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AI-Oriented Sensing for Civil Engineering Applications

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

Deadline for manuscript submissions: closed (20 July 2022) | Viewed by 9696

Special Issue Editors


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Guest Editor
Department of Civil Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
Interests: bridge engineering; infrastructure maintenance; artificial intelligence; image processing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Civil and Environmental Engineering, Yonsei University, 134 Shinchon-Dong Seodaemun-Gu, Seoul 120-749, Korea
Interests: strain rate effects; dynamic fracture of concrete; failure mode; irregular lattice model; rigid-body-spring network

Special Issue Information

Dear Colleagues,

In recent years, Artificial Intelligence (AI) and sensing technologies have developed significantly. However, there have been few efforts to combine AI and sensing, especially in the field of civil engineering, and there is much room for improvement. We also aim to spread AI-oriented sensing through further discovery of AI and sensing applications. Therefore, in this special issue, we invite applications of AI-oriented sensing in the field of civil engineering.

In this special issue, we would like to show the various possibilities for utilizing advanced technologies in civil engineering, and therefore, we would like to place the highest emphasis on novelty. Topics include, but are not limited to, the following:

  • Application of structural damage inspection and disaster investigation using image analysis with AI
  • Application of AI to evaluate deflection and structural damage from vibration measurement results
  • Prediction of future conditions or evaluation of current conditions from measurement results using AI
  • Applications of advanced sensors, such as optical fiber sensors, which promote the use of AI in civil engineering
  • Application of AI to analyze three-dimensional point cloud data in civil engineering
  • Application of data platforms that facilitate the use of measurement data with AI analysis.

Dr. Pang-jo Chun

Prof. Yun Mook Lim
Guest Editors

Manuscript Submission Information

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Published Papers (4 papers)

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Research

20 pages, 28032 KiB  
Article
Novel Methodology to Recover Road Surface Height Maps from Illuminated Scene through Convolutional Neural Networks
by Gonzalo de León, Julien Cesbron, Philippe Klein, Pietro Leandri and Massimo Losa
Sensors 2022, 22(17), 6603; https://doi.org/10.3390/s22176603 - 01 Sep 2022
Cited by 3 | Viewed by 1140
Abstract
Road surface properties have a major impact on pavement’s life service conditions. Nowadays, contactless techniques are widely used to monitor road surfaces due to their portability and high precision. Among the different possibilities, laser profilometers are widely used, even though they have two [...] Read more.
Road surface properties have a major impact on pavement’s life service conditions. Nowadays, contactless techniques are widely used to monitor road surfaces due to their portability and high precision. Among the different possibilities, laser profilometers are widely used, even though they have two major drawbacks: spatial information is missed and the cost of the equipment is considerable. The scope of this work is to show the methodology used to develop a fast and low-cost system using images taken with a commercial camera to recover the height information of the road surface using Convolutional Neural Networks. Hence, the dataset was created ad hoc. Based on photometric theory, a closed black-box with four light sources positioned around the surface sample was built. The surface was provided with markers in order to link the ground truth measurements carried out with a laser profilometer and their corresponding intensity values. The proposed network was trained, validated and tested on the created dataset. Three loss functions where studied. The results showed the Binary Cross Entropy loss to be the most performing and the best overall on the reconstruction task. The methodology described in this study shows the feasibility of a low-cost system using commercial cameras based on Artificial Intelligence. Full article
(This article belongs to the Special Issue AI-Oriented Sensing for Civil Engineering Applications)
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19 pages, 10374 KiB  
Article
Study on Accuracy Improvement of Slope Failure Region Detection Using Mask R-CNN with Augmentation Method
by Shiori Kubo, Tatsuro Yamane and Pang-jo Chun
Sensors 2022, 22(17), 6412; https://doi.org/10.3390/s22176412 - 25 Aug 2022
Cited by 6 | Viewed by 1551
Abstract
We proposed an automatic detection method of slope failure regions using a semantic segmentation method called Mask R-CNN based on a deep learning algorithm to improve the efficiency of damage assessment in the event of slope failure disaster. There is limited research on [...] Read more.
We proposed an automatic detection method of slope failure regions using a semantic segmentation method called Mask R-CNN based on a deep learning algorithm to improve the efficiency of damage assessment in the event of slope failure disaster. There is limited research on detecting landslides by deep learning, and the lack of training data is an important issue to be resolved, as aerial photographs are not taken with sufficient frequency during a disaster. This study attempts to use CutMix-based augmentation to improve detection accuracy. We also compare the detection results obtained by augmentation of multiple patterns. In the comparison of the not augmented data case, the recall increased by 0.186 in the case using the augmented data with the shape of the slope failure region maintained. When the image data was augmented while maintaining the shape of the slope failure region, the recall score indicated the low oversights in the prediction result is 0.701. This is an increase of 0.186 compared to the case where no augmentation was performed. In addition, the F1 score was 0.740, this also increased by 0.139, and high values were obtained for other indicators. Therefore, the method proposed in this study is greatly useful for grasping slope failure regions because of the detection with high accuracy, as described above. Full article
(This article belongs to the Special Issue AI-Oriented Sensing for Civil Engineering Applications)
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24 pages, 5214 KiB  
Article
A Rail-Temperature-Prediction Model Based on Machine Learning: Warning of Train-Speed Restrictions Using Weather Forecasting
by Sunguk Hong, Cheoljeong Park and Seongjin Cho
Sensors 2021, 21(13), 4606; https://doi.org/10.3390/s21134606 - 05 Jul 2021
Cited by 10 | Viewed by 3998
Abstract
Predicting the rail temperature of a railway system is important for establishing a rail management plan against railway derailment caused by orbital buckling. The rail temperature, which is directly responsible for track buckling, is closely related to air temperature, which continuously increases due [...] Read more.
Predicting the rail temperature of a railway system is important for establishing a rail management plan against railway derailment caused by orbital buckling. The rail temperature, which is directly responsible for track buckling, is closely related to air temperature, which continuously increases due to global warming effects. Moreover, railway systems are increasingly installed with continuous welded rails (CWRs) to reduce train vibration and noise. Unfortunately, CWRs are prone to buckling. This study develops a reliable and highly accurate novel model that can predict rail temperature using a machine learning method. To predict rail temperature over the entire network with high-prediction performance, the weather effect and solar effect features are used. These features originate from the analysis of the thermal environment around the rail. Precisely, the presented model has a higher performance for predicting high rail temperature than other models. As a convenient structural health-monitoring application, the train-speed-limit alarm-map (TSLAM) was also proposed, which visually maps the predicted rail-temperature deviations over the entire network for railway safety officers. Combined with TSLAM, our rail-temperature prediction model is expected to improve track safety and train timeliness. Full article
(This article belongs to the Special Issue AI-Oriented Sensing for Civil Engineering Applications)
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18 pages, 4320 KiB  
Article
Rock Particle Motion Information Detection Based on Video Instance Segmentation
by Man Chen, Maojun Li, Yiwei Li and Wukun Yi
Sensors 2021, 21(12), 4108; https://doi.org/10.3390/s21124108 - 15 Jun 2021
Cited by 3 | Viewed by 2020
Abstract
The detection of rock particle motion information is the basis for revealing particle motion laws and quantitative analysis. Such a task is crucial in guiding engineering construction, preventing geological disasters, and verifying numerical models of particles. We propose a machine vision method based [...] Read more.
The detection of rock particle motion information is the basis for revealing particle motion laws and quantitative analysis. Such a task is crucial in guiding engineering construction, preventing geological disasters, and verifying numerical models of particles. We propose a machine vision method based on video instance segmentation (VIS) to address the motion information detection problem in rock particles under a vibration load. First, we designed a classification loss function based on Arcface loss to improve the Mask R-CNN. This loss function introduces an angular distance based on SoftMax loss that distinguishes the objects and backgrounds with higher similarity. Second, this method combines the abovementioned Mask R-CNN and Deep Simple Online and Real-time Tracking (Deep SORT) to perform rock particle detection, segmentation, and tracking. Third, we utilized the equivalent ellipse characterization method for segmented particles, integrating with the proportional calibration algorithm to test the translation and detecting the rotation by calculating the change in the angle of the ellipse’s major axis. The experimental results show that the improved Mask R-CNN obtains an accuracy of 93.36% on a self-created dataset and also has some advantages on public datasets. Combining the improved Mask R-CNN and Deep SORT could fulfill the VIS with a low ID switching rate while successfully detecting movement information. The average detection errors of translation and rotation are 5.10% and 14.49%, respectively. This study provides an intelligent scheme for detecting movement information of rock particles. Full article
(This article belongs to the Special Issue AI-Oriented Sensing for Civil Engineering Applications)
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