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Artificial Intelligence Technologies for Structural Health Monitoring

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 42663

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Prof. Dr. Yun-Kyu An

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Guest Editor

Department of Architectural Engineering, Sejong University, Seoul 05006, Korea
Interests: structural health monitoring; nondestructive evaluation; smart structures; active sensing technologies; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Soojin Cho

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Guest Editor

Department of Civil Engineering, University of Seoul, Seoul 02504, Korea
Interests: civil engineering; deep learning; digital image processing; wireless sensor; system identification
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Dear Colleagues,

Recently, artificial intelligence (AI) technologies have emerged and been widely studied in the structural health monitoring (SHM) and nondestructive evaluation (NDE) fields. Although a number of SHM and NDE techniques have been developed, field data interpretation is still challenging. In reality, data interpretation highly depends on experts’ experience or judgement. However, expert-dependent data interpretation becomes more time-consuming and cumbersome as the amount of sensing data collected from a large target structure under harsh environments increases. Thus, there have recently been a number of trials to automate data interpretation. To investigate this research trend, we will focus on various AI technologies especially for SHM and NDE in this Special Issue.

The recent efforts and advances made for the comprehensive SHM and NDT of civil and mechanical structures are all welcomed in this Special Issue. The topics of interest for this Special Issue include but are not limited to the following:

Artificial Intelligence for SHM and NDE;
Deep learning for SHM and NDE;
Machine learning for SHM and NDE;
Structural health diagnosis and prognosis;
Computer vision;
Data fusion and automatic data analytics;
Robotic/UAV platform for structural inspection and preservation;
Computer-aided structural modeling;
Smart sensors and materials.

Prof. Yun-Kyu An
Prof. Soojin Cho
Guest Editors

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Keywords

artificial intelligence
deep learning
structural health monitoring
nondestructive evaluation
automatic data analytics
robotics
UAV
reliability
sensors
smart materials

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Related Special Issue

Recent Advances in Structural Health Monitoring and Nondestructive Testing in Civil Engineering in Applied Sciences (11 articles)

Published Papers (10 papers)

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Research

29 pages, 6893 KiB

Open AccessArticle

Low-Cost Sensors Accuracy Study and Enhancement Strategy

by Seyedmilad Komarizadehasl, Behnam Mobaraki, Haiying Ma, Jose-Antonio Lozano-Galant and Jose Turmo

Appl. Sci. 2022, 12(6), 3186; https://doi.org/10.3390/app12063186 - 21 Mar 2022

Cited by 17 | Viewed by 5560

Abstract

Today, low-cost sensors in various civil engineering sectors are gaining the attention of researchers due to their reduced production cost and their applicability to multiple nodes. Low-cost sensors also have the advantage of easily connecting to low-cost microcontrollers such as Arduino. A low-cost, reliable acquisition system based on Arduino technology can further reduce the price of data acquisition and monitoring, which can make long-term monitoring possible. This paper introduces a wireless Internet-based low-cost data acquisition system consisting of Raspberry Pi and several Arduinos as signal conditioners. This study investigates the beneficial impact of similar sensor combinations, aiming to improve the overall accuracy of several sensors with an unknown accuracy range. The paper then describes an experiment that gives valuable information about the standard deviation, distribution functions, and error level of various individual low-cost sensors under different environmental circumstances. Unfortunately, these data are usually missing and sometimes assumed in numerical studies targeting the development of structural system identification methods. A measuring device consisting of a total of 75 contactless ranging sensors connected to two microcontrollers (Arduinos) was designed to study the similar sensor combination theory and present the standard deviation and distribution functions. The 75 sensors include: 25 units of HC-SR04 (analog), 25 units of VL53L0X, and 25 units of VL53L1X (digital). Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Structural Health Monitoring)

