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Applied Sciences
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Innovative Applications of Artificial Intelligence in Concrete and Steel Structures
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Innovative Applications of Artificial Intelligence in Concrete and Steel Structures

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 5587

Special Issue Editors

Dr. Visar Farhangi

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Guest Editor
Department of Civil and Environmental Engineering and Construction, University of Nevada, Las Vegas (UNLV), Las Vegas, NV 89557, USA
Interests: geotechnical engineering, structural engineering, civil engineering; polymers; foundation; liquefaction; pile; deep foundation; finite element
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Moses Karakouzian

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering and Construction, University of Nevada, Las Vegas (UNLV), Las Vegas, NV 89557, USA
Interests: geotechnical engineering; construction materials; hydraulics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Evaluating the response of concrete and steel structures requires a host of large-scale, in situ experimental investigations, which are time consuming and require the investment of considerable financial resources. Presently, the application of innovative methods such as the implementation of artificial intelligence has laid the groundwork for analyzing and evaluating the behavior of steel and concrete structures as a cost-effective solution in a relatively short period of time.

Accordingly, this Special Issue will cover novel studies on applications of artificial intelligence in civil engineering to predict the response of structures subjected to static and dynamic loads.

Dr. Visar Farhangi
Prof. Dr. Moses Karakouzian
Guest Editors

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Published Papers (1 paper)

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Research

13 pages, 10605 KiB  
Article
Applications of Decision Tree and Random Forest as Tree-Based Machine Learning Techniques for Analyzing the Ultimate Strain of Spliced and Non-Spliced Reinforcement Bars
by Hamed Dabiri, Visar Farhangi, Mohammad Javad Moradi, Mehdi Zadehmohamad and Moses Karakouzian
Appl. Sci. 2022, 12(10), 4851; https://doi.org/10.3390/app12104851 - 11 May 2022
Cited by 51 | Viewed by 4969
Abstract
The performance of both non-spliced and spliced steel bars significantly affects the overall performance of structural reinforced concrete elements. In this context, the mechanical properties of reinforcement bars (i.e., their ultimate strength and strain) should be determined in order to evaluate their reliability [...] Read more.
The performance of both non-spliced and spliced steel bars significantly affects the overall performance of structural reinforced concrete elements. In this context, the mechanical properties of reinforcement bars (i.e., their ultimate strength and strain) should be determined in order to evaluate their reliability prior to the construction procedure. In this study, the application of Tree-Based machine learning techniques is implemented to analyze the ultimate strain of non-spliced and spliced steel reinforcements. In this regard, a database containing the results of 225 experimental tests was collected based on the research investigations available in peer-reviewed international publications. The database included the mechanical properties of both non-spliced and mechanically spliced bars. For better accuracy, the databases of other splicing methods such as lap and welded-spliced methods were excluded from this research. The database was categorized as two sub-databases: training (85%) and testing (15%) of the developed models. Various effective parameters such as splice technique, steel grade of the bar, diameter of the steel bar, coupler geometry—including length and outer diameter along with the testing temperatures—were defined as the input variables for analyzing the ultimate strain using tree-based approaches including Decision Trees and Random Forest. The predicted outcomes were compared to the actual values and the precision of the prediction models was assessed via performance metrics, along with a Taylor diagram. Based on the reported results, the reliability of the proposed ML-based methods was acceptable (with an R2 ≥ 85%) and they were time-saving and cost-effective compared to more complicated, time-consuming, and expensive experimental examinations. More importantly, the models proposed in this study can be further considered as a part of a comprehensive prediction model for estimating the stress-strain behavior of steel bars. Full article
(This article belongs to the Special Issue Innovative Applications of Artificial Intelligence in Concrete and Steel Structures)
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