Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Artificial Intelligence in Civil Engineering: Latest Advances and Prospects
applsci-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Artificial Intelligence in Civil Engineering: Latest Advances and Prospects

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

Deadline for manuscript submissions: 20 July 2025 | Viewed by 4287

Special Issue Editor

Dr. Georgios N. Aretoulis

E-Mail Website
Guest Editor
Department of Civil Engineering, Aristotle University of Thessaloniki, 54 124 Thessaloniki, Greece
Interests: construction budgeting and cost monitoring and control; information technologies and expert systems in construction management; cost and time prediction models
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, there are notable technological advancements and innovations that produce significant changes and transformations, providing serious prospects along with challenges, risks, and opportunities. Innovation is incorporated into various sectors such as the engineering field. This integration of new tools, techniques, methodologies, and approaches is drastically changing the traditional engineering practice, transforming both processes and procedures. Essentially, scientific breakthroughs and leaps revolutionize all project life cycles. In this Special Issue, the focus is on Artificial Intelligence (AI). The synergies of AI and civil engineering facilitate a reengineering of project management.

All project phases, from planning and construction, to operation, maintenance, and finally removal of the project, are taken into consideration. The aim is also to apply AI early within the business planning of project enterprises to achieve sustainable development and environmentally friendly projects with minimum externalities. Case studies of a plethora of project types are welcomed, including but not limited to building projects, highway/road projects, geotechnical projects, and hydraulic projects, both private and state owned.

It is interesting to highlight the established or potential synergies among AI and project management sectors such as human resource, scope, time, cost, risk, quality, integration, procurement, communication, earned value, and health and safety management.

The following topics in association with artificial intelligence could complement and elaborate on the previously mentioned areas of interest. More specifically: project cost and time, project quality, simulation, mathematical programming, digital twins, indoor environmental quality, user comfort and well-being, energy efficiency, building and project performance, intelligent design—urban planning, deep learning with satellite and aerial imagery, optimization, fuzzy decision making, health and safety, structural monitoring, image classification, artificial neural networks, decision support automation, machine learning, and last but not least blockchain technologies.

I look forward to receiving your contributions.

Dr. Georgios N. Aretoulis
Guest Editor

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

  • civil engineering
  • artificial intelligence
  • artificial neural networks
  • expert systems
  • digital twins
  • blockchain
  • machine learning
  • optimization
  • decision support automation
  • quality control
  • road infrastructure

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 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

26 pages, 44793 KiB  
Article
3D Reconstruction of Asphalt Pavement Macro-Texture Based on Convolutional Neural Network and Monocular Image Depth Estimation
by Xinliang Liu and Chao Yin
Appl. Sci. 2025, 15(9), 4684; https://doi.org/10.3390/app15094684 - 23 Apr 2025
Viewed by 191
Abstract
The 3D reconstruction of asphalt pavement macrotexture holds significant engineering value for pavement quality assessment and performance monitoring. However, conventional 3D reconstruction methods face challenges, such as high equipment costs and operational complexity, limiting their widespread application in engineering practice. Meanwhile, current deep [...] Read more.
The 3D reconstruction of asphalt pavement macrotexture holds significant engineering value for pavement quality assessment and performance monitoring. However, conventional 3D reconstruction methods face challenges, such as high equipment costs and operational complexity, limiting their widespread application in engineering practice. Meanwhile, current deep learning-based monocular image reconstruction for pavement texture remains in its early stages. To address these technical limitations, this study systematically prepared four types of asphalt mixture specimens (AC, SMA, OGFC, and PA) with a total of 14 gradations. High-precision equipment was used to simultaneously capture 2D RGB images and 3D RGB-D point cloud data of the surface texture. An innovative multi-scale feature fusion CNN model was developed based on an encoder–decoder architecture, along with an optimized training strategy for model parameters. For performance evaluation, multiple metrics were employed, including root mean square error (RMSE = 0.491), relative error (REL = 0.102), and accuracy at different thresholds (δ = 1/2/3: 0.931, 0.979, 0.990). The results demonstrate strong correlations between the reconstructed texture’s mean texture depth (MTD) and friction coefficient (f8) with actual measurements (0.913 and 0.953, respectively), outperforming existing methods. This confirms that the proposed CNN model achieves precise 3D reconstruction of asphalt pavement macrotexture, effectively supporting skid resistance evaluation. To validate engineering applicability, field tests were conducted on pavements with various gradations. The model exhibited excellent robustness under different conditions. Furthermore, based on extensive field data, this study established a quantitative relationship between MTD and friction coefficient, developing a more accurate pavement skid resistance evaluation system to support maintenance decision-making. Full article
(This article belongs to the Special Issue Artificial Intelligence in Civil Engineering: Latest Advances and Prospects)
►▼ Show Figures

