Investigating a hybrid extreme learning machine coupled with Dingo Optimization Algorithm for modeling liquefaction triggering in sand-silt mixtures.

Autor: Hameed MM; Department of Civil Engineering, Al-Maarif University College, Ramadi, Iraq. mohmmag1@gmail.com.; Department of Computer Science, Al-Maarif University College, Ramadi, Iraq. mohmmag1@gmail.com., Masood A; Department of Natural and Applied Sciences, TERI School of Advanced Studies, New Delhi, India., Srivastava A; Department of Civil Engineering, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, West Bengal, 721302, India., Abd Rahman N; Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia.; Smart and Sustainable Township Research Centre (SUTRA), Universiti Kebangsaan Malaysia (UKM), 43600, Bangi, Selangor, Malaysia., Mohd Razali SF; Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia.; Smart and Sustainable Township Research Centre (SUTRA), Universiti Kebangsaan Malaysia (UKM), 43600, Bangi, Selangor, Malaysia., Salem A; Civil Engineering Department, Faculty of Engineering, Minia University, Minia, 61111, Egypt. salem.ali@mik.pte.hu.; Structural Diagnostics and Analysis Research Group, Faculty of Engineering and Information Technology, University of Pécs, Pécs, Hungary. salem.ali@mik.pte.hu., Elbeltagi A; Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 May 11; Vol. 14 (1), pp. 10799. Date of Electronic Publication: 2024 May 11.
DOI: 10.1038/s41598-024-61059-6
Abstrakt: Liquefaction is a devastating consequence of earthquakes that occurs in loose, saturated soil deposits, resulting in catastrophic ground failure. Accurate prediction of such geotechnical parameter is crucial for mitigating hazards, assessing risks, and advancing geotechnical engineering. This study introduces a novel predictive model that combines Extreme Learning Machine (ELM) with Dingo Optimization Algorithm (DOA) to estimate strain energy-based liquefaction resistance. The hybrid model (ELM-DOA) is compared with the classical ELM, Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM model), and Sub-clustering (ANFIS-Sub model). Also, two data pre-processing scenarios are employed, namely traditional linear and non-linear normalization. The results demonstrate that non-linear normalization significantly enhances the prediction performance of all models by approximately 25% compared to linear normalization. Furthermore, the ELM-DOA model achieves the most accurate predictions, exhibiting the lowest root mean square error (484.286 J/m 3 ), mean absolute percentage error (24.900%), mean absolute error (404.416 J/m 3 ), and the highest correlation of determination (0.935). Additionally, a Graphical User Interface (GUI) has been developed, specifically tailored for the ELM-DOA model, to assist engineers and researchers in maximizing the utilization of this predictive model. The GUI provides a user-friendly platform for easy input of data and accessing the model's predictions, enhancing its practical applicability. Overall, the results strongly support the proposed hybrid model with GUI serving as an effective tool for assessing soil liquefaction resistance in geotechnical engineering, aiding in predicting and mitigating liquefaction hazards.
(© 2024. The Author(s).)
Databáze: MEDLINE
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