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Smart Sensing and Artificial Intelligence for Civil Infrastructure Monitoring and Management

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

Deadline for manuscript submissions: closed (10 July 2023) | Viewed by 34407

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Special Issue Editors

Dr. Yuguang Fu

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Guest Editor

School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: Internet of Things; structural health monitoring; digital twin; artificial intelligence; resilient infrastructure
Special Issues, Collections and Topics in MDPI journals

Dr. Jianxiao Mao

E-Mail Website
Guest Editor

School of Civil Engineering, Southeast University, Nanjing 211189, China
Interests: structural health monitoring; finite element model updating; fatigue behavior of steel and composite bridge
Special Issues, Collections and Topics in MDPI journals

Dr. Peng (Patrick) Sun

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Guest Editor

Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA
Interests: sensor development for structural health monitoring; sensing system development; UAV-based remote sensing; deep learning-based computer vision application
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The monitoring and management of civil infrastructure (e.g., bridges, buildings, tunnels, pipelines, railways, dams) is always an important topic around the world. Smart sensing, accompanied with artificial intelligence (AI), has recently received growing interest for addressing the aforementioned concern. Smart sensing can obtain timely condition or status information about civil infrastructures during their lifecycle, and hence ensure safe construction and efficient operation by providing early warnings of damage or deterioration prior to costly repair or even catastrophic failures.

In recent years, engineers and researchers have witnessed significant advancements in the development of smart sensing technologies, such as wireless smart sensors, mobile sensing, computer vision, and NDT technologies. Their significant potential is further released, with advanced signal processing and data science, which is powered by AI technologies. Meanwhile, these technologies have enabled several full-scale instrumentations of structures under construction or in service. Combined with the collected data stream from these smart sensing systems, researchers take advantage of AI techniques for modelling environmental loadings, detecting construction risks, uncovering the degradation law of structural performance, identifying the surface and interior deficiency of structures, and quickly issuing management instructions.

The focus of this Special Issue is on presenting the latest advances in smart sensing and AI for civil infrastructure monitoring and management. Potential topics include, but are not limited to:

Smart sensor development (hardware or software);
Digital signal processing and filter design;
Sensor fault diagnosis and recovery;
Computer vision and image processing;
Big data management and mining;
Long-term performance monitoring and assessment;
Condition assessment or smart upgrading of infrastructure;
Surface or interior damage detection of structures;
System identification;
Construction monitoring and management;
Vibration mitigation;
Performance improvement of aged infrastructure;
Case studies of smart civil infrastructure systems (e.g., bridges, buildings, and tunnels), etc.

Dr. Yuguang Fu
Dr. Jianxiao Mao
Dr. Peng (Patrick) Sun
Guest Editors

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Keywords

smart sensing
signal processing
artificial intelligence
structural health monitoring
infrastructure inspection
full-scale sensor deployment
full-field deformation sensing
UAV-based sensing application

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Published Papers (12 papers)

