Enhancing unconfined compressive strength of stabilized soil with lime and cement prediction through a robust hybrid machine learning approach utilizing Naive Bayes Algorithm

The unconfined compressive strength (UCS) of stabilized soil with lime and cement is a crucial mechanical factor in developing accurate geomechanical models. In the past, determining UCS required laborious laboratory testing of core samples or complex well-log analysis, both of which consumed many r...

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Vydané v:Journal of engineering and applied science (Online) Ročník 71; číslo 1; s. 84 - 23
Hlavný autor: Wan, Weiqing
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024
Springer Nature B.V
SpringerOpen
Predmet:
Accuracy
Algorithms
Cement
Civil Engineering
Compressive strength
Dynamic arithmetic optimization algorithm
Electrical Engineering
Engineering
Engineering Mathematics
Geomechanics
Heuristic methods
Industrial Chemistry/Chemical Engineering
Lime soil stabilization
Machine learning
Mechanical Engineering
Naive Bayes
Neural networks
Optimization
Performance measurement
Prediction models
Real time
Smell agent optimization
Soil lime
Soil mechanics
Soil strength
Soils
Unconfined compressive strength
Naive Bayes
Smell agent optimization
Dynamic arithmetic optimization algorithm
Unconfined compressive strength
Machine learning
ISSN:1110-1903, 2536-9512
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Shrnutí:The unconfined compressive strength (UCS) of stabilized soil with lime and cement is a crucial mechanical factor in developing accurate geomechanical models. In the past, determining UCS required laborious laboratory testing of core samples or complex well-log analysis, both of which consumed many resources. This study introduces a novel method for real-time UCS prediction while acknowledging the need for efficiency. This method makes use of Specific Naive Bayes (NB) predictive models that are strengthened by the smell agent optimization (SAO) and the Dynamic Arithmetic Optimization Algorithm (DAOA), two reliable meta-heuristic algorithms. Combining these algorithms improves prediction precision while streamlining the process. By examining UCS samples from various soil types obtained from earlier stabilization tests, these models are validated. This study identifies three different models: NBDA, NBSA, and a single NB. The individual insights each model provides work in concert to increase the overall UCS prediction accuracy. This approach represents a significant advancement in UCS prediction methodologies, revealing a quick and effective method with wide-ranging implications for various geomechanical applications. Meta-heuristic algorithms combined with particular NB models produce promising results, opening up new possibilities for real-time UCS estimation across various geological scenarios. Especially noteworthy are the NBDA model’s impressive performance metrics. The entire dataset achieves an R 2 value of 0.992 during testing. The RMSE of 108.69 for the NBDA model during the training phase also shows that it has the best performance overall. It consistently exhibits commendable generalization and predictive abilities that outperform those of the developed NB and NBSA models, highlighting its usefulness and effectiveness in practical applications.
Bibliografia:ObjectType-Article-1
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ISSN:1110-1903
2536-9512
DOI:10.1186/s44147-024-00408-8