Figure 1

15 pages, 5688 KiB  
Article
Genetic Algorithm-Based Model Updating in a Real-Time Digital Twin for Steel Bridge Monitoring
by Raihan Rahmat Rabi and Giorgio Monti
Appl. Sci. 2025, 15(8), 4074; https://doi.org/10.3390/app15084074 - 8 Apr 2025
Viewed by 290
Abstract
The integration of digital twin technology with structural health monitoring (SHM) is revolutionizing the assessment and maintenance of critical infrastructure, particularly bridges. Digital twins—virtual, data-driven replicas of physical structures—enable real-time monitoring by continuously synchronizing sensor data with computational models. This study presents the [...] Read more.
The integration of digital twin technology with structural health monitoring (SHM) is revolutionizing the assessment and maintenance of critical infrastructure, particularly bridges. Digital twins—virtual, data-driven replicas of physical structures—enable real-time monitoring by continuously synchronizing sensor data with computational models. This study presents the development of a real-time digital twin for a three-span steel railway bridge, utilizing a high-fidelity finite element (FE) model built using OpenSeesPy v 3.5 and instrumented with 18 strategically placed accelerometers. The dynamic properties of the bridge are extracted using Stochastic Subspace Identification (SSI), enabling an accurate estimation of modal parameters. To enhance the fidelity of the digital twin, a genetic algorithm-based model-updating strategy is implemented, optimizing the steel elastic modulus to minimize discrepancies between measured and simulated frequencies and mode shapes. The results demonstrate a remarkable reduction in frequency errors (below 5%) and a significant improvement in modal shape correlation (MAC > 0.93 post-calibration), confirming the model’s ability to reflect the bridge’s true condition. This work underscores the potential of digital twins in predictive maintenance, early damage detection, and life-cycle management of bridge infrastructure, offering a scalable framework for real-time SHM in complex structural systems. Full article
(This article belongs to the Special Issue Artificial Intelligence in Civil Engineering: Latest Advances and Prospects)
►▼ Show Figures

Figure 1

28 pages, 9542 KiB  
Article
Reliability-Constrained Structural Design Optimization Using Visual Programming in Building Information Modeling (BIM) Projects
by Feyzullah Yavan and Reza Maalek
Appl. Sci. 2025, 15(3), 1025; https://doi.org/10.3390/app15031025 - 21 Jan 2025
Viewed by 1048
Abstract
Providing safe, environmentally conscious, and cost-effective designs is the primary duty of civil engineers. To this end, many different algorithms and methods have been developed in parallel with the progress of digital technologies over the past decades. Techniques such as AI-based Metaheuristic Algorithms [...] Read more.
Providing safe, environmentally conscious, and cost-effective designs is the primary duty of civil engineers. To this end, many different algorithms and methods have been developed in parallel with the progress of digital technologies over the past decades. Techniques such as AI-based Metaheuristic Algorithms (MAs), Reliability Analysis, and Building Information Modelling (BIM) are some of those methods that serve this purpose. The present study focuses on establishing a design optimization methodology by implementing the techniques in the open literature on one software environment to create a robust engineering and architectural workflow. The methodology involves multiple stages such as (i) creating parametric trusses employing Visual Programming (VP) software Dynamo (Version: 3.0.4); (ii) performing a First-order Reliability Method (FORM) analysis which includes a Finite Element Method (FEM) analysis as a Limit State Function (LSF); (iii) employing MAs to achieve optimum design variables under uncertain design constraints; (iv) testing the methodology with various real-word examples and scenarios; (v) creating an optimized model on Robot Structural Analysis 2024 (RSA) software in real time in the purpose of further adjustments. The results demonstrated that creating a design optimization workflow by utilizing a BIM environment can enhance the design process by easing the storing, sharing, and utilizing of design data by different branches capable of performing different complicated tasks successfully. Full article
(This article belongs to the Special Issue Artificial Intelligence in Civil Engineering: Latest Advances and Prospects)
►▼ Show Figures

Figure 1

19 pages, 3752 KiB  
Article
Slope Stability Prediction Using Principal Component Analysis and Hybrid Machine Learning Approaches
by Daxing Lei, Yaoping Zhang, Zhigang Lu, Hang Lin, Bowen Fang and Zheyuan Jiang
Appl. Sci. 2024, 14(15), 6526; https://doi.org/10.3390/app14156526 - 26 Jul 2024
Cited by 5 | Viewed by 1556
Abstract
Traditional slope stability analysis methods are time-consuming, complex, and cannot provide fast stability estimates when facing a large amount of slope cases. In this case, artificial neural networks (ANN) provide a better alternative. Based on the ANN, the particle swarm optimization (PSO) algorithm, [...] Read more.
Traditional slope stability analysis methods are time-consuming, complex, and cannot provide fast stability estimates when facing a large amount of slope cases. In this case, artificial neural networks (ANN) provide a better alternative. Based on the ANN, the particle swarm optimization (PSO) algorithm, and the principal component analysis (PCA) method, a novel PCA-PANN model is proposed. Then, a dataset of 307 slope cases covering a wide range of slope geometries and mechanical properties of geomaterial is developed. The hybrid machine learning model trained with the dataset is applied to the factor of safety (FoS) prediction of the actual slope, and three evaluation indicators are introduced to measure the prediction performance of the model. Finally, the sensitivity analysis of input parameters is carried out, and the slope protection strategy for different sensitive factors is proposed. The results show that this new model can quickly obtain the FoS and stable state of the slope without complex calculation, only by providing the relevant characteristic parameters. The correlation coefficient of the PCA-PANN model for slope stability analysis reaches more than 0.97. The sensitivity degree of influencing factors from large to small is slope angle, cohesion, pore pressure ratio, slope height, unit weight, and friction angle. Full article
(This article belongs to the Special Issue Artificial Intelligence in Civil Engineering: Latest Advances and Prospects)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop