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Applied Sciences
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Advanced Technologies in SHM, Performance Evaluation, and Reliabilty Analysis
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Advanced Technologies in SHM, Performance Evaluation, and Reliabilty Analysis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 4888

Special Issue Editor

Dr. Qi-Ang Wang

E-Mail Website
Guest Editor
School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: structural health monitoring; reliability analysis; wireless sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Structural health monitoring (SHM), as a technique for monitoring and evaluating structural health status, has been widely concerned and applied in recent years. It can not only monitor the vibration and strain of the structure in real time, but also provide an accurate assessment of the health status of the structure, thus providing important data support for decision makers. The performance evaluation of a structure is an important step to ensure its long-term operation and safety. By using SHM technology, we can monitor the vibration characteristics and deformation of the structure in real time, and provide data support for the performance evaluation of the structure. Reliability analysis is an important means to evaluate the stability of structures under different external loads and environmental conditions. With SHM technology, we can monitor the health status of the structure in real time, identify potential faults and fragile points, and propose corresponding improvement measures to improve the reliability and durability of the structure. Potential topics include, but are not limited to: Structural health monitoring; Performance evaluation; Reliability analysis; Application of new sensor technology in structural health monitoring; Application of machine learning to performance evaluation; The development of nondestructive testing technology and its application in reliability analysis.

Dr. Qi-Ang Wang
Guest Editor

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Keywords

  • structural health monitoring
  • performance evaluation
  • reliability analysis
  • machine learning
  • sensors

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Published Papers (3 papers)

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Research

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22 pages, 2843 KiB  
Article
The Application of Structural Reliability and Sensitivity Analysis in Engineering Practice
by Urszula Radoń and Paweł Zabojszcza
Appl. Sci. 2025, 15(1), 342; https://doi.org/10.3390/app15010342 - 1 Jan 2025
Viewed by 1023
Abstract
Standard safety assessments of civil engineering systems are conducted using safety factors. An alternative method to this approach is the assessment of the engineering system using reliability analysis of the structure. In reliability analysis of the structure, both the uncertainty of the load [...] Read more.
Standard safety assessments of civil engineering systems are conducted using safety factors. An alternative method to this approach is the assessment of the engineering system using reliability analysis of the structure. In reliability analysis of the structure, both the uncertainty of the load and the properties of the materials or geometry are explicitly taken into account. The uncertainties are described in a probabilistic manner. After defining the ultimate and serviceability limit state functions, we can calculate the failure probability for each state. When assessing structural reliability, it is useful to calculate measures that provide information about the influence of random parameters on the failure probability. Classical measures are vectors, whose coordinates are the first partial derivatives of reliability indices evaluated in the design point. These values are obtained as a by-product of the First-Order Reliability Method. Furthermore, we use Sobol indices to describe the sensitivity of the failure probability to input random variables. Computations of the Sobol indices are carried out using the classic Monte Carlo method. The aim of this article is not to define new sensitivity measures, but to show the advantages of using structural reliability and sensitivity analysis in everyday design practice. Using a simple cantilever beam as an example, we will present calculations of probability failure and local and global sensitivity measures. The calculations will be performed using COMREL modules of the STRUREL computing environment. Based on the results obtained from the sensitivity analysis, we can conclude that in the case of the serviceability limit state, the most significant influence on the results is exerted by variables related to the geometry of the beam under consideration. The influence of changes in Young’s modulus and load on the probability of failure is minimal. In further calculations, these quantities can be treated as deterministic. In the case of the ultimate limit state, the influence of changes in the yield strength is significant. The influence of changes in the load and length of the beam is significantly smaller. The authors present two alternative ways of designing with a probabilistic approach, using the FORM (SORM) and Monte Carlo simulation. The approximation FORM cannot be used in every case in connection with gradient determination problems. In such cases, it is worth using the Monte Carlo simulation method. The results of both methods are comparable. Full article
(This article belongs to the Special Issue Advanced Technologies in SHM, Performance Evaluation, and Reliabilty Analysis)
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16 pages, 10223 KiB  
Article
Research on Full-Field Dynamic Deflection Measurement of Beams Based on Dense Feature Matching and Mismatch Removal Method
by Jiayan Zheng, Yichen Tang, Haijing Liu, Zhixiang Zhou and Ji He
Appl. Sci. 2024, 14(8), 3347; https://doi.org/10.3390/app14083347 - 16 Apr 2024
Viewed by 1079
Abstract
To solve the problems of measurement errors led by mismatches of dense feature matching in machine vision structural deflection measurement, this paper proposes a dense feature extraction, matching, and dual-step mismatch-removal-based full-field structural dynamic deflection measurement method. First, the of dense feature detection [...] Read more.
To solve the problems of measurement errors led by mismatches of dense feature matching in machine vision structural deflection measurement, this paper proposes a dense feature extraction, matching, and dual-step mismatch-removal-based full-field structural dynamic deflection measurement method. First, the of dense feature detection and matching theory is introduced to extract the SIFT feature points on a structural surface in an image sequence and matched by FLANN to trace the structure movement, and the mechanisms and causes of mismatches are analyzed. Then, a dual-step mismatch removal method combining RANSAC and Structural Displacement Continuity Restriction (SDCR) is introduced to achieve full-field dynamic beam deflection measurement. The proposed method is validated through indoor cantilever beam experiments, and results show that the method can effectively eliminate a large number of SIFT feature mismatches (accounting for approximately 55% of the total matched feature points). The full-field dynamic displacement field of the beam can be measured with the correctly matched dense feature points by converting dense feature point displacements into continuous and uniform spatiotemporal deflections of the structure. A comparison with the GOM Correlate Professional DIC measurement system was conducted, and the maximum measurement error of the cantilever beam dynamic displacement of the proposed method is between 0.024 and 0.053 mm, the root mean squared error of displacement is approximately 0.01 mm, and the correlation coefficient between two deflection–time curves reaches 0.9964. The proposed algorithm is proven to be effective in full-field displacement measurement and has great potential in future structural health monitoring of bridges. Full article
(This article belongs to the Special Issue Advanced Technologies in SHM, Performance Evaluation, and Reliabilty Analysis)
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Review

