Prediction of compaction parameters of compacted soil using LSSVM, LSTM, LSBoostRF, and ANN

Khatti, Jitendra; Grover, Kamaldeep Singh

doi:10.1007/s41062-023-01048-2

Cited by 33 publications

(6 citation statements)

References 70 publications

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“…Sensitivity analysis (S A ) was been conducted on the comprehensive databases to understand the impact of the input factors on the target. The sensitivity analysis (S A ) for a particular input (p) could be calculated using the following method 81,82…”

Section: Sensitivity Analysismentioning

confidence: 99%

Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete

Esmaeili‐Falak,

Sarkhani Benemaran

2024

Structural Concrete

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Concrete constructed using recycled aggregates in place of natural aggregates is an efficient approach to increase the construction sector's sustainability. To improve recycled aggregate concrete () technologies in permafrost, it is essential to certify the stability in frost‐induced conditions. The main goal of this study was to use support vector regression () methods to forecast the frost durability () of on the basis of durability agent value in cold climates. Herein, three optimization methods called Ant lion optimization (), Grey wolf optimization (), and Henry Gas Solubility Optimization () were employed for indicating optimal values of key parameters. The results depicted that all developed models have capability in predicting the of in cold regions. The values of as a comprehensive index depicted that the model has the higher value at 0.0571 as the weakest model, then at 0.0312 recognized as the second‐highest model, and finally the system at 0.0224 mentioned as outperformed model. and approaches were likewise capable of accurately forecasting the of in cold regions, but the created method outperformed them all when taking into account the explanations and justifications.

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Section: Sensitivity Analysismentioning

confidence: 99%

Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete

Esmaeili‐Falak,

Sarkhani Benemaran

2024

Structural Concrete

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“…Therefore, it is important to determine the appropriate number of hidden layers and neurons. According to existing research, ANNs with one hidden layer have been able to meet most of the requirements [41][42][43][44][45]. Therefore, in this paper, an ANN with one hidden layer is used to study the effect of the number of neurons on the prediction model, and the results are shown in Table 3.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Prediction of Soil Compaction Parameters Using Machine Learning Models

Li,

You,

et al. 2024

Applied Sciences

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Maximum Dry Density (MDD) and Optimum Moisture Content (OMC) are two important parameters of soil filling, which affect the soil stability and bearing capacity, and thus the reliability and durability of facilities such as highways and bridges. Therefore, it is important to make reasonable predictions of OMC and MDD. Four machine learning algorithms, namely, Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and Extreme Gradient Boosting Tree (XGBoost), are adopted in this paper to establish MDD and OMC prediction models. After training and testing, the best models of the four algorithms are compared. The results show that, as an ensemble learning algorithm, XGBoost is the best model for predicting MDD and OMC, with an R2 of 0.9234 for OMC, and an R2 of 0.9098 for MDD. Finally, the feature importance analysis concludes that the plastic limit (PL) and the liquid limit (LL) are the two features that affect OMC and MDD the most. The prediction of soil compaction parameters using machine learning models, especially ensemble learning, can significantly reduce the amount of laboratory work and improve the efficiency of optimizing design for soil resource utilization in engineering construction.

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“…One hundred sixty-two datasets were used for the proposed model construction, with 80% training and 20% testing databases. Khatti and Grover 90 analyzed the impact of hidden layers and neurons on the ANN model's performance in predicting compacted soil's compaction parameters. In the published work, authors observed that the performance of the ANN model increases with neurons, i.e., up to 10.…”

Section: Data Analysis and Soft Computing Approachesmentioning

confidence: 99%

“…Sixteen performance metrics have been implemented in this research. The mathematical formulation of the performance metrics is as follows 90 , 93 – 97 :…”

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

Self Cite

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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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Prediction of compaction parameters of compacted soil using LSSVM, LSTM, LSBoostRF, and ANN

Cited by 33 publications

References 70 publications

Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete

Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete

Prediction of Soil Compaction Parameters Using Machine Learning Models

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

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