, the quality of UAV images is affected by changing attitude and surveying environments, resulting in a low monitoring accuracy. Cross-shaped markers are used to improve the accuracy of UAV monitoring due to their distinct center contrast and absence of eccentricity. However, existing methods cannot rapidly and precisely detect these markers in UAV images. To address these problems, this paper proposes an adaptive Radon-transform-based marker detection and localization method for UAV displacement measurements, focusing on two critical detection parameters, namely, the radius of marker information acquisition and the edge width of the cross-shaped scoring template. The experimental results show that the marker detection rate is 97.2% under different combinations of flight altitudes, radius ratios of marker information acquisition, and marker sizes. Furthermore, the root mean square error of detection and localization is 0.57 pixels, significantly surpassing the performance and accuracy of other methods. We also derive the critical detection radius and appropriate parameter combinations for different heights to further improve the practicality of the method. Full article

(This article belongs to the Special Issue Intelligent Sensing Technologies in Structural Health Monitoring)

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12 pages, 2296 KiB

Open AccessArticle

Structural Health Monitoring of Adhesively Bonded Pipe-to-Socket Joints by Integration of Polymer Optical Fibers and Their Load-Dependent Transmission Properties

by Josef Weiland, Billy Kunze, Michael Luber, Naomi Krüger, Alexander Schiebahn, Rainer Engelbrecht and Uwe Reisgen

Sensors 2023, 23(10), 4748; https://doi.org/10.3390/s23104748 - 14 May 2023

Viewed by 1879

Abstract

Adhesively bonded pipe-to-socket joints are used in numerous industrial applications. One example is in the transport of media, e.g., in the gas industry or in structural joints for such sectors as construction, wind energy, and the vehicle industry. To monitor such load-transmitting bonded joints, this study investigates a method based on the integration of polymer optical fibers into the adhesive layer. Previous methods for monitoring the condition of pipes, such as acoustic or ultrasonic methods or the use of glass fiber optic-based sensors (FBG or OTDR), are very complex in methodology and require cost-intensive (opto-) electronic devices to generate and evaluate the sensor signals; they are therefore unsuitable for large-scale use. The method investigated in this paper is based on the measurement of integral optical transmission with a simple photodiode under increasing mechanical stress. When tried at coupon level (single-lap joint), the light coupling was varied to obtain a significant load-dependent sensor signal. Based on an angle-selective coupling of 30° to the fiber axis, a drop of 4% of the optically transmitted light power by a load of 8 N/mm² can be detected for the adhesively bonded pipe-to-socket joint with the structural adhesive Scotch Weld DP810 (2C acrylate). Full article

(This article belongs to the Special Issue Intelligent Sensing Technologies in Structural Health Monitoring)

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16 pages, 4154 KiB

Open AccessArticle

Nonlinear Modeling of Contact Stress Distribution in Thin Plate Substrates Subjected to Aspect Ratio

by Chao Lv, Huixin Wei, Zhiwen Lan and Ping Wu

Sensors 2023, 23(8), 4050; https://doi.org/10.3390/s23084050 - 17 Apr 2023

Viewed by 1418

Abstract

The foundation substrate’s basal contact stresses are typically thought to have a linear distribution, although the actual form is nonlinear. Basal contact stress in thin plates is experimentally measured using a thin film pressure distribution system. This study examines the nonlinear distribution law of basal contact stresses in thin plates with various aspect ratios under concentrated loading, and it establishes a model for the distribution of contact stresses in thin plates using an exponential function that accounts for aspect ratio coefficients. The outcomes demonstrate that the thin plate’s aspect ratio significantly affects how the substrate contact stress is distributed during concentrated loading. The contact stresses in the thin plate’s base exhibit significant nonlinearity when the aspect ratio of the test thin plate is greater than 6~8. The aspect ratio coefficient-added exponential function model can better optimize the strength and stiffness calculations of the base substrate and more accurately describe the actual distribution of contact stresses in the base of the thin plate compared to linear and parabolic functions. The correctness of the exponential function model is confirmed by the film pressure distribution measurement system that directly measures the contact stress at the base of the thin plate, providing a more accurate nonlinear load input for the calculation of the internal force of the base thin plate. Full article

(This article belongs to the Special Issue Intelligent Sensing Technologies in Structural Health Monitoring)