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Research

18 pages, 5815 KiB

Open AccessArticle

Damage Detection and Localization of Bridge Deck Pavement Based on Deep Learning

by Youhao Ni, Jianxiao Mao, Yuguang Fu, Hao Wang, Hai Zong and Kun Luo

Sensors 2023, 23(11), 5138; https://doi.org/10.3390/s23115138 - 28 May 2023

Cited by 8 | Viewed by 2757

Abstract

Bridge deck pavement damage has a significant effect on the driving safety and long-term durability of bridges. To achieve the damage detection and localization of bridge deck pavement, a three-stage detection method based on the you-only-look-once version 7 (YOLOv7) network and the revised LaneNet was proposed in this study. In stage 1, the Road Damage Dataset 202 (RDD2022) is preprocessed and adopted to train the YOLOv7 model, and five classes of damage were obtained. In stage 2, the LaneNet network was pruned to retain the semantic segmentation part, with the VGG16 network as an encoder to generate lane line binary images. In stage 3, the lane line binary images were post-processed by a proposed image processing algorithm to obtain the lane area. Based on the damage coordinates from stage 1, the final pavement damage classes and lane localization were obtained. The proposed method was compared and analyzed in the RDD2022 dataset, and was applied on the Fourth Nanjing Yangtze River Bridge in China. The results shows that the mean average precision (mAP) of YOLOv7 on the preprocessed RDD2022 dataset reaches 0.663, higher than that of other models in the YOLO series. The accuracy of the lane localization of the revised LaneNet is 0.933, higher than that of instance segmentation, 0.856. Meanwhile, the inference speed of the revised LaneNet is 12.3 frames per second (FPS) on NVIDIA GeForce RTX 3090, higher than that of instance segmentation 6.53 FPS. The proposed method can provide a reference for the maintenance of bridge deck pavement. Full article

(This article belongs to the Special Issue Smart Sensing and Artificial Intelligence for Civil Infrastructure Monitoring and Management)

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22 pages, 5589 KiB

Open AccessArticle

Machine-Aided Bridge Deck Crack Condition State Assessment Using Artificial Intelligence

by Xin Zhang, Benjamin E. Wogen, Xiaoyu Liu, Lissette Iturburu, Manuel Salmeron, Shirley J. Dyke, Randall Poston and Julio A. Ramirez

Sensors 2023, 23(9), 4192; https://doi.org/10.3390/s23094192 - 22 Apr 2023

Cited by 5 | Viewed by 2417

Abstract

The Federal Highway Administration (FHWA) mandates biannual bridge inspections to assess the condition of all bridges in the United States. These inspections are recorded in the National Bridge Inventory (NBI) and the respective state’s databases to manage, study, and analyze the data. As FHWA specifications become more complex, inspections require more training and field time. Recently, element-level inspections were added, assigning a condition state to each minor element in the bridge. To address this new requirement, a machine-aided bridge inspection method was developed using artificial intelligence (AI) to assist inspectors. The proposed method focuses on the condition state assessment of cracking in reinforced concrete bridge deck elements. The deep learning-based workflow integrated with image classification and semantic segmentation methods is utilized to extract information from images and evaluate the condition state of cracks according to FHWA specifications. The new workflow uses a deep neural network to extract information required by the bridge inspection manual, enabling the determination of the condition state of cracks in the deck. The results of experimentation demonstrate the effectiveness of this workflow for this application. The method also balances the costs and risks associated with increasing levels of AI involvement, enabling inspectors to better manage their resources. This AI-based method can be implemented by asset owners, such as Departments of Transportation, to better serve communities. Full article

(This article belongs to the Special Issue Smart Sensing and Artificial Intelligence for Civil Infrastructure Monitoring and Management)

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16 pages, 5053 KiB

Open AccessArticle

An Event-Classification Neural Network Approach for Rapid Railroad Bridge Impact Detection

by Omobolaji Lawal, Shaik Althaf V. Shajihan, Kirill Mechitov and Billie F. Spencer, Jr.

Sensors 2023, 23(6), 3330; https://doi.org/10.3390/s23063330 - 22 Mar 2023

Cited by 4 | Viewed by 2085

Abstract

Railroads are a critical part of the United States’ transportation sector. Over 40 percent (by weight) of the nation’s freight is transported by rail, and according to the Bureau of Transportation statistics, railroads moved $186.5 billion of freight in 2021. A vital part of the freight network is railroad bridges, with a good number being low-clearance bridges that are prone to impacts from over-height vehicles; such impacts can cause damage to the bridge and lead to unwanted interruption in its usage. Therefore, the detection of impacts from over-height vehicles is critical for the safe operation and maintenance of railroad bridges. While some previous studies have been published regarding bridge impact detection, most approaches utilize more expensive wired sensors, as well as relying on simple threshold-based detection. The challenge is that the use of vibration thresholds may not accurately distinguish between impacts and other events, such as a common train crossing. In this paper, a machine learning approach is developed for accurate impact detection using event-triggered wireless sensors. The neural network is trained with key features which are extracted from event responses collected from two instrumented railroad bridges. The trained model classifies events as impacts, train crossings, or other events. An average classification accuracy of 98.67% is obtained from cross-validation, while the false positive rate is minimal. Finally, a framework for edge classification of events is also proposed and demonstrated using an edge device. Full article

