A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Applications of Dynamic Cone Penetration Test for Estimating Liquefaction Susceptibility Using Machine Learning Paradigms
https://orcid.org/http://orcid.org/0000-0003-1361-0126
This study proposes an innovative method for assessing liquefaction potential by incorporating field data from the dynamic cone penetration test (DCPT) with advanced machine learning algorithms. The integration of these techniques aims to enhance the accuracy and reliability of liquefaction evaluations. The proposed framework addresses the limitations of the traditional methods by combining cost-efficient and expeditious DCPT with state-of-the-art computational tools. DCPT data, including the penetration rate (e) and dynamic resistance (qd), were collected from various sites and used to calculate the critical e/qd ratio for determining the liquefaction susceptibility. A threshold criterion was established, classifying soils with e/qd ≤ 4 as non-liquefiable and those with e/qd > 4 as liquefiable. The prediction accuracies for liquefied and non-liquefied cases were 82.7% and 86.5%, respectively. Machine-learning models, including SVM-PSO, SVM-GWO, SVM-GA, and SVM-FF, were employed to confirm the evaluation procedure. Hyperparameter tuning was performed to optimize the model performance, with SVM-PSO achieving the highest R2 (0.999) and the lowest RMSE (0.220). The results underscore the efficacy of the e/qd ratio within the DCPT framework, offering a robust tool for assessing the liquefaction susceptibility. This innovative approach enhances the accuracy, efficiency, and adaptability of liquefaction assessments across diverse soil conditions.
Applications of Dynamic Cone Penetration Test for Estimating Liquefaction Susceptibility Using Machine Learning Paradigms
https://orcid.org/http://orcid.org/0000-0003-1361-0126
This study proposes an innovative method for assessing liquefaction potential by incorporating field data from the dynamic cone penetration test (DCPT) with advanced machine learning algorithms. The integration of these techniques aims to enhance the accuracy and reliability of liquefaction evaluations. The proposed framework addresses the limitations of the traditional methods by combining cost-efficient and expeditious DCPT with state-of-the-art computational tools. DCPT data, including the penetration rate (e) and dynamic resistance (qd), were collected from various sites and used to calculate the critical e/qd ratio for determining the liquefaction susceptibility. A threshold criterion was established, classifying soils with e/qd ≤ 4 as non-liquefiable and those with e/qd > 4 as liquefiable. The prediction accuracies for liquefied and non-liquefied cases were 82.7% and 86.5%, respectively. Machine-learning models, including SVM-PSO, SVM-GWO, SVM-GA, and SVM-FF, were employed to confirm the evaluation procedure. Hyperparameter tuning was performed to optimize the model performance, with SVM-PSO achieving the highest R2 (0.999) and the lowest RMSE (0.220). The results underscore the efficacy of the e/qd ratio within the DCPT framework, offering a robust tool for assessing the liquefaction susceptibility. This innovative approach enhances the accuracy, efficiency, and adaptability of liquefaction assessments across diverse soil conditions.
Applications of Dynamic Cone Penetration Test for Estimating Liquefaction Susceptibility Using Machine Learning Paradigms
https://orcid.org/http://orcid.org/0000-0003-1361-0126
This study proposes an innovative method for assessing liquefaction potential by incorporating field data from the dynamic cone penetration test (DCPT) with advanced machine learning algorithms. The integration of these techniques aims to enhance the accuracy and reliability of liquefaction evaluations. The proposed framework addresses the limitations of the traditional methods by combining cost-efficient and expeditious DCPT with state-of-the-art computational tools. DCPT data, including the penetration rate (e) and dynamic resistance (qd), were collected from various sites and used to calculate the critical e/qd ratio for determining the liquefaction susceptibility. A threshold criterion was established, classifying soils with e/qd ≤ 4 as non-liquefiable and those with e/qd > 4 as liquefiable. The prediction accuracies for liquefied and non-liquefied cases were 82.7% and 86.5%, respectively. Machine-learning models, including SVM-PSO, SVM-GWO, SVM-GA, and SVM-FF, were employed to confirm the evaluation procedure. Hyperparameter tuning was performed to optimize the model performance, with SVM-PSO achieving the highest R2 (0.999) and the lowest RMSE (0.220). The results underscore the efficacy of the e/qd ratio within the DCPT framework, offering a robust tool for assessing the liquefaction susceptibility. This innovative approach enhances the accuracy, efficiency, and adaptability of liquefaction assessments across diverse soil conditions.
Applications of Dynamic Cone Penetration Test for Estimating Liquefaction Susceptibility Using Machine Learning Paradigms
Transp. Infrastruct. Geotech.
Singh, Shubhendu Vikram (author) / Ghani, Sufyan (author)
2025-01-01
Article (Journal)
Electronic Resource
English
Liquefaction risk , Dynamic Cone Penetration Test (DCPT) , Machine learning , Sustainable infrastructure , Seismic risk assessment , Resilient infrastructure Information and Computing Sciences , Artificial Intelligence and Image Processing , Engineering , Geoengineering, Foundations, Hydraulics , Geotechnical Engineering & Applied Earth Sciences , Building Materials
| British Library Online Contents | 2004
Reliability of sandy soil liquefaction susceptibility using cone penetration testing
| British Library Conference Proceedings | 2008