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16 pages, 5122 KiB

Open AccessArticle

A Mitigation Method for Optical-Turbulence-Induced Errors and Optimal Target Design in Vision-Based Displacement Measurement

by Xingyu Huang, Wujiao Dai, Yunsheng Zhang, Lei Xing and Yichao Ye

Sensors 2023, 23(4), 1884; https://doi.org/10.3390/s23041884 - 8 Feb 2023

Viewed by 1680

Abstract

Computer vision-based displacement measurement techniques are increasingly used for structural health monitoring. However, the vision sensors employed are easily affected by optical turbulence when capturing images of the structure, resulting in displacement measurement errors that significantly reduce the accuracy required in engineering applications. Hence, this paper develops a multi-measurement point method to address this problem by mitigating optical-turbulence errors with spatial randomness. Then, the effectiveness of the proposed method in mitigating optical-turbulence errors is verified by static target experiments, in which the RMSE correction rate can reach up to 82%. Meanwhile, the effects of target size and the number of measurement points on the proposed method are evaluated, and the optimal target design criteria are proposed to improve our method’s performance in mitigating optical-turbulence errors under different measurement conditions. Additionally, extensive dynamic target experiments reveal that the proposed method achieves an RMSE correction rate of 69% after mitigating the optical-turbulence error. The experimental results demonstrate that the proposed method improves the visual displacement measurement accuracy and retains the detailed information of the displacement measurement results. Full article

(This article belongs to the Special Issue Intelligent Sensing Technologies in Structural Health Monitoring)

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21 pages, 2751 KiB

Open AccessArticle

Health Monitoring of Serial Structures Applying Piezoelectric Film Sensors and Modal Passport

by Aleksey Mironov, Aleksejs Safonovs, Deniss Mironovs, Pavel Doronkin and Vitalijs Kuzmickis

Sensors 2023, 23(3), 1114; https://doi.org/10.3390/s23031114 - 18 Jan 2023

Cited by 6 | Viewed by 1752

Abstract

Health monitoring of critical structures, that form parts of serial operating objects, is a pressing task. The Operational Modal Analysis (OMA) techniques could be the optimal solution. An inexpensive measurement system, such as the OMA, uses a lot of sensors for structural response assessment. The health monitoring of serial structures has to also consider possible deviations between samples. A solution providing the OMA application includes the compact measurement system based on piezoelectric film sensors and modal passport (MP) techniques. For validation of the proposed approach, a series of five similar composite cylinders, with a network of piezoelectric film sensors, was used. Applying modal tests on the specimens, and using OMA with MP methods, the set of typical modal parameters was determined and analyzed. The results of the study confirmed the feasibility of the sensor network and its applicability for structural health monitoring of serial samples using OMA methods. The proven effectiveness of OMA/MP techniques, combined with a sensor network, provides a prototype of intelligent sensor technology, which can be used for health monitoring of structures, including those that are part of an operating facility. Full article

(This article belongs to the Special Issue Intelligent Sensing Technologies in Structural Health Monitoring)

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27 pages, 4017 KiB

Open AccessArticle

Influence of Noise in Computer-Vision-Based Measurements on Parameter Identification in Structural Dynamics

by Mariusz Ostrowski, Bartlomiej Blachowski, Grzegorz Mikułowski and Łukasz Jankowski

Sensors 2023, 23(1), 291; https://doi.org/10.3390/s23010291 - 27 Dec 2022

Cited by 4 | Viewed by 3066

Abstract

Nowadays, consumer electronics offer computer-vision-based (CV) measurements of dynamic displacements with some trade-offs between sampling frequency, resolution and low cost of the device. This study considers a consumer-grade smartphone camera based on complementary metal-oxide semiconductor (CMOS) technology and investigates the influence of its hardware limitations on the estimation of dynamic displacements, modal parameters and stiffness parameters of bolted connections in a laboratory structure. An algorithm that maximizes the zero-normalized cross-correlation function is employed to extract the dynamic displacements. The modal parameters are identified with the stochastic subspace identification method. The stiffness parameters are identified using a model-updating technique based on modal sensitivities. The results are compared with the corresponding data obtained with accelerometers and a laser distance sensor. The CV measurement allows lower-order vibration modes to be identified with a systematic (bias) error that is nearly proportional to the vibration frequency: from 2% for the first mode (9.4 Hz) to 10% for the third mode (71.4 Hz). However, the measurement errors introduced by the smartphone camera have a significantly lower influence on the values of the identified stiffness parameters than the numbers of modes and parameters taken into account. This is due to the bias–variance trade-off. The results show that consumer-grade electronics can be used as a low-cost and easy-to-use measurement tool if lower-order modes are required. Full article

(This article belongs to the Special Issue Intelligent Sensing Technologies in Structural Health Monitoring)