(This article belongs to the Special Issue Smart Sensing and Artificial Intelligence for Civil Infrastructure Monitoring and Management)

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19 pages, 5663 KiB

Open AccessArticle

Denoising of BOTDR Dynamic Strain Measurement Using Convolutional Neural Networks

by Bo Li, Ningjun Jiang and Xiaole Han

Sensors 2023, 23(4), 1764; https://doi.org/10.3390/s23041764 - 4 Feb 2023

Cited by 9 | Viewed by 2167

Abstract

The Brillouin optical time domain reflectometry (BOTDR) system measures the distributed strain and temperature information along the optic fibre by detecting the Brillouin gain spectra (BGS) and finding the Brillouin frequency shift profiles. By introducing small gain stimulated Brillouin scattering (SBS), dynamic measurement using BOTDR can be realized, but the performance is limited due to the noise of the detected information. An image denoising method using the convolutional neural network (CNN) is applied to the derived Brillouin gain spectrum images to enhance the performance of the Brillouin frequency shift detection and the strain vibration measurement of the BOTDR system. By reducing the noise of the BGS images along the length of the fibre under test with different network depths and epoch numbers, smaller frequency uncertainties are obtained, and the sine-fitting R-squared values of the detected strain vibration profiles are also higher. The Brillouin frequency uncertainty is improved by 24% and the sine-fitting R-squared value of the obtained strain vibration profile is enhanced to 0.739, with eight layers of total depth and 200 epochs. Full article

(This article belongs to the Special Issue Smart Sensing and Artificial Intelligence for Civil Infrastructure Monitoring and Management)

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17 pages, 3464 KiB

Open AccessArticle

Multiple In-Mold Sensors for Quality and Process Control in Injection Molding

by Richárd Dominik Párizs, Dániel Török, Tatyana Ageyeva and József Gábor Kovács

Sensors 2023, 23(3), 1735; https://doi.org/10.3390/s23031735 - 3 Feb 2023

Cited by 8 | Viewed by 3639

Abstract

The simultaneous improvement of injection molding process efficiency and product quality, as required by Industry 4.0, is a complex, non-trivial task that requires a comprehensive approach, which involves a combination of sensoring and information techniques. In this study, we investigated the suitability of in-mold pressure sensors to control the injection molding process in multi-cavity molds. We have conducted several experiments to show how to optimize the clamping force, switchover, or holding time by measuring only pressure in a multi-cavity mold. The results show that the pressure curves and the pressure integral are suitable for determining optimal clamping force. We also proved that in-channel sensors could be effectively used for a pressure-controlled SWOP. In the volume-controlled method, only the sensors in the cavity were capable of correctly detecting the end of the filling. We proposed a method to optimize the holding phase. In this method, we first determined the integration time of the area under the pressure curve and then performed a model fit using the relationship between the pressure integral and product mass. The saturation curve fitted to the pressure data can easily determine the gate freeze-off time from pressure measurements. Full article

(This article belongs to the Special Issue Smart Sensing and Artificial Intelligence for Civil Infrastructure Monitoring and Management)

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21 pages, 6236 KiB

Open AccessArticle

Buried RF Sensors for Smart Road Infrastructure: Empirical Communication Range Testing, Propagation by Line of Sight, Diffraction and Reflection Model and Technology Comparison for 868 MHz–2.4 GHz

by Vlad Marsic, Soroush Faramehr, Joe Fleming, Peter Ball, Shumao Ou and Petar Igic

