Assessment of fine-grained soil compaction parameters using advanced soft computing techniques

Khatti, Jitendra; Grover, Kamaldeep Singh

doi:10.1007/s12517-023-11268-6

Cited by 41 publications

(2 citation statements)

References 62 publications

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“…The performance of the machine learning models was assessed using several metrics. The mathematical formulation of the performance metrics is as follows 37 – 39 :…”

Section: Data Analysis and Computational Methodsmentioning

confidence: 99%

Assessment of small strain modulus in soil using advanced computational models

Fan,

Hang,

Song

et al. 2023

Sci Rep

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Small-strain shear modulus ($$G_0$$ G 0 ) of soils is a crucial dynamic parameter that significantly impacts seismic site response analysis and foundation design. $$G_0$$ G 0 is susceptible to multiple factors, including soil uniformity coefficient ($$C_u$$ C u ), void ratio (e), mean particle size ($$d_{50}$$ d 50 ), and confining stress ($$\sigma '$$ σ ′ ). This study aims to establish a $$G_0$$ G 0 database and suggests three advanced computational models for $$G_0$$ G 0 prediction. Nine performance indicators, including four new indices, are employed to calculate and analyze the model’s performance. The XGBoost model outperforms the other two models, with all three models achieving $$R^2$$ R 2 values exceeding 0.9, RMSE values below 30, MAE values below 25, VAF values surpassing 80%, and ARE values below 50%. Compared to the empirical formula-based traditional prediction models, the model proposed in this study exhibits better performance in IOS, IOA, a20-index, and PI metrics values. The model has higher prediction accuracy and better generalization ability.

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“…The performance of the machine learning models was assessed using several metrics. The mathematical formulation of the performance metrics is as follows 37 – 39 :…”

Section: Data Analysis and Computational Methodsmentioning

confidence: 99%

Assessment of small strain modulus in soil using advanced computational models

Fan,

Hang,

Song

et al. 2023

Sci Rep

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show abstract

“…This research has employed MLR, LSSVM, SVM, GPR, DT, ET, ANN, and LSTM models to assess the ground vibrations during blasting in a mining project. These models have been configured by analyzing the published research of Taiwo et al 50 , 51 , Hosseini et al 52 , 53 , Wang et al 54 , Khatti and Grover 55 – 58 .…”

Section: Data Analysis and Soft Computing Approachesmentioning

confidence: 99%

Assessment of the ground vibration during blasting in mining projects using different computational approaches

Hosseini,

Khatti,

Taiwo

et al. 2023

Sci Rep

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The investigation compares the conventional, advanced machine, deep, and hybrid learning models to introduce an optimum computational model to assess the ground vibrations during blasting in mining projects. The long short-term memory (LSTM), artificial neural network (ANN), least square support vector machine (LSSVM), ensemble tree (ET), decision tree (DT), Gaussian process regression (GPR), support vector machine (SVM), and multilinear regression (MLR) models are employed using 162 data points. For the first time, the blackhole-optimized LSTM model has been used to predict the ground vibrations during blasting. Fifteen performance metrics have been implemented to measure the prediction capabilities of computational models. The study concludes that the blackhole optimized-LSTM model PPV11 is highly capable of predicting ground vibration. Model PPV11 has assessed ground vibrations with RMSE = 0.0181 mm/s, MAE = 0.0067 mm/s, R = 0.9951, a20 = 96.88, IOA = 0.9719, IOS = 0.0356 in testing. Furthermore, this study reveals that the prediction accuracy of hybrid models is less affected by multicollinearity because of the optimization algorithm. The external cross-validation and literature validation confirm the prediction capabilities of model PPV11. The ANOVA and Z tests reject the null hypothesis for actual ground vibration, and the Anderson–Darling test rejects the null hypothesis for predicted ground vibration. This study also concludes that the GPR and LSSVM models overfit because of moderate to problematic multicollinearity in assessing ground vibration during blasting.

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Estimation of Heavy Compaction Parameters Using Light Compaction Parameters of Granular Soil

Khatti,

Grover

2024

Lecture Notes in Civil Engineering

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Assessment of fine-grained soil compaction parameters using advanced soft computing techniques

Cited by 41 publications

References 62 publications

Assessment of small strain modulus in soil using advanced computational models

Assessment of small strain modulus in soil using advanced computational models

Assessment of the ground vibration during blasting in mining projects using different computational approaches

Estimation of Heavy Compaction Parameters Using Light Compaction Parameters of Granular Soil

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