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22 pages, 6374 KiB

Open AccessArticle

Improving the Accuracy of an R-CNN-Based Crack Identification System Using Different Preprocessing Algorithms

by Mian Zhao, Peixin Shi, Xunqian Xu, Xiangyang Xu, Wei Liu and Hao Yang

Sensors 2022, 22(18), 7089; https://doi.org/10.3390/s22187089 - 19 Sep 2022

Cited by 15 | Viewed by 2997

Abstract

The accurate intelligent identification and detection of road cracks is a key issue in road maintenance, and it has become popular to perform this task through the field of computer vision. In this paper, we proposed a deep learning-based crack detection method that initially uses the idea of image sparse representation and compressed sensing to preprocess the datasets. Only the pixels that represent the crack features remain, while most pixels of non-crack features are relatively sparse, which can significantly improve the accuracy and efficiency of crack identification. The proposed method achieved good results based on the limited datasets of crack images. Various algorithms were tested, namely, linear smooth, median filtering, Gaussian smooth, and grayscale threshold, where the optimal parameters of the various algorithms were analyzed and trained with faster regions with convolutional neural network features (faster R-CNN). The results of the experiments showed that the proposed method has good robustness, with higher detection efficiency in the presence of, for example, road markings, shallow cracks, multiple cracks, and blurring. The result shows that the improvement of mean average precision (mAP) can reach 5% compared with the original method. Full article

(This article belongs to the Special Issue Intelligent Sensing Technologies in Structural Health Monitoring)

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22 pages, 24308 KiB

Open AccessArticle

Dynamic Response of CFRP Reinforced Steel Beams Subjected to Impact Action Based on FBG Sensing Technology

by Hua-Ping Wang, Yi-Bin Wu, Cong Chen, Hu-Yuan Zhang, Hao Jiang, Xue-Mei Zhang and Xiang-Yang Xu

Sensors 2022, 22(17), 6377; https://doi.org/10.3390/s22176377 - 24 Aug 2022

Cited by 2 | Viewed by 1888

Abstract

The in-situ health condition of carbon fiber reinforced polymer (CFRP) reinforced structures has become an important topic, which can reflect the structural performance of the retrofitted structures and judge the design theory. An optical fiber-based structural health monitoring technique is thus suggested. To check the effectiveness of the proposed method, experimental testing on smart CFRP reinforced steel beams under impact action has been performed, and the dynamic response of the structure has been measured by the packaged FBG sensors attached to the surface of the beam and the FBG sensors inserted in the CFRP plates. Time and frequency domain analysis has been conducted to check the structural feature of the structures and the performance of the installed sensors. Results indicate that the packaged Fiber Bragg Grating (FBG) sensors show better sensing performance than the bare FBG sensors in perceiving the impact response of the beam. The sensors embedded in the CFRP plate show good measurement accuracy in sensing the external excitation and can replace the surface-attached FBG sensors. The dynamic performance of the reinforced structures subjected to the impact action can be straightforwardly read from the signals of FBG sensors. The larger impact energies bring about stronger impact signals. Full article

(This article belongs to the Special Issue Intelligent Sensing Technologies in Structural Health Monitoring)

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19 pages, 16898 KiB

Open AccessArticle

Safety Risk Analysis of a New Design of Basalt Fiber Gabion Slope Based on Improved 3D Discrete Element Method and Monitoring Data

by Jianjian Dai, Xiangyang Xu, Hao Yang, Chao Su and Nan Ye

Sensors 2022, 22(10), 3645; https://doi.org/10.3390/s22103645 - 10 May 2022

Cited by 3 | Viewed by 2704

Abstract

Gabion has been extensively used in retaining walls and slope protection. This study carries out a safety risk analysis of a new structure combining basalt fiber reinforcement (BFR) and the traditional gabion structure. The micro-parameters of BFR and soil were calibrated by using the 3D discrete element method after the tensile test of BFR was completed. The mechanical property of the gabion unit was investigated by using a refined model and a numerical test of uniaxial compression. This work developed a simplified method to simulate the seepage effect. The stress condition and sliding displacement between gabions were also investigated. Deformation, stress, and porosity were all used to evaluate the stability of the new type of gabion slope. According to this study, BFR has a tensile strength of 68.22 MPa, and the safety factor increased by 25.68% after using these BFR gabions. The damage is mainly manifested by bending the BFRs and the dislocation of the gabion units, as the slope does not slip. It is indicated this novel gabion structure has a lower safety risk compared to traditional ones, and thus can be popularized and used in retaining walls and slope protection. Full article

(This article belongs to the Special Issue Intelligent Sensing Technologies in Structural Health Monitoring)

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