Sensors 2023, 23(3), 1669; https://doi.org/10.3390/s23031669 - 2 Feb 2023

Cited by 3 | Viewed by 2997

Abstract

Updating the road infrastructure requires the potential mass adoption of the road studs currently used in car detection, speed monitoring, and path marking. Road studs commonly include RF transceivers connecting the buried sensors to an offsite base station for centralized data management. Since traffic monitoring experiments through buried sensors are resource expensive and difficult, the literature detailing it is insufficient and inaccessible due to various strategic reasons. Moreover, as the main RF frequencies adopted for stud communication are either 868/915 MHz or 2.4 GHz, the radio coverage differs, and it is not readily predictable due to the low-power communication in the near proximity of the ground. This work delivers a reference study on low-power RF communication ranging for the two above frequencies up to 60 m. The experimental setup employs successive measurements and repositioning of a base station at three different heights of 0.5, 1 and 1.5 m, and is accompanied by an extensive theoretical analysis of propagation, including line of sight, diffraction, and wall reflection. Enhancing the tutorial value of this work, a correlation analysis using Pearson’s coefficient and root mean square error is performed between the field test and simulation results. Full article

(This article belongs to the Special Issue Smart Sensing and Artificial Intelligence for Civil Infrastructure Monitoring and Management)

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17 pages, 9104 KiB

Open AccessArticle

Multirotor Motor Failure Detection with Piezo Sensor

by Leszek Ambroziak, Daniel Ołdziej and Andrzej Koszewnik

Sensors 2023, 23(2), 1048; https://doi.org/10.3390/s23021048 - 16 Jan 2023

Cited by 13 | Viewed by 3352

Abstract

Failure detection of Unmanned Aerial Vehicle (UAV) motors and propulsion systems is the most important step in the implementation of active fault-tolerant control systems. This will increase the reliability of unmanned systems and increase the level of safety, especially in civil and commercial applications. The following paper presents a method of motor failure detection in the multirotor UAV using piezo bars. The results of a real flight, in which the failure of the propulsion system caused the crash of a hybrid VTOL UAV, were presented and analyzed. The conclusions drawn from this flight led to the development of a lightweight, simple and reliable sensor that can detect a failure of the UAV propulsion system. The article presents the outcomes of laboratory tests concerning measurements made with a piezo sensor. An extensive analysis of the obtained results of vibrations recorded on a flying platform arm with a propulsion system is presented, and a methodology for using this type of data to detect failures is proposed. The article presents the possibility of using a piezoelectric sensor to record vibrations on the basis of which it is possible to detect a failure of the UAV propulsion system. Full article

(This article belongs to the Special Issue Smart Sensing and Artificial Intelligence for Civil Infrastructure Monitoring and Management)

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23 pages, 4333 KiB

Open AccessArticle

Physics-Guided Real-Time Full-Field Vibration Response Estimation from Sparse Measurements Using Compressive Sensing

by Debasish Jana and Satish Nagarajaiah

Sensors 2023, 23(1), 384; https://doi.org/10.3390/s23010384 - 29 Dec 2022

Cited by 7 | Viewed by 2882

Abstract

In civil, mechanical, and aerospace structures, full-field measurement has become necessary to estimate the precise location of precise damage and controlling purposes. Conventional full-field sensing requires dense installation of contact-based sensors, which is uneconomical and mostly impractical in a real-life scenario. Recent developments in computer vision-based measurement instruments have the ability to measure full-field responses, but implementation for long-term sensing could be impractical and sometimes uneconomical. To circumvent this issue, in this paper, we propose a technique to accurately estimate the full-field responses of the structural system from a few contact/non-contact sensors randomly placed on the system. We adopt the Compressive Sensing technique in the spatial domain to estimate the full-field spatial vibration profile from the few actual sensors placed on the structure for a particular time instant, and executing this procedure repeatedly for all the temporal instances will result in real-time estimation of full-field response. The basis function in the Compressive Sensing framework is obtained from the closed-form solution of the generalized partial differential equation of the system; hence, partial knowledge of the system/model dynamics is needed, which makes this framework physics-guided. The accuracy of reconstruction in the proposed full-field sensing method demonstrates significant potential in the domain of health monitoring and control of civil, mechanical, and aerospace engineering systems. Full article

