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Advances in Structural Engineering Using AI and Sustainable Materials
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Institut für Strukturmechanik (ISM), Bauhaus-Universität Weimar, D-99423 Weimar, Germany
School of Engineering, Cardiff University, Cardiff CF10 3AT, UK
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Advances in Structural Engineering Using AI and Sustainable Materials

Abstract submission deadline
20 March 2026
Manuscript submission deadline
20 May 2026
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Topic Information

Dear Colleagues,

We are pleased to announce a call for papers for a Topic dedicated to Advances in Structural Engineering Using AI and Sustainable Materials. This topic will explore the intersection of cutting-edge technologies and sustainable practices in structural engineering. We encourage submissions covering the following topics:

  • AI-driven design and optimization of sustainable materials
  • Machine learning applications for material performance and structural analysis
  • Case studies on the integration of AI and eco-friendly materials in construction
  • Predictive modeling for sustainability and life-cycle assessment
  • Smart materials and their applications in future infrastructure

Join us in shaping the future of structural engineering by showcasing innovations at the nexus of AI and sustainability.

Dr. Ehsan Harirchian
Dr. Viviana Novelli
Topic Editors

Keywords

  • artificial intelligence in engineering
  • sustainable construction materials
  • machine learning in structural design
  • green building technologies
  • structural health monitoring
  • AI-driven optimization
  • eco-friendly infrastructure
  • smart materials
  • carbon-neutral construction
  • AI-powered structural analysis

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci 		2.5 5.5 2011 19.8 Days CHF 2400 Submit
Architecture
architecture 		1.4 2.2 2021 34.2 Days CHF 1200 Submit
Buildings
buildings 		3.1 4.4 2011 14.9 Days CHF 2600 Submit
Infrastructures
infrastructures 		2.9 6.0 2016 15.7 Days CHF 1800 Submit
Materials
materials 		3.2 6.4 2008 15.2 Days CHF 2600 Submit
Technologies
technologies 		3.6 8.5 2013 21.8 Days CHF 1600 Submit

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Published Papers (1 paper)

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23 pages, 7247 KiB  
Article
Pit Collapse Risk Fusion Early-Warning Method Based on Machine Learning and Improved Cloud Dempster–Shafer
by Jiajia Zeng, Bo Wu and Cong Liu
Appl. Sci. 2025, 15(13), 7571; https://doi.org/10.3390/app15137571 - 5 Jul 2025
Viewed by 249
Abstract
Considering the complexity of the metro pit construction environment, the existing risk early-warning methods cannot ensure high-precision early warning. A high-accuracy metro pit collapse risk fusion early-warning method is proposed in present study. The main contributions include (1) presenting a new input to [...] Read more.
Considering the complexity of the metro pit construction environment, the existing risk early-warning methods cannot ensure high-precision early warning. A high-accuracy metro pit collapse risk fusion early-warning method is proposed in present study. The main contributions include (1) presenting a new input to the fusion model by optimizing the machine learning model through a multi-step rolling method, and then using the basic probability assignment values obtained from the cloud model as input to the fusion model and (2) developing an improved methodology to address the paradoxical results of the fusion of traditional Dempster–Shafer evidence theory when there is a high level of conflict in multi-source risk prediction data. The proposed method is successfully applied to the Guangzhou Metro station project. By analyzing the early-warning results of 240 moments in 6 monitoring points, compared with the single information source method and the traditional D-S method, the early-warning accuracy of this method is increased by 15.8% and 10.8% respectively, the false alarm rate is reduced by 6.3% and 5.5%, respectively, and the missed alarm rate is reduced by 9.5% and 5.3%, respectively. The high-accuracy fusion early-warning method proposed in this paper has good universality and effectiveness in the early warning of subway foundation pit collapse risk. Full article
(This article belongs to the Topic Advances in Structural Engineering Using AI and Sustainable Materials)
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