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Recent Development and Applications of Sensing Technology in Resilient and Sustainable Infrastructure

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

Deadline for manuscript submissions: 20 June 2025 | Viewed by 7461

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Special Issue Editors

Prof. Dr. Mohammad Noori

E-Mail Website1 Website2
Guest Editor

1. Department of Mechanical Engineering, California Polytechnic State University, San Luis Obispo, CA 93405, USA
2. School of Civil Engineering, University of Leeds, Leeds LS2 9JT, UK
Interests: AI-based methods for structural health monitoring and dynamic response; random vibrations; hysteretic systems; seismic isolation; reliability and resilience
Special Issues, Collections and Topics in MDPI journals

Dr. Yihua Zeng

E-Mail Website
Guest Editor

School of Civil Engineering, Southeast University, Nanjing 211189, China
Interests: lifeline infrastructure; advanced sensing technology; advanced construction techniques; structural performance, reliability and resilience; machine learning application

Special Issue Information

Dear Colleagues,

Infrastructure, including buildings, roads, bridges, tunnels, water supply, sewers, electrical grids, and telecommunications, serves as the most essential component to enable, sustain, and enhance modern societal living conditions and economic development. The use of advanced sensing technologies to contribute to improving infrastructure resilience and sustainability capacities has become a recurring hot theme in government, industrial, and academic discussions. Recently, accidents due to, for example, natural hazards and/or human errors have occurred worldwide from time to time. To improve the resilience and sustainability of various kinds of infrastructures, the demand of using innovative sensing technologies to inspect and monitor infrastructural physical conditions to warrant infrastructure to withstand or efficiently recover from multihazard disruptive events keeps growing.

To foster knowledge conversation and explore the recent development and applications of sensing technology in resilient and sustainable infrastructure, we initiate this Special Issue to invite researchers and experts in related fields to contribute their insights, ideas, and experimental, theoretical, and computational findings within, but not limited to, the following topics:

Structural health monitoring;
Acoustic and ultrasonic sensing;
Fiber optic sensing technique;
Infrared detection technique;
Computer vision-based technique and methods;
Electromagnetic and magnetism-based sensing technology;
Nondestructive testing;
Laboratory testing method;
IoT and remote sensing;
Sensor placement and optimization;
Environmental effect detection;
Damage and event detection and identification;
Structural performance assessment;
Data quality and data mining;
Artificial intelligence and machine learning;
Lifecycle management and carbon emission analysis;
Application and case studies.

Prof. Dr. Mohammad Noori
Dr. Yihua Zeng
Guest Editors

Manuscript Submission Information

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Keywords

advanced sensing technologies
resilient and sustainable infrastructure
structural health monitoring

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Published Papers (7 papers)

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Research

10 pages, 2220 KiB

Open AccessArticle

Prediction of Blast Vibration Velocity of Buried Steel Pipe Based on PSO-LSSVM Model

by Hongyu Zhang, Shengwu Tu, Senlin Nie and Weihua Ming

Sensors 2024, 24(23), 7437; https://doi.org/10.3390/s24237437 - 21 Nov 2024

Viewed by 347

Abstract

In order to ensure the safe operation of adjacent buried pipelines under blast vibration, it is of great practical engineering significance to accurately predict the peak vibration velocity ofburied pipelines under blasting loads. Relying on the test results of the buried steel pipe blast model test, a sensitivity analysis of relevant influencing factors was carried out by using the gray correlation analysis method. A least squares support vector machine (LS-SVM) model was established to predict the peak vibration velocity of the pipeline and determine the best parameter combination in the LS-SVM model through a local particle swarm optimization (PSO), and the results of the PSO-LSSVM model were predicted. These were compared with BP neural network model and Sa’s empirical formula. The results show that the fitting correlation coefficient (R2), root mean square error (RMSE), average relative error (MRE), and Nash coefficient (NSE) of the PSO-LSSVM model for the prediction of pipeline peak vibration velocity are 91.51%, 2.95%, 8.69%, and 99.03%, showing that the PSO-LSSVM model has a higher prediction accuracy and better generalization ability, which provides a new idea for the vibration velocity prediction of buried pipelines under complex blasting conditions. Full article

(This article belongs to the Special Issue Recent Development and Applications of Sensing Technology in Resilient and Sustainable Infrastructure)

