Special Issue "Advance of Structural Health Monitoring in Civil Engineering"

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

Deadline for manuscript submissions: 31 December 2022 | Viewed by 2064

Special Issue Editors

Dr. Chung Song
E-Mail Website
Guest Editor
Geotechnical Engineering, University of Nebraska–Lincoln, Lincoln, NE, USA
Interests: field instrumentation; advanced analysis based on multiphysics and multiscale approach
Prof. Dr. S. Sonny Kim
E-Mail Website
Co-Guest Editor
School of Environmental, Civil, Agricultural, and Mechanical Engineering, University of Georgia, Athens, GA 30602, USA
Interests: tidal marsh soils; transportation geotechnics; nondestructive remote sensing and machine learning application in geomaterials
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Special Issue Information

Dear Colleagues,

Dramatic advancements in structural health monitoring in Civil Engineering have been made over the last few decades. Structural health monitoring has also become much more popular because monitoring sensors have become more compatible with field applications, economically viable, and better understood among engineers. Well-executed health monitoring systems could provide warnings for potential failures or could enable substantial savings in budgets by optimizing the construction process in some cases. However, poorly executed monitoring systems could create confusion amongst engineers and have disastrous consequences.

The aim of "Advances in Structural Health Monitoring in Civil Engineering" is to provide up-to-date knowledge of structural health monitoring sensors and the usage of sensors. Articles that provide information such as case studies of structural health monitoring, innovative and fast data analysis methods, the pros and cons of different sensors, the evaluation of current practice, the assessment and usage of indirect (soft, non-contact) sensors such as cell phone signals and aerial photos, overarching sensing methods such as ubiquitous systems by merging conventional direct sensors and indirect sensors, and other relevant issues in the broad discipline of Civil Engineering are welcome.

This Special Issue will provide the current practice and new perspectives in structural health monitoring in Civil Engineering so this new area in Civil Engineering may advance to a new paradigm.

Dr. Chung Song
Prof. Dr. S. Sonny Kim
Guest Editors

Manuscript Submission Information

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Keywords

  • sensor
  • strain gauge
  • structural health monitoring
  • instrumentation in civil engineering
  • ubiquitous system

Published Papers (5 papers)

