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Applied Sciences
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Application of Data Mining and Deep Learning in Tunnels
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Application of Data Mining and Deep Learning in Tunnels

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

Deadline for manuscript submissions: closed (30 December 2022) | Viewed by 9070

Special Issue Editors

Prof. Dr. Dongming Zhang

E-Mail Website
Guest Editor
Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China
Interests: machine learning; big data in tunneling; tunnel mechanics
Special Issues, Collections and Topics in MDPI journals
Dr. Mingliang Zhou

E-Mail Website
Guest Editor
Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China
Interests: neural network model; 3D computer vision; numerical modeling; geotechnical engineering; distributed computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With ongoing digitalization in the tunneling industry, smart sensing, building information modeling, and artificial intelligence technologies can address various challenges in different phases of the tunneling lifecycle. Increasing overall amounts of tunneling data (big data), combined with the fast-growing computing capabilities, leads to a sharp increase in the successful application of data mining and deep learning techniques in tunneling. The way of gathering geological data in tunnel project surveying, the application of tunnel design analyses, the optimized construction processes based on on-site monitoring data, and the automated inspection and maintenance of the tunnels in operation have provided exciting opportunities to improve the design, construction, and operational processes in tunneling.

This special issue seeks contributions to the latest development, in theory, models, applications, and case studies using smart sensing, data mining and deep learning techniques in tunneling practice. New insights into the scientific knowledge or engineering practice in underground construction practice are also welcomed if they aspire to adopt the intelligent data analytics and deep learning to advance the tunneling practice.

Prof. Dr. Dongming Zhang
Dr. Mingliang Zhou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data mining
  • deep learning
  • smart sensing
  • building information modeling
  • automated inspection

Published Papers (5 papers)

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Research

20 pages, 5928 KiB  
Article
Soil Classification by Machine Learning Using a Tunnel Boring Machine’s Operating Parameters
by Tae-Ho Kang, Soon-Wook Choi, Chulho Lee and Soo-Ho Chang
Appl. Sci. 2022, 12(22), 11480; https://doi.org/10.3390/app122211480 - 11 Nov 2022
Cited by 6 | Viewed by 1808
Abstract
This study predicted soil classification using data gathered during the operation of an earth-pressure-balance-type tunnel boring machine (TBM). The prediction methodology used machine learning to find relationships between the TBM’s operating parameters which are monitored continuously during excavation, and the engineering characteristics of [...] Read more.
This study predicted soil classification using data gathered during the operation of an earth-pressure-balance-type tunnel boring machine (TBM). The prediction methodology used machine learning to find relationships between the TBM’s operating parameters which are monitored continuously during excavation, and the engineering characteristics of the ground which are only available from prior geotechnical investigation. Classification criteria were set using the No. 200 sieve pass rate and N-value and employed classification algorithms that used data for six operating parameters (penetration rate, thrust force, cutterhead torque, screw torque, screw revolution speed, and earth pressure). The results of the ensemble model (i.e., AdaBoost, gradient boosting, XG boosting, and Light GBM), decision tree, and SVM model were examined. As a result, the decision tree and AdaBoost models showed accuracy values of 0.759 to 0.879 in the first and second classification steps, but with poor precision and recall values of around 0.6. In contrast, the gradient boosting, XG boosting, Light GBM, and support vector models all showed excellent performance, with accuracy values over 0.90, and strong precision and recall values. Comparing the performance and the speed of learning using the same PC found Light GBM which showed both excellent learning performance and speed to be a suitable model for predicting soil classification using TBM operating data. The classification model developed here is expected to help guide excavation in sections of ground that lack prior geotechnical information. Full article
(This article belongs to the Special Issue Application of Data Mining and Deep Learning in Tunnels)
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18 pages, 3410 KiB  
Article
Calculation of Nonlimit Active Earth Pressure against Rigid Retaining Wall Rotating about Base
by Zeyue Wang, Xinxi Liu and Weiwei Wang
Appl. Sci. 2022, 12(19), 9638; https://doi.org/10.3390/app12199638 - 26 Sep 2022
Cited by 4 | Viewed by 1582
Abstract
A retaining wall with sandy fill was considered as the research object in order to study the nonlimiting active earth pressure under the rotation about the base (RB mode). Rankine’s and Coulomb’s earth pressure theories are no longer applicable to the above conditions [...] Read more.
A retaining wall with sandy fill was considered as the research object in order to study the nonlimiting active earth pressure under the rotation about the base (RB mode). Rankine’s and Coulomb’s earth pressure theories are no longer applicable to the above conditions (RB mode and nonlimiting active earth pressure). In order to improve the traditional earth pressure calculation methods (Rankine and Coulomb), a calculation method using curvilinear thin layer elements is presented with overall considerations of wall displacement, soil arching effect, and friction angle exertion coefficient to deduce the nonlimit active earth pressure under RB. Additionally, the calculation results were in good agreement with model test data (from Fang and Smita). Moreover, a parametric analysis was carried out. It was revealed that the developed value of the shear strength decreased with the depth, and the active earth pressure distribution curve was linear and nonlinear in the upper and lower halves, respectively. Full article
(This article belongs to the Special Issue Application of Data Mining and Deep Learning in Tunnels)
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18 pages, 19173 KiB  
Article
Accurate Prediction of Tunnel Face Deformations in the Rock Tunnel Construction Process via High-Granularity Monitoring Data and Attention-Based Deep Learning Model
by Mingliang Zhou, Zhenhua Xing, Cong Nie, Zhunguang Shi, Bo Hou and Kang Fu
Appl. Sci. 2022, 12(19), 9523; https://doi.org/10.3390/app12199523 - 22 Sep 2022
Cited by 3 | Viewed by 1765
Abstract
Monitoring and predicting the deformation of surrounding rocks in the rock tunnel construction process is of great significance. This study implemented a wireless sensor network (WSN), including gateway transmission, relay point, and sensor nodes, to obtain high granularity deformation data during construction. A [...] Read more.
Monitoring and predicting the deformation of surrounding rocks in the rock tunnel construction process is of great significance. This study implemented a wireless sensor network (WSN), including gateway transmission, relay point, and sensor nodes, to obtain high granularity deformation data during construction. A transformer model is proposed, which considers the construction sequence into the positional embedding and has an attention module to deeply learn the high dimensionality correlation between the nearby deformation data and the tunnel face deformation. The attention-enhanced LSTM model and the LSTM model are also constructed to compare them with the performance of the transformer model. A site study conducted on a shallow buried tunnel section suggested an excellent performance of the proposed WSN system. The transformer model shows the best performance in terms of the model prediction results, which can extract more information from the time sequence data than the attention-enhanced LSTM and LSTM models. The proposed system has great value as guidance and reference for the construction of rock tunnel projects in complex and unfavourable geological conditions. Full article
(This article belongs to the Special Issue Application of Data Mining and Deep Learning in Tunnels)
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26 pages, 8332 KiB  
Article
3D Point Cloud Generation Based on Multi-Sensor Fusion
by Yulong Han, Haili Sun, Yue Lu, Ruofei Zhong, Changqi Ji and Si Xie
Appl. Sci. 2022, 12(19), 9433; https://doi.org/10.3390/app12199433 - 20 Sep 2022
Cited by 5 | Viewed by 1757
Abstract
Traditional precise engineering surveys adopt manual static, discrete observation, which cannot meet the dynamic, continuous, high-precision and holographic fine measurements required for large-scale infrastructure construction, operation and maintenance, where mobile laser scanning technology is becoming popular. However, in environments without GNSS signals, it [...] Read more.
Traditional precise engineering surveys adopt manual static, discrete observation, which cannot meet the dynamic, continuous, high-precision and holographic fine measurements required for large-scale infrastructure construction, operation and maintenance, where mobile laser scanning technology is becoming popular. However, in environments without GNSS signals, it is difficult to use mobile laser scanning technology to obtain 3D data. We fused a scanner with an inertial navigation system, odometer and inclinometer to establish and track mobile laser measurement systems. The control point constraints and Rauch-Tung-Striebel filter smoothing were fused, and a 3D point cloud generation method based on multi-sensor fusion was proposed. We verified the method based on the experimental data; the average deviation of positioning errors in the horizontal and elevation directions were 0.04 m and 0.037 m, respectively. Compared with the stop-and-go mode of the Amberg GRP series trolley, this method greatly improved scanning efficiency; compared with the method of generating a point cloud in an absolute coordinate system based on tunnel design data conversion, this method improved data accuracy. It effectively avoided the deformation of the tunnel, the sharp increase of errors and more accurately and quickly processed the tunnel point cloud data. This method provided better data support for subsequent tunnel analysis such as 3D display, as-built surveying and disease system management of rail transit tunnels. Full article
(This article belongs to the Special Issue Application of Data Mining and Deep Learning in Tunnels)
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18 pages, 3070 KiB  
Article
Multifactor Uncertainty Analysis of Construction Risk for Deep Foundation Pits
by Wei Zhang, Zhen Huang, Jiabing Zhang, Ruifu Zhang and Shaokun Ma
Appl. Sci. 2022, 12(16), 8122; https://doi.org/10.3390/app12168122 - 13 Aug 2022
Cited by 6 | Viewed by 1403
Abstract
As it is affected by many uncertain factors, the construction risk of deep foundation subway station pits involves fuzzy and random uncertainties. Considering the fuzzy and random uncertainties involved in risk evaluation, an improved fuzzy comprehensive evaluation method combining a triangular cloud model [...] Read more.
As it is affected by many uncertain factors, the construction risk of deep foundation subway station pits involves fuzzy and random uncertainties. Considering the fuzzy and random uncertainties involved in risk evaluation, an improved fuzzy comprehensive evaluation method combining a triangular cloud model and the probability density function (PDF) is proposed in this study. First, with reference to the actual situation of deep foundation pit construction, the sources of construction risk are identified, and a construction risk evaluation index system is established. Second, the Delphi method is used to analyse the importance of each index of the evaluation object in order to obtain the evaluation data. The fuzzy best worst method (FBWM) is used to calculate the weight of the evaluation indices. Then, the triangular cloud model is used to represent the risk grade membership function. In addition, the fuzzy comprehensive evaluation method is used to comprehensively evaluate the construction risk of deep foundation pits. The fuzzy comprehensive evaluation vector is obtained for the indices possibility (P) and loss (C), and the weighted average value of the vector’s risk grade is calculated. Finally, probability analysis is carried out using PDF to determine the risk grade of P and C, and thus, to determine the risk grade of deep foundation pit construction. This method optimises the risk evaluation process of deep foundation pit construction and realises the visualisation of the comprehensive evaluation results, making the risk evaluation process transparent and convenient for use by risk analysts. This method is applied to predict the construction risk grade of a deep foundation pit project in Nanning, China, and the prediction results are consistent with the actual situation. Full article
(This article belongs to the Special Issue Application of Data Mining and Deep Learning in Tunnels)
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