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AI in Civil Engineering is a peer-reviewed journal published by Springer Nature, covering Infrastructure Maintenance and Monitoring, Innovative concrete reinforcement materials, BIM and Construction Integration.
| Publisher | Springer Nature |
|---|---|
| Reference style | Superscript numbered (Nature) Superscript — small raised numerals in the text 1. Smith, A., Jones, B. & Lee, C. A representative article title. AI in Civil Engineering 12, 45–58 (2023).
Formats any DOI in AI in Civil Engineering style. No sign-up. |
| Publishes research in | Infrastructure Maintenance and Monitoring Innovative concrete reinforcement materials BIM and Construction Integration Concrete Corrosion and Durability Concrete and Cement Materials Research |
| ISSN | 2097-0943 |
| Citation impact (2-yr) | 4.93 |
| h-index | 15 |
| i10-index | 25 |
| Total citations | 758 |
| Open access | Yes |
| Top institutions publishing here | Tongji University |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Abstract Recent diffusion-based AI art platforms can create impressive images from simple text descriptions. This makes them powerful tools for concept design in any discipline that requires creativity in visual design tasks. This is also true for early stages of architectural design with multiple stages of ideation, sketching and modelling. In this paper, we investigate…
Abstract The past decade has witnessed a notable transformation in the Architecture, Engineering and Construction (AEC) industry, with efforts made both in the academia and industry to facilitate improvement of efficiency, safety and sustainability in civil projects. Such advances have greatly contributed to a higher level of automation in the lifecycle management of civil assets…
Abstract Research has been continually growing toward the development of image-based structural health monitoring tools that can leverage deep learning models to automate damage detection in civil infrastructure. However, these tools are typically based on RGB images, which work well under ideal lighting conditions, but often have degrading performance in poor and low-light scenes. On…
The use of deep generative models (DGMs) such as variational autoencoders, autoregressive models, flow-based models, energy-based models, generative adversarial networks, and diffusion models has been advantageous in various disciplines due to their high data generative skills. Using DGMs has become one of the most trending research topics in Artificial Intelligence in recent years. On the…
Abstract Geopolymer concrete is acknowledged as a sustainable alternative to conventional Portland cement concrete owing to its ability to reduce carbon emissions and reutilize industrial by-products. This paper reviews the application of Artificial Intelligence-based Life Cycle Analysis (LCA) techniques in the sustainability assessment of geopolymer concrete. The assessment covers the entire life cycle of geopolymer…