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Research

Article
Design of a Structural Health Monitoring System and Performance Evaluation for a Jacket Offshore Platform in East China Sea
Appl. Sci. 2022, 12(23), 12021; https://doi.org/10.3390/app122312021 - 24 Nov 2022
Viewed by 249
Abstract
Offshore platform plays an important role in ocean strategy, and the construction of structural health monitoring (SHM) system could significantly improve the safety of the platform. In this paper, complete SHM system architecture design for offshore platform is presented, including the sensor subsystem, [...] Read more.
Offshore platform plays an important role in ocean strategy, and the construction of structural health monitoring (SHM) system could significantly improve the safety of the platform. In this paper, complete SHM system architecture design for offshore platform is presented, including the sensor subsystem, data reading and transferring subsystem, data administration subsystem, and assessment subsystem. First, the sensor subsystem is determined to include the structure information, component information, and vibration information monitoring of the offshore platform. Based on the monitoring target, three sensor types including incline sensor, acceleration sensor, and strain sensor are initially selected. Second, the assessment subsystem is determined to include safety monitoring and early warning evaluation using static measurements, overall performance evaluation based on frequency variation, and damage identification based on strain modal using strain monitoring. Overall performance evaluation based on frequency variation and damage identification based on Strain modal are illustrated. Finally, an offshore platform in the East China Sea is selected to establish a finite-element model to discuss the application and feasibility of the SHM system, the frequency variation due to scouring, corrosion, the growth of marine organisms, and temperature variation was investigated, and the overall performance of the platform was also evaluated. This work can provide a reference for installation and implementation of SHM system for offshore platform. Full article
(This article belongs to the Special Issue Advance of Structural Health Monitoring in Civil Engineering)
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Article
The Use of a Movable Vehicle in a Stationary Condition for Indirect Bridge Damage Detection Using Baseline-Free Methodology
Appl. Sci. 2022, 12(22), 11625; https://doi.org/10.3390/app122211625 - 16 Nov 2022
Viewed by 298
Abstract
The use of an instrumented scanning vehicle has become the center of focus for bridge health monitoring (BHM) due to its cost efficiency, mobility, and practicality. However, indirect BHM still faces challenges such as the effects of road roughness on vehicle response, which [...] Read more.
The use of an instrumented scanning vehicle has become the center of focus for bridge health monitoring (BHM) due to its cost efficiency, mobility, and practicality. However, indirect BHM still faces challenges such as the effects of road roughness on vehicle response, which can be avoided when the vehicle is in a stationary condition. This paper proposes a baseline-free method to detect bridge damage using a stationary vehicle. The proposed method is implemented in three steps. First, the contact-point response (CPR) of the stationary vehicle is computed. Secondly, the CPR is decomposed into intrinsic mode functions (IMFs) using the variational mode decomposition (VMD) method. Finally, instantaneous amplitude (IA) of a high frequency IMF is computed. The peak represents the existence and location of the damage. A finite element model of a bridge with damage is created. The results show that the method can identify the damage location under different circumstances, such as a vehicle with and without damping, different speeds of the moving vehicle, different sizes of damage, and multiple damage. A higher speed was found to provide better visibility of damages. In addition, smaller damage was less visible than wider damage. Full article
(This article belongs to the Special Issue Advance of Structural Health Monitoring in Civil Engineering)
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Article
Enhancing Reliability Analysis with Multisource Data: Mitigating Adverse Selection Problems in Bridge Monitoring and Management
Appl. Sci. 2022, 12(20), 10359; https://doi.org/10.3390/app122010359 - 14 Oct 2022
Viewed by 355
Abstract
Data collected using sensors plays an essential role in active bridge health monitoring. When analyzing a large number of bridges in the U.S., the National Bridge Inventory data as been widely used. Yet, the database does not provide information about live loads, one [...] Read more.
Data collected using sensors plays an essential role in active bridge health monitoring. When analyzing a large number of bridges in the U.S., the National Bridge Inventory data as been widely used. Yet, the database does not provide information about live loads, one of the most indeterminate variables for monitoring bridges. Such asymmetric information can lead to an adverse selection problem in making maintenance, rehabilitation, and repair decisions. This study proposes a data-driven reliability analysis to assess probabilities of bridge failure by synthesizing NBI data and Weigh-In-Motion (WIM) data for a large number of bridges in Georgia. On the resistance side, tree ensemble methods are employed to support the hypothesis that the NBI operating load rating represents the distribution of bridge resistance capacities which change over time. On the loading side, the live load distribution is derived from field data collected using WIM sensors. Our results show that the proposed WIM data-enabled reliability analysis substantially enhances information symmetry and provides a reliability index that supports monitoring of bridge conditions, depending on live loads and load-carrying capacities. Full article
(This article belongs to the Special Issue Advance of Structural Health Monitoring in Civil Engineering)
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Article
Identification of Vehicle Loads on an Orthotropic Deck Steel Box Beam Bridge Based on Optimal Combined Strain Influence Lines
Appl. Sci. 2022, 12(19), 9848; https://doi.org/10.3390/app12199848 - 30 Sep 2022
Viewed by 357
Abstract
Vehicles are critical living loads to bridge structure; thus, identifying vehicle loads is very important for structural health monitoring and safety evaluations. This paper proposed a load identification method based on an optimal combined strain influence line. Firstly, two types of strain gauges [...] Read more.
Vehicles are critical living loads to bridge structure; thus, identifying vehicle loads is very important for structural health monitoring and safety evaluations. This paper proposed a load identification method based on an optimal combined strain influence line. Firstly, two types of strain gauges were arranged at the lower edge of a deck to monitor the strain response when vehicles cross the deck. One type of sensor was installed at the lower edge of the deck between U-ribs to detect axle information, including the number of axles, wheelbase, and vehicle speed. The other type of sensor was set on the lower edge of U-ribs to identify the axle’s weight. Secondly, structural responses under the vehicle load with known weights across the bridge was used to identify the strain influence line by using least square method. Because the local mechanical characteristic of the deck was very prominent under the wheel load, the strain influence line was short and susceptible to the transverse position of the vehicle. An index of variation coefficient is proposed as the object function, and an optimal combined strain influence line was developed using a genetic algorithm to decrease the influence of the transverse position of the load. Finally, the unknown vehicle load can be identified based on a calibrated combined strain influence line. A numerical simulation and an experimental test were carried out to validate the effectiveness and anti-noise performance of the proposed method. The identified results showed that the proposed algorithm has good accuracy and anti-noise performance. Full article
(This article belongs to the Special Issue Advance of Structural Health Monitoring in Civil Engineering)
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Article
A Deep-Convolutional-Neural-Network-Based Semi-Supervised Learning Method for Anomaly Crack Detection
Appl. Sci. 2022, 12(18), 9244; https://doi.org/10.3390/app12189244 - 15 Sep 2022
Viewed by 536
Abstract
Crack detection plays a pivotal role in structural health monitoring. Deep convolutional neural networks (DCNN) provide a way to achieve image classification efficiently and accurately due to their powerful image processing ability. In this paper, we propose a semi-supervised learning method based on [...] Read more.
Crack detection plays a pivotal role in structural health monitoring. Deep convolutional neural networks (DCNN) provide a way to achieve image classification efficiently and accurately due to their powerful image processing ability. In this paper, we propose a semi-supervised learning method based on a DCNN to achieve anomaly crack detection. In the proposed method, the training set for the network only requires a small number of normal (non-crack) images but can achieve high detection accuracy. Moreover, the trained model has strong robustness in the condition of uneven illumination and evident crack difference. The proposed method is applied to the images of walls, bridges and pavements, and the results show that the detection accuracy comes up to 99.48%, 92.31% and 97.57%, respectively. In addition, the features of the neural network can be visualized to describe its working principle. This method has great potential in practical engineering applications. Full article
(This article belongs to the Special Issue Advance of Structural Health Monitoring in Civil Engineering)
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