Modelling Soil Compaction Parameters Using an Enhanced Hybrid Intelligence Paradigm of ANFIS and Improved Grey Wolf Optimiser

Bardhan, Abidhan; Singh, Raushan Kumar; Ghani, Sufyan; Konstantakatos, Gerasimos

doi:10.3390/math11143064

Cited by 21 publications

(3 citation statements)

References 60 publications

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“…Stepping beyond traditional ANNs, a novel hybrid intelligence paradigm known as the ANFIS-IGWO model was proposed in a different study. This model demonstrated superior precision in predicting soil compaction parameters, boasting correlation values of 0.9203 and 0.9050 for w opt and ρ dmax , respectively [29]. This accuracy was found to surpass other hybrid ANFIS models.…”

Section: Introductionmentioning

confidence: 82%

Utilizing Multivariate Adaptive Regression Splines (MARS) for Precise Estimation of Soil Compaction Parameters

Abed,

Kadhim,

Almusawi

et al. 2023

Applied Sciences

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Traditional laboratory methods for estimating soil compaction parameters, such as the Proctor test, have been recognized as time-consuming and labor-intensive. Given the increasing need for the rapid and accurate estimation of soil compaction parameters for a range of geotechnical applications, the application of machine learning models offers a promising alternative. This study focuses on employing the multivariate adaptive regression splines (MARS) model algorithm, a machine learning method that presents a significant advantage over other models through generating human-understandable piecewise linear equations. The MARS model was trained and tested on a comprehensive dataset to predict essential soil compaction parameters, including optimum water content (wopt) and maximum dry density (ρdmax). The performance of the model was evaluated using coefficient of determination (R2) and root mean square error (RMSE) values. Remarkably, the MARS models showed excellent predictive ability with high R2 and low RMSE, MAE, and relative error values, indicating its robustness and reliability in predicting soil compaction parameters. Through rigorous five-fold cross-validation, the model’s predictions for wopt returned an RMSE of 1.948%, an R2 of 0.893, and an MAE of 1.498%. For ρdmax, the results showcased an RMSE of 0.064 Mg/m3, an R2 of 0.899, and an MAE of 0.050 Mg/m3. When evaluated on unseen data, the model’s performance for wopt prediction was marked with an MAE of 1.276%, RMSE of 1.577%, and R2 of 0.948. Similarly, for ρdmax, the predictions were characterized by an MAE of 0.047 Mg/m3, RMSE of 0.062 Mg/m3, and R2 of 0.919. The results also indicated that the MARS model outperformed previously developed machine learning models, suggesting its potential to replace conventional testing methods. The successful application of the MARS model could revolutionize the geotechnical field through providing quick and reliable predictions of soil compaction parameters, improving efficiency for construction projects. Lastly, a variable importance analysis was performed on the model to assess how input variables affect its outcomes. It was found that fine content (Cf) and plastic limit (PL) have the greatest impact on compaction parameters.

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Section: Introductionmentioning

confidence: 82%

Utilizing Multivariate Adaptive Regression Splines (MARS) for Precise Estimation of Soil Compaction Parameters

Abed,

Kadhim,

Almusawi

et al. 2023

Applied Sciences

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“…Building upon these traditional approaches, advanced AI methodologies have brought significant advancements in the fields of geotechnical characterization in cohesive soils. Comparative analyses have been conducted on various machine learning models and soft computing techniques to predict key soil properties such as soil compaction parameters [60]- [62], shear strength properties [63]- [67] and the suitable percentage of waste materials for soil improvement [68]. In these studies, the proposed models showed superior performance with high prediction accuracy.…”

Section: F Research Frontier Identificationmentioning

confidence: 99%

Global Research Trends in Soft Soil Management for Infrastructure Development: Opportunities and Challenges

Sim,

Katman,

Baharuddin

et al. 2024

IEEE Access

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Soft soils present significant challenges to infrastructure development worldwide due to high compressibility, low shear strength and low permeability. Bibliometric analysis remains scarce in the soft soil management field, making it difficult to grasp global trends and contributions. This research addresses this gap by providing a novel and comprehensive bibliometric analysis of soft soil management literature to investigate the trend, opportunities and challenges. The analysis predominantly examines ground improvement methods based on 2,260 journal articles and proceeding papers from WOS core collection between 2013 and 2023 using CiteSpace. The analysis encompasses country distribution, authorship and cocited authors, cited journals, reference co-citations and identification of research hotspots and frontiers. Findings show a growing interest and increased research focus in soft soil management, with China emerging as a prominent contributor. Reference co-citation clustering analysis reveals two dominant research themes: ground improvement methods (geosynthetic-reinforced embankments and stone columns) and sustainable practices (reuse of waste materials). The emerging word analysis reveals an evolutionary trend from investigating innovative and sustainable techniques to shear strength and failure mechanisms. Recent studies focus on the use of vertical drains, deep mixing columns, chemical stabilization, and sustainable ground improvement strategies. Opportunities lie in sustainable ground improvement methods and novel sensing technologies. Key challenges include bearing capacity, settlement, and slope stability. The study highlights the need for more research into two other key areas such as geotechnical characterization and foundation design. Overall, this bibliometric review contributes to a more thorough understanding of soft soil management studies for researchers and practitioners.

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“…Wolves with lesser responsibilities are positioned at the lower ranks of the pack hierarchy. The hunting procedure in GWO may be categorized as searching, surrounding, hunting, and attacking (Bardhan et al, 2023). They exhibit several solutions and continuously adjust the locations of these wolves according to their tness function values.…”

Section: Optimization Algorithmmentioning

confidence: 99%

Prediction of split tensile strength of recycled aggregate concrete leveraging explainable hybrid XGB with optimization algorithm

Sapkota,

Saini

2024

Multiscale and Multidiscip. Model. Exp. and Des.

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Recycled aggregate-based concrete has been adopted in building construction as it can reduce concrete waste, eventually minimizing the environmental impact. However, using recycled materials can lead to compromised performance of mechanical properties like split tensile strength (STS). Several factors, including density, water absorption, and recycled aggregate proportion, play a vital role in assessments of STS. This study explores the better evaluation of STS using a hybridized machine learning algorithm. Ensemble model XGBoost with ve optimization algorithms, namely Random search (RS), Grid search (GS), Bayesian Optimization (BO), Grey Wolf optimization (GWO), and Particle Swarm Optimization (PSO) are considered for the study. The comparison shows that XGB-PSO performed very well with R 2 of 0.9988 and 0.9602 in the training and testing sets, respectively. The potential performance of GWO is also seen during the assessments. Further, the 10-fold crossvalidation used in this study ensures that the models can predict better without over tting. The model's explainability is done using Shapley Additive Explanations (SHAP) analysis. SHAP-based study reveals that Cement, Water, and size of aggregates (M-RCA) are critical elements and may enhance STS if considered. The best cement range is 300 to 500 kg/m 3 , the M-RCA size is 10 to 20 mm, and the water range is 180 to 200 kg/m 3 . SHAP interaction graphs con rm the result. This study helps engineers and researchers to understand the critical parameters for making informed decisions, thus promoting sustainable construction practices.

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Modelling Soil Compaction Parameters Using an Enhanced Hybrid Intelligence Paradigm of ANFIS and Improved Grey Wolf Optimiser

Cited by 21 publications

References 60 publications

Utilizing Multivariate Adaptive Regression Splines (MARS) for Precise Estimation of Soil Compaction Parameters

Utilizing Multivariate Adaptive Regression Splines (MARS) for Precise Estimation of Soil Compaction Parameters

Global Research Trends in Soft Soil Management for Infrastructure Development: Opportunities and Challenges

Prediction of split tensile strength of recycled aggregate concrete leveraging explainable hybrid XGB with optimization algorithm

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