(This article belongs to the Special Issue Smart Sensing and Artificial Intelligence for Civil Infrastructure Monitoring and Management)

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16 pages, 3644 KiB

Open AccessArticle

A Novel Approach for Cable Tension Monitoring Based on Mode Shape Identification

by Yichao Xu, Jian Zhang, Yufeng Zhang and Changzhao Li

Sensors 2022, 22(24), 9975; https://doi.org/10.3390/s22249975 - 18 Dec 2022

Cited by 6 | Viewed by 2470

Abstract

Estimation and monitoring of cable tension is of great significance in the structural assessment of cable-supported bridges. For short cables, the traditional cable tension identification method via frequency measurement has large errors due to the influence of complex boundaries, which affect the accuracy of estimation. A new cable tension estimation method based on mode shape identification with a multiple sensor arrangement on the cable can take the influence of boundary conditions into account and its accuracy has been verified. However, it requires more sensors compared to the traditional frequency-based method, which will significantly increase the cost of long-term monitoring in practice. Therefore, a novel approach for cable tension monitoring considering both cost and accuracy is further proposed in this study. The approach adopts multiple sensors to measure the influence of boundary conditions. Then, only a single sensor is required for long-term monitoring of the cable. In this paper, an analytical model of the cable is firstly established. The influence of boundary conditions is calculated, which ensures the accuracy of mode shape identification. Furthermore, a field experiment is carried out to verify the effectiveness of the new approach. The results have demonstrated the effectiveness and accurateness of the proposed method in long-term short cable tension monitoring. Full article

(This article belongs to the Special Issue Smart Sensing and Artificial Intelligence for Civil Infrastructure Monitoring and Management)

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16 pages, 9022 KiB

Open AccessArticle

Wheel-Rail Contact-Induced Impact Vibration Analysis for Switch Rails Based on the VMD-SS Method

by Pan Hu, Haitao Wang, Chunlin Zhang, Liang Hua and Guiyun Tian

Sensors 2022, 22(18), 6872; https://doi.org/10.3390/s22186872 - 11 Sep 2022

Cited by 1 | Viewed by 2429

Abstract

When trains pass through damaged switch rails, rail head damage will change wheel–rail contact states from rolling frictions to unsteady contacts, which will result in impact vibrations and threaten structural safeties. In addition, under approaching and moving away rolling contact excitations and complex wheel–rail contacts, the non-stationary vibrations make it difficult to extract and analyze impact vibrations. In view of the above problems, this paper proposes a variational-mode-decomposition (VMD)-spectral-subtraction (SS)-based impact vibration extraction method. Firstly, the time domain feature analysis method is applied to calculate the time moments that the wheels pass joints, and to correct vehicle velocities. This can help estimate and confine impact vibration distribution ranges. Then, the stationary intrinsic mode function (IMF) components of the impact vibration are decomposed and analyzed with the VMD method. Finally, impact vibrations are further filtered with the SS method. For rail head damage with different dimensions, under different velocity experiments, the frequency and amplitude features of the impact vibrations are analyzed. Experimental results show that, in low-velocity scenarios, the proposed VMD–SS–based method can extract impact vibrations, the frequency features are mainly concentrated in 3500–5000 Hz, and the frequency and peak-to-peak features increase with the increase in excitation velocities. Full article

(This article belongs to the Special Issue Smart Sensing and Artificial Intelligence for Civil Infrastructure Monitoring and Management)