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15 pages, 7454 KiB

Open AccessArticle

Road Defect Identification and Location Method Based on an Improved ML-YOLO Algorithm

by Tianwen Li and Gongquan Li

Sensors 2024, 24(21), 6783; https://doi.org/10.3390/s24216783 - 22 Oct 2024

Viewed by 1045

Abstract

The conventional method for detecting road defects relies heavily on manual inspections, which are often inefficient and struggle with precise defect localization. This paper introduces a novel approach for identifying and locating road defects based on an enhanced ML-YOLO algorithm. By refining the YOLOv8 object detection framework, we optimize both the traditional convolutional layers and the spatial pyramid pooling network. Additionally, we incorporate the Convolutional Block Attention to effectively capture channel and spatial features, along with the Selective Kernel Networks that dynamically adapt to feature extraction across varying scales. An optimized target localization algorithm is proposed to achieve high-precision identification and accurate positioning of road defects. Experimental results indicate that the detection accuracy of the improved ML-YOLO algorithm reaches 0.841, with a recall rate of 0.745 and an average precision of 0.817. Compared to the baseline YOLOv8 model, there is an increase in accuracy by 0.13, a rise in recall rate by 0.117, and an enhancement in average precision by 0.116. After the high detection accuracy of road defects was confirmed, generalization experiments were carried out on the improved ML-YOLO model in the public data set. The experimental results showed that compared with the original YOLOv8n, the average precision and recall rate of all types of ML-YOLO increased by 0.075, 0.121, and 0.035 respectively, indicating robust generalization capabilities. When applied to real-time road monitoring scenarios, this algorithm facilitates precise detection and localization of defects while significantly mitigating traffic accident risks and extending roadway service life. A high detection accuracy of road defects was achieved. Full article

(This article belongs to the Special Issue Recent Development and Applications of Sensing Technology in Resilient and Sustainable Infrastructure)

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14 pages, 6445 KiB

Open AccessArticle

Multi-Sensor-Assisted Low-Cost Indoor Non-Visual Semantic Map Construction and Localization for Modern Vehicles

by Guangxiao Shao, Fanyu Lin, Chao Li, Wei Shao, Wennan Chai, Xiaorui Xu, Mingyue Zhang, Zhen Sun and Qingdang Li

Sensors 2024, 24(13), 4263; https://doi.org/10.3390/s24134263 - 30 Jun 2024

Viewed by 1228

Abstract

With the transformation and development of the automotive industry, low-cost and seamless indoor and outdoor positioning has become a research hotspot for modern vehicles equipped with in-vehicle infotainment systems, Internet of Vehicles, or other intelligent systems (such as Telematics Box, Autopilot, etc.). This paper analyzes modern vehicles in different configurations and proposes a low-cost, versatile indoor non-visual semantic mapping and localization solution based on low-cost sensors. Firstly, the sliding window-based semantic landmark detection method is designed to identify non-visual semantic landmarks (e.g., entrance/exit, ramp entrance/exit, road node). Then, we construct an indoor non-visual semantic map that includes the vehicle trajectory waypoints, non-visual semantic landmarks, and Wi-Fi fingerprints of RSS features. Furthermore, to estimate the position of modern vehicles in the constructed semantic maps, we proposed a graph-optimized localization method based on landmark matching that exploits the correlation between non-visual semantic landmarks. Finally, field experiments are conducted in two shopping mall scenes with different underground parking layouts to verify the proposed non-visual semantic mapping and localization method. The results show that the proposed method achieves a high accuracy of 98.1% in non-visual semantic landmark detection and a low localization error of 1.31 m. Full article

(This article belongs to the Special Issue Recent Development and Applications of Sensing Technology in Resilient and Sustainable Infrastructure)

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27 pages, 10431 KiB

Open AccessArticle

Design and Construction of a Portable IoT Station

by Mario A. Trape, Ali Hellany, Syed K. H. Shah, Jamal Rizk, Mahmood Nagrial and Tosin Famakinwa

Sensors 2024, 24(13), 4116; https://doi.org/10.3390/s24134116 - 25 Jun 2024

Viewed by 902

Abstract

This paper discusses the design and implementation of a portable IoT station. Communication and data synchronization issues in several installations are addressed here, making possible a detailed analysis of the entire system during its operation. The system operator requires a synchronized data stream, combining multiple communication protocols into one single time stamp. The hardware selected for the portable IoT station complies with the International Electrotechnical Commission (IEC) industrial standards. A short discussion regarding interface customization shows how easily the hardware can be modified so that it is integrated with almost any system. A programmable logic controller enables the Node-RED to be utilized. This open-source middleware defines operations for each global variable nominated in the Modbus register. Two applications are presented and discussed in this paper; each application has a distinct methodology utilized to publish and visualize the acquired data. The portable IoT station is highly customizable, consisting of a modular structure and providing the best platform for future research and development of dedicated algorithms. This paper also demonstrates how the portable IoT station can be implemented in systems where time-based data synchronization is essential while introducing a seamless implementation and operation. Full article

(This article belongs to the Special Issue Recent Development and Applications of Sensing Technology in Resilient and Sustainable Infrastructure)

