Advances in Structural Engineering Using AI and Sustainable Materials

Abstract submission deadline

30 November 2026

Manuscript submission deadline

31 January 2027

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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

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Architecture architecture	1.4	2.2	2021	18.9 Days	CHF 1200	Submit
Buildings buildings	3.1	4.4	2011	15.1 Days	CHF 2600	Submit
Infrastructures infrastructures	2.9	6.0	2016	18.3 Days	CHF 1800	Submit
Materials materials	3.2	6.4	2008	15.5 Days	CHF 2600	Submit
Technologies technologies	3.6	8.5	2013	19.1 Days	CHF 1800	Submit

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Published Papers (3 papers)

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All Applied Sciences Architecture Buildings Infrastructures Materials Technologies

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31 pages, 4674 KB  

Open AccessArticle

Deep Learning-Based Prediction of the Axial Capacity of CFRP-Strengthened Concrete Columns

by Nasim Shakouri Mahmoudabadi, Charles V. Camp and Afaq Ahmad

Infrastructures 2026, 11(5), 151; https://doi.org/10.3390/infrastructures11050151 - 28 Apr 2026

Viewed by 363

Abstract

Fiber-reinforced polymer (FRP) composites are widely used to strengthen reinforced concrete (RC) columns due to their high strength, durability, and ease of installation. Accurate prediction of the axial capacity of CFRP-strengthened concrete columns is essential for reliable structural design. Yet conventional empirical models [...] Read more.

Fiber-reinforced polymer (FRP) composites are widely used to strengthen reinforced concrete (RC) columns due to their high strength, durability, and ease of installation. Accurate prediction of the axial capacity of CFRP-strengthened concrete columns is essential for reliable structural design. Yet conventional empirical models often exhibit limited accuracy due to the complex interactions among structural parameters. This study develops a deep learning-based model to predict the axial capacity of CFRP-wrapped RC columns using a database of 469 experimental tests collected from published studies. A deep neural network (DNN) was optimized using the Optuna hyperparameter tuning framework and k-fold cross-validation to enhance model accuracy and robustness. Model performance was evaluated using statistical indicators, including R², RMSE, MAE, MAPE, and the a20-index. The results show excellent predictive performance with R² values approaching 0.99 and an a20-index of 0.98, demonstrating strong agreement between predicted and experimental results. Comparisons with the ACI 440.2R-17 and CSA S806-12 design codes indicate that the proposed DNN model provides significantly improved prediction accuracy, with lower errors. The developed approach offers a reliable and efficient tool for estimating the axial capacity of CFRP-strengthened concrete columns. Full article

(This article belongs to the Topic Advances in Structural Engineering Using AI and Sustainable Materials)

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24 pages, 1722 KB  

Open AccessSystematic Review

Deep Learning in the Architecture, Engineering, and Construction (AEC) Industry: Methods, Challenges, and Emerging Opportunities

by Muhammad Imran Khan, Abdul Waheed, Ehsan Harirchian and Bilal Manzoor

Buildings 2026, 16(8), 1546; https://doi.org/10.3390/buildings16081546 - 14 Apr 2026

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Abstract

In recent years, deep learning (DL) has emerged as a transformative technology with significant potential to advance the Architecture, Engineering, and Construction (AEC) industry. DL enables automation, intelligent decision-making, and predictive analytics across various phases of construction, including design, site monitoring, safety management, [...] Read more.

In recent years, deep learning (DL) has emerged as a transformative technology with significant potential to advance the Architecture, Engineering, and Construction (AEC) industry. DL enables automation, intelligent decision-making, and predictive analytics across various phases of construction, including design, site monitoring, safety management, and facility operations. Despite its growing adoption, research on the comprehensive methods, practical challenges and emerging opportunities of DL in the AEC industry remains limited. This study presents a state-of-the-art review of DL applications in the AEC industry by focusing on key methods, challenges, emerging opportunities and future research directions. A systematic literature review (SLR) was conducted in this study. Three major DL methods applied in the AEC industry were examined: (i) data-driven computer vision, (ii) natural language processing (NLP), and (iii) generative and simulation-based methods. Key challenges were identified: (i) data scarcity issues, (ii) high computational requirements, (iii) limited generalization across projects, (iv) human factors and resistance to adoption, and (v) lack of standardization and interoperability. Additionally, emerging opportunities and future research directions are highlighted: (i) advanced construction site monitoring and safety management, (ii) automated design and generative modeling, (iii) predictive maintenance and facility management, (iv) integration with robotics and autonomous construction systems, and (v) smart project management and decision support systems. This study advances a holistic understanding of DL in the AEC industry by systematically synthesizing current methods, challenges, and emerging trends. It establishes a structured foundation for future research to overcome technical, practical, and organizational challenges, thereby supporting the scalable, intelligent, and sustainable transformation of construction practices. Full article

(This article belongs to the Topic Advances in Structural Engineering Using AI and Sustainable Materials)

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23 pages, 7247 KB  

Open AccessArticle

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

Cited by 1 | Viewed by 1173

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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