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Designs
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Intelligent Infrastructure and Construction in Civil Engineering
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Intelligent Infrastructure and Construction in Civil Engineering

A special issue of Designs (ISSN 2411-9660). This special issue belongs to the section "Civil Engineering Design".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 932

Special Issue Editors

Dr. Jie Tang

E-Mail
Guest Editor
1. Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China
2. School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China
Interests: numerical simulation; earthquake disaster; tunnel engineering; rock mechanics
Dr. Qingxiang Meng

E-Mail Website
Guest Editor
School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China
Interests: multiscale analysis; rock mechanics; numerical simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As civil engineering continues to evolve, the integration of intelligent technologies into infrastructure design and construction is becoming increasingly critical. This Special Issue focuses on the intersection of advanced computational methods and physical simulations in the development of intelligent infrastructure. It explores innovations that leverage AI-driven simulations, multi-field and multi-scale numerical methods, and digital twin technologies to optimize civil engineering projects.

This Special Issue aims to investigate how intelligent systems can enhance the design, construction, and maintenance of tunnels, foundations, and other essential infrastructure elements. Additionally, contributions on urban hydraulics, dam foundations, high slopes, and underground engineering will be featured, with a particular emphasis on the application of AI and digital twins to optimize structural performance, ensure resilience, and predict long-term behavior. We seek papers that integrate these technologies to improve construction efficiency, reduce costs, and mitigate risks in infrastructure projects, ensuring adaptability to environmental challenges such as water management and geological disturbances.

The goal of this Special Issue is to provide a platform for innovative solutions in intelligent infrastructure, informed by cutting-edge research in AI-driven physical simulations, multi-scale modeling, and digital engineering, all contributing to the development of smarter, more sustainable civil infrastructure.

Dr. Jie Tang
Dr. Qingxiang Meng
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 250 words) can be sent to the Editorial Office for assessment.

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. Designs 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 1600 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

  • intelligent infrastructure
  • AI-driven simulations
  • digital twin technologies
  • multiscale numerical methods
  • sustainable civil engineering

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Published Papers (1 paper)

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Research

44 pages, 4569 KB  
Article
LSTM-Based Fast Prediction of Seismic Response and Fragility for Bridge Pile-Group Foundations: A Data-Driven Design Approach
by Zhenfeng Han, Deming She and Jun Liu
Designs 2026, 10(2), 37; https://doi.org/10.3390/designs10020037 - 23 Mar 2026
Cited by 1 | Viewed by 606
Abstract
Rapid and accurate prediction of seismic response and fragility for bridge pile-group foundations (PGFs) is crucial for assessing seismic resilience. However, the high computational cost of traditional high-fidelity nonlinear analysis limits the application of probabilistic seismic risk analysis. To address this, an integrated [...] Read more.
Rapid and accurate prediction of seismic response and fragility for bridge pile-group foundations (PGFs) is crucial for assessing seismic resilience. However, the high computational cost of traditional high-fidelity nonlinear analysis limits the application of probabilistic seismic risk analysis. To address this, an integrated deep learning framework is proposed that employs a unidirectional, multi-layer LSTM network for end-to-end prediction of structural responses directly from ground motions. The proposed model features two innovations. First, its multi-output capability enables simultaneous prediction of complete response time histories and peak values for key engineering demand parameters—bending moment, curvature, and pile cap displacement. Second, the network incorporates sliding time windows and residual connections to capture complex nonlinear soil–structure interaction. These predictions are integrated into a probabilistic seismic demand model to generate fragility curves. The framework is validated using a high-fidelity OpenSees model of a real bridge PGF subjected to 1000 ground motions. Results demonstrate the model’s excellent predictive accuracy: for peak bending moment, the mean predicted-to-actual ratio ranges from 0.97 to 1.03, with standard deviation below 0.12; the derived fragility curves show excellent agreement with benchmarks, achieving an average R2 of 0.985 across four damage states. More importantly, the framework reduces the time for a complete fragility assessment (200 incremental dynamic analyses) from approximately 12 h to about 1 s—a 40,000× speed-up—making data-driven rapid and large-scale seismic risk assessment a reality. The proposed framework provides engineers with a practical design tool for rapidly evaluating alternative foundation configurations and informing seismic design decisions, thereby integrating advanced data-driven methods directly into the engineering design workflow. Full article
(This article belongs to the Special Issue Intelligent Infrastructure and Construction in Civil Engineering)
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