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23 pages, 33385 KiB

Open AccessArticle

An Improvement Method for Improving the Surface Defect Detection of Industrial Products Based on Contour Matching Algorithms

by Haorong Wu, Ziqi Luo, Fuchun Sun, Xiaoxiao Li and Yongxin Zhao

Sensors 2024, 24(12), 3932; https://doi.org/10.3390/s24123932 - 17 Jun 2024

Cited by 1 | Viewed by 1064

Abstract

Aiming at the problems of the poor robustness and universality of traditional contour matching algorithms in engineering applications, a method for improving the surface defect detection of industrial products based on contour matching algorithms is detailed in this paper. Based on the image pyramid optimization method, a three-level matching method is designed, which can quickly obtain the candidate pose of the target contour at the top of the image pyramid, combining the integral graph and the integration graph acceleration strategy based on weak classification. It can quickly obtain the rough positioning and rough angle of the target contour, which greatly improves the performance of the algorithm. In addition, to solve the problem that a large number of duplicate candidate points will be generated when the target candidate points are expanded, a method to obtain the optimal candidate points in the neighborhood of the target candidate points is designed, which can guarantee the matching accuracy and greatly reduce the calculation amount. In order to verify the effectiveness of the algorithm, functional test experiments were designed for template building function and contour matching function, including uniform illumination condition, nonlinear condition and contour matching detection under different conditions. The results show that: (1) Under uniform illumination conditions, the detection accuracy can be maintained at about 93%. (2) Under nonlinear illumination conditions, the detection accuracy can be maintained at about 91.84%. (3) When there is an external interference source, there will be a false detection or no detection, and the overall defect detection rate remains above 94%. It is verified that the proposed method can meet the application requirements of common defect detection, and has good robustness and meets the expected functional requirements of the algorithm, providing a strong technical guarantee and data support for the design of embedded image sensors in the later stage. Full article

(This article belongs to the Special Issue Recent Development and Applications of Sensing Technology in Resilient and Sustainable Infrastructure)

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21 pages, 11966 KiB

Open AccessArticle

Image-Based Bolt-Loosening Detection Using a Checkerboard Perspective Correction Method

by Chengqian Xie, Jun Luo, Kaili Li, Zhitao Yan, Feng Li, Xiaogang Jia and Yuanlai Wang

Sensors 2024, 24(11), 3271; https://doi.org/10.3390/s24113271 - 21 May 2024

Cited by 1 | Viewed by 1049

Abstract

In this paper, a new image-correction method for flange joint bolts is proposed. A checkerboard is arranged on the side of a flange node bolt, and the homography matrix can be estimated using more than four feature points, which include the checkerboard corner points. Then, the perspective distortion of the captured image and the deviation of the camera position angle are corrected using the estimated homography matrix. Due to the use of more feature points, the stability of homography matrix identification is effectively improved. Simultaneously, the influence of the number of feature points, camera lens distance, and light intensities are analyzed. Finally, based on a bolt image taken using an iPhone 12, the prototype structure of the flange joint in the laboratory is verified. The results show that the proposed method can effectively correct image distortion and camera position angle deviation. The use of more than four correction points not only effectively improves the stability of bolt image correction but also improves the stability and accuracy of bolt-loosening detection. The analysis of influencing factors shows that the proposed method is still effective when the number of checkerboard correction points is reduced to nine, and the average error of the bolt-loosening detection result is less than 1.5 degrees. Moreover, the recommended camera shooting distance range is 20 cm to 60 cm, and the method exhibits low sensitivity to light intensity. Full article

(This article belongs to the Special Issue Recent Development and Applications of Sensing Technology in Resilient and Sustainable Infrastructure)

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13 pages, 2749 KiB

Open AccessArticle

A Laboratory-Scale Evaluation of Smart Pebble Sensors Embedded in Geomaterials

by Syed Faizan Husain, Mohammad Shoaib Abbas, Han Wang, Issam I. A. Qamhia, Erol Tutumluer, John Wallace and Matthew Hammond

Sensors 2024, 24(9), 2733; https://doi.org/10.3390/s24092733 - 25 Apr 2024

Viewed by 875

Abstract

This paper introduces a novel approach to measure deformations in geomaterials using the recently developed ‘Smart Pebble’ sensors. Smart Pebbles were included in triaxial test specimens of unbound aggregates stabilized with geogrids. The sensors are equipped with an aggregate particle/position tracking algorithm that can manage uncertainty arising due to signal noise and random walk effects. Two Smart Pebbles were placed in each test specimen, one at specimen’s mid-height, where a geogrid was installed in the mechanically stabilized specimen, and one towards the top of the specimen. Even with simple raw data processing, the trends on linear vertical acceleration indicated the ability of Smart Pebbles to assess the geomaterial configuration and applied stress states. Employing a Kalman filter-based algorithm, the Smart Pebble position coordinates were tracked during testing. The specimen’s resilient deformations were simultaneously recorded. bender element shear wave transducer pairs were also installed on the specimens to further validate the Smart Pebble small-strain responses. The results indicate a close agreement between the BE sensors and Smart Pebbles estimates towards local stiffness enhancement quantification in the geogrid specimen. The study findings confirm the viability of using the Smart Pebbles in describing the resilient behavior of an aggregate material under repeated loading. Full article

(This article belongs to the Special Issue Recent Development and Applications of Sensing Technology in Resilient and Sustainable Infrastructure)

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