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Special Issue: Advancements in Civil Engineering: Integrating AI, Digital Twins, and Industry 4.0 Innovations

2025-08-30

Summary

The civil engineering and construction sectors are undergoing a profound transformation, driven by the convergence of cyber-physical systems and the technologies of the 4th Industrial Revolution (Industry 4.0). Globally, the integration of Artificial Intelligence (AI), Digital Twins, Additive Manufacturing (3D Printing), Nanotechnology, and the Internet of Things (IoT) is moving from theoretical research to large-scale practical application.

In this dynamic context, nations like Vietnam are at a pivotal moment, making substantial strategic investments to adopt and master these key technologies. This national focus is creating a fertile ground for innovation, applying advanced computational and material sciences to solve long-standing challenges in infrastructure and urban development. We are witnessing these technologies being actively deployed in both academic research and high-impact, real-world applications.

This Special Issue aims to capture this global momentum by curating a collection of high-quality, original research articles and comprehensive review papers. We seek to document and analyze the most recent developments in the application of AI and other Industry 4.0 technologies within civil engineering.

The primary goal is to illustrate the tangible advantages these innovations bring, such as:

  • Accelerating project timelines from design to completion.
  • Enhancing structural safety, resilience, and sustainability.
  • Optimizing resource management, logistics, and operational efficiency.
  • Creating new pathways for smart materials, structures and autonomous systems.

We invite researchers, academics, and industry practitioners to submit their work and contribute to this timely and critical discourse.

Keywords

  • Machine Learning & Deep Learning
  • Computer Vision (CV)
  • Physics-Informed Neural Networks (PINNs)
  • Natural Language Processing (NLP)
  • Generative AI
  • Digital Twins
  • AI-augmented Building Information Modeling (BIM)
  • Robotics and Automation in construction
  • Optimization Algorithms
  • Additive Manufacturing
  • AI-driven Materials Science
  • IoT and Sensor Networks
  • Intelligent Transportation Systems (ITS)

Submission guidelines & Deadlines

Manuscript submission deadline: 30 August, 2026

Authors are invited to submit their manuscripts through the Journal of Science and Transport Technology, JSTT (E-ISSN: 2734-9950) online submission system at https://jstt.vn/index.php/en

All submissions must be original and may not be under review by any other journal. Manuscripts should be prepared according to the "Guide for Authors" available on the journal's website. All papers will be subject to a rigorous, double-blind peer-review process.

On behalf of the Guest Editorial Team

Assoc. Prof. Hai-Bang Ly

E-mail: banglh@utt.edu.vn

Special Issue: Modern Mechanics and Emerging Applications for Industry 4.0

2025-06-30

Summary

The convergence of mechanical sciences with digital technologies, smart materials, and intelligent systems is reshaping the foundation of modern engineering within the context of the Fourth Industrial Revolution (Industry 4.0). As the demand grows for smarter, faster, and more adaptive systems across the aerospace, automotive, biomedical, manufacturing, and infrastructure sectors, modern mechanics is evolving to address these interdisciplinary challenges.

Traditional paradigms in structural analysis, material modeling, and mechanical system design are being augmented by advancements in computational mechanics, additive manufacturing, artificial intelligence, and cyber-physical systems. These innovations enable the development of materials and structures with superior performance, functionality, and adaptability.

However, these emerging applications also introduce significant complexity regarding modeling fidelity, multi-scale interactions, real-time control, and data-driven optimization. Addressing such challenges necessitates a new generation of mechanical modeling techniques, synergistic integration with information technologies, and robust cross-disciplinary collaboration.

This Special Issue invites high-quality contributions that explore these advancements. While the issue welcomes selected extended papers from the 2nd International Conference on Modern Mechanics and Applications (ICOMMA2025) (Ho Chi Minh City, Vietnam, October 17–19, 2025), it is strictly open to external submissions to ensure broad participation and global impact.

All manuscripts must be submitted through the journal’s online system and will undergo a rigorous peer-review process, adhering to the journal’s high standards. Both conference-extended and independent submissions will be evaluated based on originality, technical depth, and relevance to the Special Issue's theme.

Keywords:

  • Modern mechanics
  • Computational mechanics
  • Mechanics 4.0
  • Multiphysics modeling
  • Smart materials and structures
  • Additive manufacturing
  • Data-driven modeling
  • Machine learning in mechanics
  • Bioinspired structures
  • Cyber-physical systems
  • AI-driven design

Deadline: 30/06/2026

Chief Guest Editor:

Co-Chief Guest Editor:

  • Name: Assoc.Prof. Dr. Vu Hoai Nam
  • Affiliation: Faculty of Civil Engineering, University of Transport Technology, Hanoi, Vietnam
  • E-mail: hoainam.vu@utt.edu.vn 

NOTICE 1-2025

2025-02-05

We are pleased to inform that the Journal of Science and Transport Technology (JSTT) (E-ISSN: 2734-9950) of the University of Transport Technology permitted by the Ministry of Information and Communications with License for press activities No 399/GP-BTTTT date 29th June 2021,  has finally been accepted and indexed in SCOPUS. Please find the link below for scopus progress and acceptance:   https://suggestor.step.scopus.com/progressTracker/?trackingID=E0847093E172D6F4 

Special Issue: Big Data, AI, and Machine Learning in Transportation

2021-11-16

Summary

It is predicted that Big Data will profoundly impact the transportation and logistics sector, which accounts for around 15% of global GDP. Researchers in the transportation field are no strangers to the challenges provided by big data. Model and observation resolution developments, together with the introduction of unique observation tools, have all led to the heightened sharpness of challenges. If big data difficulties are defined as the capacity to cost-effectively extend processing and storage in the face of ever-expanding data volumes and varieties and an ever-increasing requirement for speed, these concerns have existed from the inception of digital computing in the transportation field. Since models and observations generate an enormous amount of data, the requirement to extract the most value from it has become a new difficulty. There was no other alternative except to rely only on the cognitive capacity of humans until lately.

Artificial intelligence and machine learning (AIML) may be able to assist in solving the big data problem since AIML has made significant progress in recent years, routinely outperforming people in specialized cognitive tasks and often doing better than humans. To add to the intrigue, the fact that every transportation challenge has its own unique set of dynamics makes it even more fascinating. Similar to the rise of data, machine learning models improve as the amount and variety of data grow. There is a direct correlation between these two issues: if the data cannot be wrangled, processed, and stored using methodologies that are scalable and parallel in nature, machine learning will not be incredibly effective. Therefore, there is a crucial need to illustrate the advances that Big Data and AIML will bring to the transportation field in a realistic, quantifiable, and reproducible fashion.

This Special Issue proposes assessing the progress and investigating the synergy of tackling these two connected challenges in the transportation field. High-quality original research papers on "Big Data, AI, and Machine Learning in Transportation" are welcome.

NOTICE 2-2021

2021-10-05

ISSN : 2734-9950 for Journal of Science and Transport Technology

We are pleased to inform that the Journal of Science and Transport Technology has got an International Standard Serial Number (ISSN: 2734-9950) which is an unique number used to identify an periodical journal. We welcome all authors and researches to submit their quality works and papers for consideration of publication in our journal.

NOTICE 1-2021

2021-09-20

Call for papers:

We are pleased to inform you that our journal namely Journal of Science and Transport Technology (JSTT) has been finally given an announcement for submission and publication.

We would like to invite you to submit highly qualified papers to our journal for publication. All manuscripts should be prepared in line with the requirements of the journal, which can be refered to “Guide for authors”. Please submit your article via website. Who has the difficulty in e-submitting, can send by email to the journal secretary: jstt@utt.edu.vn