Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.1
3.8
Journals
Buildings
Special Issues
Application of Machine Learning in Structural Engineering in Construction
share announcement

Application of Machine Learning in Structural Engineering in Construction

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 5594

Special Issue Editors

Dr. Xinyu Zhao

E-Mail Website
Guest Editor
State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou 510640, China
Interests: emerging concrete materials & structures; structural fire safety; seismic engineering; structural modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Jinjun Xu

E-Mail Website
Guest Editor
College of Civil Engineering, Nanjing Tech University, Nanjing 211816, China
Interests: machine learning; numerical modeling; civil engineering materials; dynamic analysis; sustainable construction; composite structures
Dr. Yong Yu

E-Mail
Guest Editor
School of Environment and Civil Engineering, Dongguan University of Technology, Dongguan 523808, China
Interests: concrete recycling technology; machine learning; life cycle assessment
Dr. Yunchao Tang

E-Mail Website
Guest Editor
College of Urban and Rural Construction, Zhongkai University of Agriculture and Engineering, Guangzhou, China
Interests: computer vision; field robotics; frp composites; recycled concrete; mechanical performance of steel structures; computer vision; 3d reconstruction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is currently transitioning from research to deployment. Specifically, AI has gone through shallow learning stages from "reasoning" through "knowledge" to "learning". Recently, it has entered a new era of deep learning development, driven by information technology with the themes of algorithms, computing power, and mega data and has realized cross-border integration of increasingly profound and sophisticated information, such as text, images, videos, and other categorical variables.

In civil engineering (CE), the development of AI-related technologies represented by machine learning, computer vision and intelligent robotics is also on the rise. This has injected new vitality into the ancient discipline of CE. In doing so, these new technologies will inevitably bring unforeseen opportunities as well as challenges, providing new horizons for researchers and practitioners who are pursuing this avenue for new values and solutions. However, in any case, these technologies must be CE centric, which can ensure their in-depth and long-term development.

By exploring the intersection and integration of these technologies with CE, this Special Issue is dedicated to demonstrating the possibilities for leveraging machine learning, computer vision, and robotics in CE, including, but not limited to, the following wide range of topics:

  • Performance prediction and evaluation based on machine learning
  • Improvement and optimization of machine learning algorithms in CE
  • Challenges and solutions of machine learning in CE applications
  • Computer automatic visual recognition for CE
  • Deep Learning applied for structural control and structural health monitoring
  • Application of 3D point cloud data in Civil Engineering
  • Robotic automatic construction and building technology
  • Ingenious applications of robots in CE
  • Improvement of deep learning algorithms for scene-specific defect detection

Dr. Xinyu Zhao
Dr. Jinjun Xu
Dr. Yong Yu
Dr. Yunchao Tang
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. Buildings is an international peer-reviewed open access monthly 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 2600 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

  • artificial intelligence
  • machine learning
  • computer vision
  • intelligent robotics
  • deep learning
  • structural health monitoring and optimization
  • structural vibration control
  • intelligent construction

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

25 pages, 8451 KiB  
Article
Computer-Vision and Machine-Learning-Based Seismic Damage Assessment of Reinforced Concrete Structures
by Yang Xu, Yi Li, Xiaohang Zheng, Xiaodong Zheng and Qiangqiang Zhang
Buildings 2023, 13(5), 1258; https://doi.org/10.3390/buildings13051258 - 11 May 2023
Cited by 9 | Viewed by 2077
Abstract
Seismic damage assessment of reinforced concrete (RC) structures is a vital issue for post-earthquake evaluation. Conventional onsite inspection depends greatly on subjective judgments and engineering experiences of human inspectors, and the efficiency is limited to large-scale urban areas. This study proposes a computer-vision [...] Read more.
Seismic damage assessment of reinforced concrete (RC) structures is a vital issue for post-earthquake evaluation. Conventional onsite inspection depends greatly on subjective judgments and engineering experiences of human inspectors, and the efficiency is limited to large-scale urban areas. This study proposes a computer-vision and machine-learning-based seismic damage assessment framework for RC structures. A refined Park-Ang model is built to express the coupled effects of structural ductility and energy dissipation, which reflects the nonlinear seismic damage accumulation and generates a synthetical seismic damage indicator within 0~1 using hysteretic curve data. A deep neural network is established to regress the damage indicator using damage-related and design-related parameters as inputs. The results show that the correlation coefficients between the predicted and actual seismic damage index exceed 0.98, and the predicted seismic damage index is unbiased and stable without overfitting. Furthermore, the effectiveness, robustness, and generalization ability of the proposed method are verified. Full article
(This article belongs to the Special Issue Application of Machine Learning in Structural Engineering in Construction)
►▼ Show Figures

