Optimized supervised machine learning for accurate prediction of periods in Türkiye's heritage minarets.
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| Title: | Optimized supervised machine learning for accurate prediction of periods in Türkiye's heritage minarets. |
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| Authors: | Tran, Chon1 (AUTHOR), Nguyen, Nhan Thanh Vu1 (AUTHOR), Le, Duong Thai1 (AUTHOR), Nguyen, Quy Thue1 (AUTHOR) nguyenthuequy@gmail.com, Livaoğlu, Ramazan2 (AUTHOR) |
| Source: | Bulletin of Earthquake Engineering. May2026, Vol. 24 Issue 5, p2685-2708. 24p. |
| Subject Terms: | *Supervised learning, *Structural stability, *Masonry, *Historic buildings, *Earthquake damage |
| Geographic Terms: | Turkey |
| Abstract: | Historical masonry minarets, known for their tall and slender forms, are especially susceptible to environmental and seismic impacts because of their distinct structural characteristics. Traditional methods, such as three-dimensional numerical modeling, are widely used to evaluate the stability of these structures. However, the complex, heterogeneous materials common in historical construction often lead to significant differences between simulated predictions and actual dynamic behaviors, posing challenges to accurately assessing their stability. This study addresses these issues by introducing a supervised machine learning (SML) approach specifically designed to predict the fundamental period of 27 historical minarets in Türkiye. Unlike conventional techniques that depend on extensive field testing or highly detailed numerical models, this SML model utilizes straightforward geometric (such as equivalent height and diameters) and material parameters (Young's modulus and mass density) to achieve accurate predictions with high reliability. Additionally, the input vectors are expanded to include slenderness parameters, significantly enhancing prediction accuracy. Three optimized SML functions are systematically evaluated, with the Grid Search method identified as the most effective approach for this application. The inclusion of slenderness parameters and the use of the Grid Search method yields exceptional prediction performance, demonstrating the outstanding of the proposed methodology compared to some existing empirical equations when achieving prediction error margins below 20%. This framework offers a practical, non-invasive tool for analyzing the dynamic stability and resilience of culturally significant structures, providing a modern, efficient solution for heritage conservation. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 193651666 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Optimized supervised machine learning for accurate prediction of periods in Türkiye's heritage minarets. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Tran%2C+Chon%22">Tran, Chon</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nguyen%2C+Nhan+Thanh+Vu%22">Nguyen, Nhan Thanh Vu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Le%2C+Duong+Thai%22">Le, Duong Thai</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nguyen%2C+Quy+Thue%22">Nguyen, Quy Thue</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> nguyenthuequy@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Livaoğlu%2C+Ramazan%22">Livaoğlu, Ramazan</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Bulletin+of+Earthquake+Engineering%22">Bulletin of Earthquake Engineering</searchLink>. May2026, Vol. 24 Issue 5, p2685-2708. 24p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Supervised+learning%22">Supervised learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Structural+stability%22">Structural stability</searchLink><br />*<searchLink fieldCode="DE" term="%22Masonry%22">Masonry</searchLink><br />*<searchLink fieldCode="DE" term="%22Historic+buildings%22">Historic buildings</searchLink><br />*<searchLink fieldCode="DE" term="%22Earthquake+damage%22">Earthquake damage</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Turkey%22">Turkey</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Historical masonry minarets, known for their tall and slender forms, are especially susceptible to environmental and seismic impacts because of their distinct structural characteristics. Traditional methods, such as three-dimensional numerical modeling, are widely used to evaluate the stability of these structures. However, the complex, heterogeneous materials common in historical construction often lead to significant differences between simulated predictions and actual dynamic behaviors, posing challenges to accurately assessing their stability. This study addresses these issues by introducing a supervised machine learning (SML) approach specifically designed to predict the fundamental period of 27 historical minarets in Türkiye. Unlike conventional techniques that depend on extensive field testing or highly detailed numerical models, this SML model utilizes straightforward geometric (such as equivalent height and diameters) and material parameters (Young's modulus and mass density) to achieve accurate predictions with high reliability. Additionally, the input vectors are expanded to include slenderness parameters, significantly enhancing prediction accuracy. Three optimized SML functions are systematically evaluated, with the Grid Search method identified as the most effective approach for this application. The inclusion of slenderness parameters and the use of the Grid Search method yields exceptional prediction performance, demonstrating the outstanding of the proposed methodology compared to some existing empirical equations when achieving prediction error margins below 20%. This framework offers a practical, non-invasive tool for analyzing the dynamic stability and resilience of culturally significant structures, providing a modern, efficient solution for heritage conservation. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=193651666 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10518-025-02146-5 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 2685 Subjects: – SubjectFull: Supervised learning Type: general – SubjectFull: Structural stability Type: general – SubjectFull: Masonry Type: general – SubjectFull: Historic buildings Type: general – SubjectFull: Earthquake damage Type: general – SubjectFull: Turkey Type: general Titles: – TitleFull: Optimized supervised machine learning for accurate prediction of periods in Türkiye's heritage minarets. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tran, Chon – PersonEntity: Name: NameFull: Nguyen, Nhan Thanh Vu – PersonEntity: Name: NameFull: Le, Duong Thai – PersonEntity: Name: NameFull: Nguyen, Quy Thue – PersonEntity: Name: NameFull: Livaoğlu, Ramazan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1570761X Numbering: – Type: volume Value: 24 – Type: issue Value: 5 Titles: – TitleFull: Bulletin of Earthquake Engineering Type: main |
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