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18 pages, 12188 KiB  
Review
A Concise State-of-the-Art Review of Crack Monitoring Enabled by RFID Technology
by Sheng-Cai Ran, Qi-Ang Wang, Jun-Fang Wang, Yi-Qing Ni, Zhong-Xu Guo and Yang Luo
Appl. Sci. 2024, 14(8), 3213; https://doi.org/10.3390/app14083213 - 11 Apr 2024
Cited by 4 | Viewed by 2081
Abstract
Cracking is an important factor affecting the performance and life of large structures. In order to maximize personal safety and reduce costs, it is highly necessary to carry out research on crack monitoring technology. Sensors based on Radio Frequency Identification (RFID) antennas have [...] Read more.
Cracking is an important factor affecting the performance and life of large structures. In order to maximize personal safety and reduce costs, it is highly necessary to carry out research on crack monitoring technology. Sensors based on Radio Frequency Identification (RFID) antennas have the advantages of wireless and low cost, which makes them highly competitive in the field of structure health monitoring (SHM). Thus, this study systematically summarizes the research progress of crack monitoring based on RFID technology in recent years. Firstly, this study introduces the causes of cracks and the traditional monitoring methods. Further, this study summarizes several main RFID-based crack monitoring and detection methods, including crack monitoring based on chipless RFID technology, passive RFID technology, and ultra-high-frequency (UHF) RFID technology, including the implementation methods, as well as the advantages and disadvantages of those technologies. In addition, for RFID-based crack monitoring applications, the two most commonly used materials are concrete materials and metal materials, which are also illustrated in detail. In general, this study can provide technical support and a theoretical basis for crack monitoring and detection to ensure the safety of engineering structures. Full article
(This article belongs to the Special Issue Advanced Technologies in SHM, Performance Evaluation, and Reliabilty Analysis)
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