A hybrid approach of support vector regression with genetic algorithm optimization for predicting spalling in continuously reinforced concrete pavement

Alnaqbi, Ali; Al-Khateeb, Ghazi; Zeiada, Waleed

doi:10.1007/s41024-024-00499-z

J Build Rehabil

2024

DOI: 10.1007/s41024-024-00499-z

|View full text |Cite

A hybrid approach of support vector regression with genetic algorithm optimization for predicting spalling in continuously reinforced concrete pavement

Ali Alnaqbi,

Ghazi Al-Khateeb,

Waleed Zeiada

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Machine learning modeling of transverse cracking in flexible pavement

Zeiada,

Alnaqbi,

Al-Khateeb

et al. 2024

Discov Civ Eng

View full text Add to dashboard Cite

Machine learning modeling of transverse cracking in flexible pavement

Zeiada,

Alnaqbi,

Al-Khateeb

et al. 2024

Discov Civ Eng

View full text Add to dashboard Cite

Developing precision in WEDM machining of Mg-SiC nanocomposites using machine learning algorithms

Tamang,

Chauhan,

Banerjee

et al. 2024

Eng. Res. Express

View full text Add to dashboard Cite

Wire electrical discharge machining (WEDM) is one of the most commonly used non-conventional machining processes in the aerospace, nuclear, and precision industries. This technology possesses numerous advantages over traditional machining processes due to its superior properties, such as high precision of machined surfaces, ease of machining of complex shapes, and hard material processing. This study focuses on investigating the performance characteristics of Mg-SiC nanocomposite through experimental analysis using WEDM, with surface roughness as the key evaluation parameter. Employing a fractional factorial design, twenty-five experimental datasets were generated to explore the impact of WEDM machining parameters, including Pulse on time (Ton), Pulse off time (Toff), Servo voltage (SV), and Peak current (Ip), on surface roughness. Leveraging a machine learning approach, specifically, Support Vector Regression (SVR) integrated with Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), an integrated predictive surface roughness model for machined substrates was developed. The predicted results exhibited a high level of agreement with experimental data, boasting a coefficient of determination (R2) value of 0.866 and a mean square error (MSE) of 0.364. A novel aspect of this work lies in integrating GA-PSO with SVR to obtain optimized surface roughness values. Through this methodology, SVR-GA and SVR-PSO achieved optimum surface roughness values of 0.187 μm and 0.132 μm, respectively, with SVR-PSO demonstrating superior performance by outperforming SVR-GA after 360 and 428 iterations, respectively. Thus, this study presents a novel and effective approach to optimizing surface roughness values in WEDM processes.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A hybrid approach of support vector regression with genetic algorithm optimization for predicting spalling in continuously reinforced concrete pavement

Cited by 2 publications

References 23 publications

Machine learning modeling of transverse cracking in flexible pavement

Machine learning modeling of transverse cracking in flexible pavement

Developing precision in WEDM machining of Mg-SiC nanocomposites using machine learning algorithms

Contact Info

Product

Resources

About