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22 pages, 7594 KiB

Open AccessArticle

Hierarchical Dynamic Bayesian Network-Based Fatigue Crack Propagation Modeling Considering Initial Defects

by Yang Xu, Bin Zhu, Zheng Zhang and Jiahui Chen

Sensors 2022, 22(18), 6777; https://doi.org/10.3390/s22186777 - 7 Sep 2022

Cited by 7 | Viewed by 2129

Abstract

Orthotropic steel decks (OSDs) are inevitably subjected to fatigue damage caused by cycled vehicle loads in long-span bridges. This study establishes a probabilistic analysis framework integrating the dynamic Bayesian network (DBN) and fracture mechanics to model the fatigue crack propagation considering mutual correlations among multiple fatigue details. Both the observations of fatigue crack length from field inspection and monitoring data of vehicle loads from the weight-in-motion (WIM) system are utilized. First, fracture mechanics-based uncertainty analysis is performed to determine the multiple uncertainty sources in the Paris crack propagation model, material property, and observation data of crack length. The uncertainty of monitoring data of vehicle loads is ignored because of its high accuracy; consequently, the vehicle-load-related uncertainty is spontaneously ignored, which is also demonstrated to be very small on the investigated actual bridges. Second, a hierarchical DBN model is introduced to construct mutual dependencies among various uncertainties and intra-correlations in the propagation process of multiple fatigue cracks at different components. Third, a stochastic traffic model is established based on the WIM monitoring data and multi-scale finite element analysis via substructure techniques to determine the probability distribution of vehicle-load-related parameters. After variable discretization, a refined exact inference algorithm in a forward–backward–forward manner is adopted to estimate the posterior distribution of equivalent initial crack length and update the established DBN model. Finally, the proposed method is demonstrated by a numerical case study and a typical application on an actual cable-stayed bridge with steel box girders using OSDs in China. The results show that the probability distribution of equivalent initial crack size can be spontaneously derived with a larger mean value than the results of conventional empirical analysis. Furthermore, the component-level fatigue reliability is tracked and predicted based on the established crack propagation model. Full article

(This article belongs to the Special Issue Smart Sensing and Artificial Intelligence for Civil Infrastructure Monitoring and Management)

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18 pages, 7699 KiB

Open AccessArticle

xImpact: Intelligent Wireless System for Cost-Effective Rapid Condition Assessment of Bridges under Impacts

by Yuguang Fu, Yaoyu Zhu, Tu Hoang, Kirill Mechitov and Billie F. Spencer, Jr.

Sensors 2022, 22(15), 5701; https://doi.org/10.3390/s22155701 - 29 Jul 2022

Cited by 9 | Viewed by 2667

Abstract

Bridge strikes by over-height vehicles or ships are critical sudden events. Due to their unpredictable nature, many events go unnoticed or unreported, but they can induce structural failures or hidden damage that accelerates the bridge’s long-term degradation. Therefore, always-on monitoring is essential for deployed systems to enhance bridge safety through the reliable detection of such events and the rapid assessment of bridge conditions. Traditional bridge monitoring systems using wired sensors are too expensive for widespread implementation, mainly due to their significant installation cost. In this paper, an intelligent wireless monitoring system is developed as a cost-effective solution. It employs ultralow-power, event-triggered wireless sensor prototypes, which enables on-demand, high-fidelity sensing without missing unpredictable impact events. Furthermore, the proposed system adopts a smart artificial intelligence (AI)-based framework for rapid bridge assessment by utilizing artificial neural networks. Specifically, it can identify the impact location and estimate the peak force and impulse of impacts. The obtained impact information is used to provide early estimation of bridge conditions, allowing the bridge engineers to prioritize resource allocation for the timely inspection of the more severe impacts. The performance of the proposed monitoring system is demonstrated through a full-scale field test. The test results show that the developed system can capture the onset of bridge impacts, provide high-quality synchronized data, and offer a rapid damage assessment of bridges under impact events, achieving the error of around 2 m in impact localization, 1 kN for peak force estimation, and 0.01 kN·s for impulse estimation. Long-term deployment is planned in the future to demonstrate its reliability for real-life impact events. Full article

(This article belongs to the Special Issue Smart Sensing and Artificial Intelligence for Civil Infrastructure Monitoring and Management)

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