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17 pages, 39030 KiB

Open AccessArticle

Vegetation Removal on 3D Point Cloud Reconstruction of Cut-Slopes Using U-Net

by Ying Wang and Ki-Young Koo

Appl. Sci. 2022, 12(1), 395; https://doi.org/10.3390/app12010395 - 31 Dec 2021

Cited by 6 | Viewed by 3104

Abstract

The 3D point cloud reconstruction from photos taken by an unmanned aerial vehicle (UAV) is a promising tool for monitoring and managing risks of cut-slopes. However, surface changes on cut-slopes are likely to be hidden by seasonal vegetation variations on the cut-slopes. This paper proposes a vegetation removal method for 3D reconstructed point clouds using (1) a 2D image segmentation deep learning model and (2) projection matrices available from photogrammetry. For a given point cloud, each 3D point of it is reprojected into the image coordinates by the projection matrices to determine if it belongs to vegetation or not using the 2D image segmentation model. The 3D points belonging to vegetation in the 2D images are deleted from the point cloud. The effort to build a 2D image segmentation model was significantly reduced by using U-Net with the dataset prepared by the colour index method complemented by manual trimming. The proposed method was applied to a cut-slope in Doam Dam in South Korea, and showed that vegetation from the two point clouds of the cut-slope at winter and summer was removed successfully. The M3C2 distance between the two vegetation-removed point clouds showed a feasibility of the proposed method as a tool to reveal actual change of cut-slopes without the effect of vegetation. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Structural Health Monitoring)

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12 pages, 5574 KiB

Open AccessArticle

Port Structure Inspection Based on 6-DOF Displacement Estimation Combined with Homography Formulation and Genetic Algorithm

by Jiyoung Min, Yuseok Bang, Hyuntae Bang and Haemin Jeon

Appl. Sci. 2021, 11(14), 6470; https://doi.org/10.3390/app11146470 - 13 Jul 2021

Cited by 4 | Viewed by 1937

Abstract

A vision sensor-based 6-DOF displacement evaluation method incorporating a genetic algorithm was proposed to monitor the critical defects of port infrastructure, such as deflection, slope, and slip. The 6-DOF behavior of the port structure, including subsidence, was estimated based on the specification of the target and fixed structures nearby. The method calculates the relative position of the target port structure and measures the movement of the structure over time. To improve the measurement accuracy, a genetic algorithm was used to adjust the intrinsic parameters that were previously estimated using the checkerboards. The results of measuring 6-DOF displacements based on the tuned intrinsic parameters confirmed that it has the potential to accurately measure the 6-DOF behavior of port facilities. The possibility of field application was examined through an artificial movement that was induced in the image of the port facility to create an arbitrary displacement between two points. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Structural Health Monitoring)

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13 pages, 3152 KiB

Open AccessArticle

Automated Vision-Based Crack Detection on Concrete Surfaces Using Deep Learning

by Rajagopalan-Sam Rajadurai and Su-Tae Kang

Appl. Sci. 2021, 11(11), 5229; https://doi.org/10.3390/app11115229 - 4 Jun 2021

Cited by 44 | Viewed by 4638

Abstract

Cracking in concrete structures affects performance and is a major durability problem. Cracks must be detected and repaired in time in order to maintain the reliability and performance of the structure. This study focuses on vision-based crack detection algorithms, based on deep convolutional neural networks that detect and classify cracks with higher classification rates by using transfer learning. The image dataset, consisting of two subsequent image classes (no-cracks and cracks), was trained by the AlexNet model. Transfer learning was applied to the AlexNet, including fine-tuning the weights of the architecture, replacing the classification layer for two output classes (no-cracks and cracks), and augmenting image datasets with random rotation angles. The fine-tuned AlexNet model was trained by stochastic gradient descent with momentum optimizer. The precision, recall, accuracy, and F₁ metrics were used to evaluate the performance of the trained AlexNet model. The accuracy and loss obtained through the training process were 99.9% and 0.1% at the learning rate of 0.0001 and 6 epochs. The trained AlexNet model accurately predicted 1998/2000 and 3998/4000 validation and test images, which demonstrated the prediction accuracy of 99.9%. The trained model also achieved precision, recall, accuracy, and F₁ scores of 0.99, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Structural Health Monitoring)

