Hybrid soft computing models for predicting unconfined compressive strength of lime stabilized soil using strength property of virgin cohesive soil

Bahmed, Ismehen Taleb; Khatti, Jitendra; Grover, Kamaldeep Singh

doi:10.1007/s10064-023-03537-1

Cited by 22 publications

(2 citation statements)

References 84 publications

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“…In the last few years, some researchers applied the machine learning and soft computing-based techniques to estimate both UCS and E of various rock types [e.g. 9 – 23 ]. In this regard, Ghasemi et al [ 24 ] applied model trees as a predicting approach and Schmidt hardness, effective porosity, dry unit weight, P‐wave velocity, and slake durability index as input variables for predicting the UCS and E. They found that pruned and unpruned model trees provide suitable predictions of the parameters.…”

Section: Introductionmentioning

confidence: 99%

Non-destructive test-based assessment of uniaxial compressive strength and elasticity modulus of intact carbonate rocks using stacking ensemble models

Fereidooni,

Karimi,

Ghasemi

2024

PLoS ONE

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The uniaxial compressive strength (UCS) and elasticity modulus (E) of intact rock are two fundamental requirements in engineering applications. These parameters can be measured either directly from the uniaxial compressive strength test or indirectly by using soft computing predictive models. In the present research, the UCS and E of intact carbonate rocks have been predicted by introducing two stacking ensemble learning models from non-destructive simple laboratory test results. For this purpose, dry unit weight, porosity, P‐wave velocity, Brinell surface harnesses, UCS, and static E were measured for 70 carbonate rock samples. Then, two stacking ensemble learning models were developed for estimating the UCS and E of the rocks. The applied stacking ensemble learning method integrates the advantages of two base models in the first level, where base models are multi-layer perceptron (MLP) and random forest (RF) for predicting UCS, and support vector regressor (SVR) and extreme gradient boosting (XGBoost) for predicting E. Grid search integrating k-fold cross validation is applied to tune the parameters of both base models and meta-learner. The results demonstrate the generalization ability of the stacking ensemble method in the comparison of base models in the terms of common performance measures. The values of coefficient of determination (R2) obtained from the stacking ensemble are 0.909 and 0.831 for predicting UCS and E, respectively. Similarly, the stacking ensemble yielded Root Mean Squared Error (RMSE) values of 1.967 and 0.621 for the prediction of UCS and E, respectively. Accordingly, the proposed models have superiority in the comparison of SVR and MLP as single models and RF and XGBoost as two representative ensemble models. Furthermore, sensitivity analysis is carried out to investigate the impact of input parameters.

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Section: Introductionmentioning

confidence: 99%

Non-destructive test-based assessment of uniaxial compressive strength and elasticity modulus of intact carbonate rocks using stacking ensemble models

Fereidooni,

Karimi,

Ghasemi

2024

PLoS ONE

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“…Furthermore, machine learning techniques have found extensive applications in engineering. For instance, references 16 – 18 demonstrate modeling using the cosine amplitude method, and relevant algorithms were then employed to analyze and predict various engineering issues. Simultaneously, numerous machine learning studies to solve engineering problems have proposed several valuable and innovative designs 19 – 22 .…”

Section: Introductionmentioning

confidence: 99%

Calibration of contact parameters of sandy soil for planting tiger nut based on non-linear tools

Qi,

Chen,

Yang

et al. 2024

Sci Rep

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A methodology combining physical experiments with simulation was employed to acquire contact parameters of sandy soil precisely for planting tiger nuts in the desert area of Xinjiang. The stacking angle under different parameter combinations was applied as a response value. Through the Plackett–Burman test, several factors that have a significant influence were determined. The steepest ascent test was conducted to establish the finest scope of values for these parameters. The stacking angle was considered the response variable, and non-linear tools were used to optimize these parameters for simulation. The findings showed that applying response surface methodology (RSM) resulted in a relative error of 1.24%. In the case of BP-GA, the relative error compared to the physical test value was 0.34%, while for BP, it was 2.18%. After optimization using Wavelet Neural Network (WNN), the relative error was reduced to only 0.15%. Results suggest that WNN outperforms the RSM model, and the sandy soil model and parameters generated using WNN can be effectively utilized for discrete element simulation research.

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Determination of Compaction Parameters of Cement‐Lime Soils: Boosting‐Based Ensemble Models

Tilahun,

Qinghua,

Ashongo

et al. 2024

Num Anal Meth Geomechanics

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This study investigates the application of artificial intelligence (AI) models to predict soil compaction characteristics, specifically maximum dry density (MDD) and optimum moisture content (OMC), which are critical for stabilizing construction foundations. Traditional methods for determining MDD and OMC are labor‐intensive and often influenced by factors such as soil type, plasticity, and compaction energy (E). The research employed AI models, including random forest regression (RF‐R), gradient boosting regression (GB‐R), XGBoosting regressor (XGB‐R), and multilinear regression (ML‐R), trained on a comprehensive dataset of soil properties. For the first time, compaction energy has been used as an input variable to predict soil cement lime stabilized compaction parameters. Among the models, GB‐R demonstrated the highest prediction accuracy for MDD and OMC, outperforming RF‐R, XGB‐R, and ML‐R. The performance of built‐in models has been measured by three new index performance metrics: the a20‐index, the index of scatter (IS), and the index of agreement (IA), in addition to four common metrics. Taylor diagrams confirmed the robustness of these predictions during lab testing. A sensitivity analysis revealed that MDD and OMC were most influenced by plastic limit (PL), compaction energy (E), liquid limit (LL), and plasticity index (PI). Additionally, curve‐fitting techniques were applied to model the relationship between MDD, OMC, and these key factors. The results indicated that the GB‐R model, particularly when focused on essential features, provided superior accuracy compared to traditional regression methods, offering a reliable tool for soil stabilization assessments in construction.

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Hybrid soft computing models for predicting unconfined compressive strength of lime stabilized soil using strength property of virgin cohesive soil

Cited by 22 publications

References 84 publications

Non-destructive test-based assessment of uniaxial compressive strength and elasticity modulus of intact carbonate rocks using stacking ensemble models

Non-destructive test-based assessment of uniaxial compressive strength and elasticity modulus of intact carbonate rocks using stacking ensemble models

Calibration of contact parameters of sandy soil for planting tiger nut based on non-linear tools

Determination of Compaction Parameters of Cement‐Lime Soils: Boosting‐Based Ensemble Models

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