Figure 1

18 pages, 5557 KiB  
Article
An Improved Equation for the Bearing Capacity of Concrete-Filled Steel Tube Concrete Short Columns Based on GPR
by Wei Ding and Suizi Jia
Buildings 2023, 13(5), 1226; https://doi.org/10.3390/buildings13051226 - 06 May 2023
Cited by 2 | Viewed by 1212
Abstract
The determination of the bearing capacity prediction model of concrete-filled steel tubular columns is a key issue in the structural design of prefabricated buildings, which directly relates to the stability and safety of prefabricated buildings. The purpose of this paper is to study [...] Read more.
The determination of the bearing capacity prediction model of concrete-filled steel tubular columns is a key issue in the structural design of prefabricated buildings, which directly relates to the stability and safety of prefabricated buildings. The purpose of this paper is to study the bearing capacity model of concrete-filled steel tubular columns, and propose an explicit formula based on the Gaussian process regression algorithm to calculate the bearing capacity. In order to solve the problem of low accuracy of the traditional empirical bearing capacity model, this paper first proposes a more accurate bearing capacity prediction model based on Gaussian process regression algorithm to automatically learn and capture the characteristics of 122 groups of test data; the paper then determines the function of high sensitivity parameters and section influence parameters through the established bearing capacity prediction model, and this process gives the display formula. Compared with the implicit formula given by a machine learning model, the explicit formula proposed in this paper is more suitable for practical engineering design. In order to verify the validity of the formula, we generated the bearing capacity data through the proposed formula based on the test data and used the descriptive statistical method to verify. The results show that the proposed formula is superior to other existing methods, the error between the data generated by the proposed formula and the test data is smaller, and its accuracy reaches 93.73%, which is more suitable for calculating the bearing capacity of concrete-filled steel tubes with different cross sections. Full article
(This article belongs to the Special Issue Application of Machine Learning in Structural Engineering in Construction)
►▼ Show Figures

Figure 1

17 pages, 8866 KiB  
Article
DNN-Based Estimation of the Maximum Lateral Flange Moments of Horizontally Curved I-Girder Bridges
by Seongbin Ryu, Jeonghwa Lee and Young Jong Kang
Buildings 2023, 13(2), 317; https://doi.org/10.3390/buildings13020317 - 20 Jan 2023
Viewed by 1109
Abstract
Horizontally curved I-girder bridges are known to be complex. Bending and torsion forces are imposed on the bridges owing to their shapes with initial curvatures. This torsion is a combination of pure and warping forces. The horizontally curved I-girder is significantly affected by [...] Read more.
Horizontally curved I-girder bridges are known to be complex. Bending and torsion forces are imposed on the bridges owing to their shapes with initial curvatures. This torsion is a combination of pure and warping forces. The horizontally curved I-girder is significantly affected by warping behavior, which decreases the bending rigidity of its member. To investigate the warping behavior of the horizontally curved I-girder bridges a finite element analysis (FEA) must be performed. In this study, an FEA was performed to investigate the warping torsional behavior of a horizontally curved I-girder bridge, and a structural response database was obtained. Based on the database, the least absolute shrinkage and selection operator was employed to select features affecting the warping behavior. Subsequently, deep neural network models were trained with selected features for an input layer and maximum lateral flange moment data for an output layer. Several models were constructed and compared according to the number of hidden layers and neurons, and the model with the highest performance was proposed. Finally, it was confirmed that the estimated lateral flange moments computed by the proposed model showed a good correlation with the FEA results. Full article
(This article belongs to the Special Issue Application of Machine Learning in Structural Engineering in Construction)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop