A hybrid approach of ANN and improved PSO for estimating soaked CBR of subgrade soils of heavy-haul railway corridor

Bardhan, Abidhan; Alzo’ubi, Abdel Kareem; Sangeetha, P.; Hamidian, Pouria; GuhaRay, Anasua; Kumar, Gaurav; Tsoukalas, Markos Z.

doi:10.1080/10298436.2023.2176494

Cited by 15 publications

(3 citation statements)

References 77 publications

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“…To address the limits of classic regression approaches in geotechnical engineering, the application of machine learning (ML) algorithms have been extensively developed, demonstrating improved performance for predicting several soil engineering properties compared to traditional statistical methods (e.g. Bardhan et al 2023; Bardhan et al 2021;Dam Nguyen et al 2022;Díaz and Tomás 2021;Salvatore et al 2022;Singh et al 2023;Trong et al 2021).…”

Section: Introductionmentioning

confidence: 99%

“…However, it is important to be aware of the limitations and uncertainties associated with ML approaches before applying them to real-world geotechnical engineering projects. Numerous studies (Baghbani et al 2022;Zhang et al 2023;Zhang et al 2022) have exposed these limitations, which are primarily: a) the scarcity of high-quality data, b) the difficulty in interpreting the models, and c) the lack of generalization. Regarding data availability, geotechnical data can be costly and often incomplete or uncertain.…”

Section: Introductionmentioning

confidence: 99%

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A super-learner machine learning model for a global prediction of compression index in clays

Díaz,

Spagnoli

2024

Applied Clay Science

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A super-learner machine learning model for a global prediction of compression index in clays

Díaz,

Spagnoli

2024

Applied Clay Science

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“…In another finding, the modelling results of ANN estimated the mechanical properties of pond ash stabilized ES impressively (with a coefficient of correlation R ≈ 0.96) 58 . Recently, new empirical prediction models were developed by Jalal et al 31 for the determination of P s UCS -ES by deploying neural networks, i.e., ANN, adaptive neuro-fuzzy inference system (ANFIS), and genetic programming approach, i.e., GEP 59 , 60 . The results revealed that both the GEP as well as ANN are efficient methods to accurately compute P s UCS -ES.…”

Section: Introductionmentioning

confidence: 99%

ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength

Jalal,

Iqbal,

Khan

et al. 2024

Sci Rep

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This research suggests a robust integration of artificial neural networks (ANN) for predicting swell pressure and the unconfined compression strength of expansive soils (PsUCS-ES). Four novel ANN-based models, namely ANN-PSO (i.e., particle swarm optimization), ANN-GWO (i.e., grey wolf optimization), ANN-SMA (i.e., slime mould algorithm) alongside ANN-MPA (i.e., marine predators’ algorithm) were deployed to assess the PsUCS-ES. The models were trained using the nine most influential parameters affecting PsUCS-ES, collected from a broader range of 145 published papers. The observed results were compared with the predictions made by the ANN-based metaheuristics models. The efficacy of all these formulated models was evaluated by utilizing mean absolute error (MAE), Nash–Sutcliffe (NS) efficiency, performance index ρ, regression coefficient (R2), root mean square error (RMSE), ratio of RMSE to standard deviation of actual observations (RSR), variance account for (VAF), Willmott’s index of agreement (WI), and weighted mean absolute percentage error (WMAPE). All the developed models for Ps-ES had an R significantly > 0.8 for the overall dataset. However, ANN-MPA excelled in yielding high R values for training dataset (TrD), testing dataset (TsD), and validation dataset (VdD). This model also exhibited the lowest MAE of 5.63%, 5.68%, and 5.48% for TrD, TsD, and VdD, respectively. The results of the UCS model’s performance revealed that R exceeded 0.9 in the TrD. However, R decreased for TsD and VdD. Also, the ANN-MPA model yielded higher R values (0.89, 0.93, and 0.94) and comparatively low MAE values (5.11%, 5.67, and 3.61%) in the case of PSO, GWO, and SMA, respectively. The UCS models witnessed an overfitting problem because the aforementioned R values of the metaheuristics were 0.62, 0.56, and 0.58 (TsD), respectively. On the contrary, no significant observation was recorded in the VdD of UCS models. All the ANN-base models were also tested using the a-20 index. For all the formulated models, maximum points were recorded to lie within ± 20% error. The results of sensitivity as well as monotonicity analyses depicted trending results that corroborate the existing literature. Therefore, it can be inferred that the recently built swarm-based ANN models, particularly ANN-MPA, can solve the complexities of tuning the hyperparameters of the ANN-predicted PsUCS-ES that can be replicated in practical scenarios of geoenvironmental engineering.

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Reliability‐based state parameter liquefaction probability prediction using soft computing techniques

Kumar,

Samui,

Choudhary

2024

Geological Journal

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The state parameter (ѱ) accounts for both relative density and effective stress, which influence the cyclic stress or liquefaction characteristic of the soil significantly. This study presents a ѱ‐based probabilistic liquefaction evaluation method using six soft computing (SC) techniques. The liquefaction probability of failure (PL) is calculated using the first‐order second moment (FOSM) method based on the cone penetration test (CPT) database. Then, six SC techniques, such as Gaussian process regression (GPR), relevance vector machine (RVM), functional network (FN), genetic programming (GP), minimax probability machine regression (MPMR) and multivariate adaptive regression splines (MARS), are used to predict PL. The performance of these models is examined using nine statistical indices. Additionally, plots such as regression plots, Taylor diagrams, error matrix and rank analysis are shown to assess the SC model's performance. Finally, sensitivity analysis is performed using the cosine amplitude method (CAM) to assess the influence of input parameters on output. The current study demonstrates that SC models based on state parameter predict PL effectively. RVM and MPMR models closely follow the GPR model in terms of performance, which is superior to the other models. Notably, two equations are generated using GP and MARS models to predict PL. The results of the sensitivity analysis reveal the magnitude of earthquake (Mw) as the most sensitive parameter. The outcomes of this research will offer risk evaluations for geotechnical engineering designs and expand the use of state parameter‐based SC models in liquefaction analysis.

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A hybrid approach of ANN and improved PSO for estimating soaked CBR of subgrade soils of heavy-haul railway corridor

Cited by 15 publications

References 77 publications

A super-learner machine learning model for a global prediction of compression index in clays

A super-learner machine learning model for a global prediction of compression index in clays

ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength

Reliability‐based state parameter liquefaction probability prediction using soft computing techniques

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