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13 pages, 6394 KiB

Open AccessArticle

Semantic Structure from Motion for Railroad Bridges Using Deep Learning

by Gun Park, Jae Hyuk Lee and Hyungchul Yoon

Appl. Sci. 2021, 11(10), 4332; https://doi.org/10.3390/app11104332 - 11 May 2021

Cited by 10 | Viewed by 2831

Abstract

Current maintenance practices consume significant time, cost, and manpower. Thus, a new technique for maintenance is required. Construction information technologies, including building information modeling (BIM), have recently been applied to the field to carry out systematic and productive planning, design, construction, and maintenance. Although BIM is increasingly being applied to new structures, its application to existing structures has been limited. To apply BIM to an existing structure, a three-dimensional (3D) model of the structure that accurately represents the as-is status should be constructed and each structural component should be specified manually. This study proposes a method that constructs a 3D model and specifies the structural component automatically using photographic data with a camera installed on an unmanned aerial vehicle. This procedure is referred to as semantic structure from motion because it constructs a 3D point cloud model together with semantic information. A validation test was carried out on a railroad bridge to validate the performance of the proposed system. The average precision, intersection over union, and BF scores were 80.87%, 66.66%, and 56.33%, respectively. The proposed method could improve the current scan-to-BIM procedure by generating the as-is 3D point cloud model by specifying the structural component automatically. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Structural Health Monitoring)

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12 pages, 5399 KiB

Open AccessArticle

Deep Learning-Based Automated Background Removal for Structural Exterior Image Stitching

by Myung Soo Kang and Yun-Kyu An

Appl. Sci. 2021, 11(8), 3339; https://doi.org/10.3390/app11083339 - 8 Apr 2021

Cited by 4 | Viewed by 3412

Abstract

This paper presents a deep learning-based automated background removal technique for structural exterior image stitching. In order to establish an exterior damage map of a structure using an unmanned aerial vehicle (UAV), a close-up vision scanning is typically required. However, unwanted background objects are often captured within the scanned digital images. Since the unnecessary background objects often cause serious distortion on the image stitching process, they should be removed. In this paper, the automated background removal technique using deep learning-based depth estimation is proposed. Based on the fact that the region of interest has closer working distance than the background ones from the camera, the background region within the digital images can be automatically removed using a deep learning-based depth estimation network. In addition, an optimal digital image selection based on feature matching-based overlap ratio is proposed. The proposed technique is experimentally validated using UAV-scanned digital images acquired from an in-situ high-rise building structure. The validation test results show that the optimal digital images obtained from the proposed technique produce the precise structural exterior map with computational cost reduction of 85.7%, while raw scanned digital images fail to construct the structural exterior map and cause serious stitching distortion. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Structural Health Monitoring)

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13 pages, 4408 KiB

Open AccessArticle

Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network

by Shuai Teng, Zongchao Liu, Gongfa Chen and Li Cheng

Appl. Sci. 2021, 11(2), 813; https://doi.org/10.3390/app11020813 - 16 Jan 2021

Cited by 61 | Viewed by 5654

Abstract

This paper compares the crack detection performance (in terms of precision and computational cost) of the YOLO_v2 using 11 feature extractors, which provides a base for realizing fast and accurate crack detection on concrete structures. Cracks on concrete structures are an important indicator for assessing their durability and safety, and real-time crack detection is an essential task in structural maintenance. The object detection algorithm, especially the YOLO series network, has significant potential in crack detection, while the feature extractor is the most important component of the YOLO_v2. Hence, this paper employs 11 well-known CNN models as the feature extractor of the YOLO_v2 for crack detection. The results confirm that a different feature extractor model of the YOLO_v2 network leads to a different detection result, among which the AP value is 0.89, 0, and 0 for ‘resnet18’, ‘alexnet’, and ‘vgg16’, respectively meanwhile, the ‘googlenet’ (AP = 0.84) and ‘mobilenetv2’ (AP = 0.87) also demonstrate comparable AP values. In terms of computing speed, the ‘alexnet’ takes the least computational time, the ‘squeezenet’ and ‘resnet18’ are ranked second and third respectively; therefore, the ‘resnet18’ is the best feature extractor model in terms of precision and computational cost. Additionally, through the parametric study (influence on detection results of the training epoch, feature extraction layer, and testing image size), the associated parameters indeed have an impact on the detection results. It is demonstrated that: excellent crack detection results can be achieved by the YOLO_v2 detector, in which an appropriate feature extractor model, training epoch, feature extraction layer, and testing image size play an important role. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Structural Health Monitoring)

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17 pages, 3246 KiB

Open AccessArticle

Automated Multiple Concrete Damage Detection Using Instance Segmentation Deep Learning Model

by Byunghyun Kim and Soojin Cho

Appl. Sci. 2020, 10(22), 8008; https://doi.org/10.3390/app10228008 - 12 Nov 2020

Cited by 60 | Viewed by 6297

Abstract

In many developed countries with a long history of urbanization, there is an increasing need for automated computer vision (CV)-based inspection to replace conventional labor-intensive visual inspection. This paper proposes a technique for the automated detection of multiple concrete damage based on a state-of-the-art deep learning framework, Mask R-CNN, developed for instance segmentation. The structure of Mask R-CNN, which consists of three stages (region proposal, classification, and segmentation) is optimized for multiple concrete damage detection. The optimized Mask R-CNN is trained with 765 concrete images including cracks, efflorescence, rebar exposure, and spalling. The performance of the trained Mask R-CNN is evaluated with 25 actual test images containing damage as well as environmental objects. Two types of metrics are proposed to measure localization and segmentation performance. On average, 90.41% precision and 90.81% recall are achieved for localization and 87.24% precision and 87.58% recall for segmentation, which indicates the excellent field applicability of the trained Mask R-CNN. This paper also qualitatively discusses the test results by explaining that the architecture of Mask R-CNN that is optimized for general object detection purposes, can be modified to detect long and slender shapes of cracks, rebar exposure, and efflorescence in further research. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Structural Health Monitoring)

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23 pages, 18188 KiB

Open AccessArticle

A Study on Data Pre-Processing and Accident Prediction Modelling for Occupational Accident Analysis in the Construction Industry

by Jae Yun Lee, Young Geun Yoon, Tae Keun Oh, Seunghee Park and Sang Il Ryu

Appl. Sci. 2020, 10(21), 7949; https://doi.org/10.3390/app10217949 - 9 Nov 2020

Cited by 26 | Viewed by 4931

Abstract

In the construction industry, it is difficult to predict occupational accidents because various accident characteristics arise simultaneously and organically in different types of work. Furthermore, even when analyzing occupational accident data, it is difficult to deduce meaningful results because the data recorded by the incident investigator are qualitative and include a wide variety of data types and categories. Recently, numerous studies have used machine learning to analyze the correlations in such complex construction accident data; however, heretofore the focus has been on predicting severity with various variables, and several limitations remain when deriving the correlations between features from various variables. Thus, this paper proposes a data processing procedure that can efficiently manipulate accident data using optimal machine learning techniques and derive and systematize meaningful variables to rationally approach such complex problems. In particular, among the various variables, the most influential variables are derived through methods such as clustering, chi-square, Cramer’s V, and predictor importance; then, the analysis is simplified by optimally grouping the variables. For accident data with optimal variables and elements, a predictive model is constructed between variables, using a support vector machine and decision-tree-based ensemble; then, the correlation between the dependent and independent variables is analyzed through an alluvial flow diagram for several cases. Therefore, a new processing procedure has been introduced in data preprocessing and accident prediction modelling to overcome difficulties from complex and diverse construction occupational accident data, and effective accident prevention is possible by deriving correlations of construction accidents using this process. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Structural Health Monitoring)

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22 pages, 5185 KiB

Open AccessArticle

Study on Prediction and Application of Initial Chord Elastic Modulus with Resonance Frequency Test of ASTM C 215

by Young Geun Yoon, HaJin Choi and Tae Keun Oh

Appl. Sci. 2020, 10(16), 5464; https://doi.org/10.3390/app10165464 - 7 Aug 2020

Viewed by 2664

Abstract

For accurate design, construction, and maintenance, it is important to identify the elastic modulus of concrete. This is usually achieved using a destructive test based on American Society for Testing and Materials (ASTM) C469. However, obtaining an appropriate static elastic modulus (Ec) requires many specimens, and the testing is difficult and time-consuming. Thus, a dynamic elastic modulus (Ed) is often obtained through a natural frequency for a specific size (e.g., the longitudinal (LT) or transverse (TR) mode) based on a resonance frequency test. However, this method uses a gradient at a very low-stress part of the stress–strain curve and assumes a completely elastic body. In fact, the initial chord elastic modulus (Ei) of the stress–strain curve in a concrete fracture test differs from the Ed, owing to the non-homogeneity and inelasticity of the concrete. The Ei of the experimental value may be more accurate. In this study, the Ei was predicted using machine learning methods for natural frequencies. The prediction accuracy for Ei was analyzed based on f1–f4, as calculated through the LT and TR modes. The predicted Ei had higher correlations with the actual Ec and compressive strength (fc) than Ed. Thus, more accurate prediction of concrete mechanical properties is possible. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Structural Health Monitoring)

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