Novel integration of extreme learning machine and improved Harris hawks optimization with particle swarm optimization-based mutation for predicting soil consolidation parameter

Bardhan, Abidhan; Kardani, Navid; Alzo’ubi, Abdel Kareem; Roy, Bappadittya; Samui, Pijush; Gandomi, Amir H.

doi:10.1016/j.jrmge.2021.12.018

Cited by 34 publications

(8 citation statements)

References 71 publications

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“…The outcomes of the ANNs are presented in were used to determine the predictive precision of the developed models (Bardhan et al, 2022(Bardhan et al, , 2021a(Bardhan et al, , 2021bBardhan and Samui, 2022b;Grover, 2023a, 2023b). Based on the performance, the developed models were raked as per score analysis.…”

Section: Discussion Of Resultsmentioning

confidence: 99%

Probabilistic analysis of pile foundations using Monte Carlo and Subset simulations compared with FOSM-based hybrid ANN paradigm

Suman,

Burman,

Choudhary

2024

Preprint

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The presence of uncertainty and variability in soil and sub-soils is a fundamental aspect of pile design. Thus, extensive study has been conducted to accurately measure the reliability or likelihood of structural failure. This work investigates the suitability of Monte Carlo and Subset simulations for the risk assessment of piles. In addition, First-order second-moment method (FOSM)-based hybrid artificial neural network (ANN) frameworks were used. Specifically, five hybrid ANNs were constructed using swarm intelligence algorithms A comparative analysis of Monte Carlo, Subset, FOSM, and FOSM-based hybrid ANN was conducted at different co-efficient of variation levels. The effectiveness of the utilized hybrid ANNs was determined using diverse statistical indices. Based on the performance, the best-fitted hybrid ANN was selected and utilized for risk assessment of pile foundations. According to the results, the employed ANN-MPA framework exhibit the best-fitted estimation with 99.2% (R2 = 0.9920) accuracy. The probability of failure was subsequently determined using Monte Carlo, Subset simulation and FOSM-based ANN-MPA methods. According to the results, the FOSM-based ANN-MPA approach can be considered as an alternative tool for quick estimation of risk assessment of pile foundations under different coefficient of variations levels.

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Section: Discussion Of Resultsmentioning

confidence: 99%

Probabilistic analysis of pile foundations using Monte Carlo and Subset simulations compared with FOSM-based hybrid ANN paradigm

Suman,

Burman,

Choudhary

2024

Preprint

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“…It is important to note that, right after model development, various performance metrics including Adj.R 2 , NS, PI, R 2 , RMSE, RSR, VAF, and WI, were used to evaluate hybrid LSSVMs. Note that these indices are frequently used [ 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 ] to evaluate the generalization capabilities of any prediction model from a variety of perspectives, including correlation accuracy, related error, variance, and so on. The expressions of these indices can be given as follows:

where p and

represent the total number of input parameters and observations, respectively; …”

Section: Resultsmentioning

confidence: 99%

Modelling Compression Strength of Waste PET and SCM Blended Cementitious Grout Using Hybrid of LSSVM Models

et al. 2022

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Nowadays, concretes blended with pozzolanic additives such as fly ash (FA), silica fume (SF), slag, etc., are often used in construction practices. The utilization of pozzolanic additives and industrial by-products in concrete and grouting materials has an important role in reducing the Portland cement usage, the CO2 emissions, and disposal issues. Thus, the goal of the present work is to estimate the compressive strength (CS) of polyethylene terephthalate (PET) and two supplementary cementitious materials (SCMs), namely FA and SF, blended cementitious grouts to produce green mix. For this purpose, five hybrid least-square support vector machine (LSSVM) models were constructed using swarm intelligence algorithms, including particle swarm optimization, grey wolf optimizer, salp swarm algorithm, Harris hawks optimization, and slime mold algorithm. To construct and validate the developed hybrid models, a sum of 156 samples were generated in the lab with varying percentages of PET and SCM. To estimate the CS, five influencing parameters, namely PET, SCM, FLOW, 1-day CS (CS1D), and 7-day CS (CS7D), were considered. The performance of the developed models was assessed in terms of multiple performance indices. Based on the results, the proposed LSSVM-PSO (a hybrid model of LSSVM and particle swarm optimization) was determined to be the best performing model with R2 = 0.9708, RMSE = 0.0424, and total score = 40 in the validation phase. The results of sensitivity analysis demonstrate that all the input parameters substantially impact the 28-day CS (CS28D) of cementitious grouts. Among them, the CS7D has the most significant effect. From the experimental results, it can be deduced that PET/SCM has no detrimental impact on CS28D of cementitious grouts, making PET a viable alternative for generating sustainable and green concrete. In addition, the proposed LSSVM-PSO model can be utilized as a novel alternative for estimating the CS of cementitious grouts, which will aid engineers during the design phase of civil engineering projects.

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“…The max_depth parameter controls the maximum depth of the individual decision trees, managing their capacity to capture complex relationships. Lastly, the learning_rate hyperparameter governs the contribution of each tree to the model, regulating the impact of new trees on the overall ensemble [75,76].…”

Section: Gradient Boosting Regressionmentioning

confidence: 99%

A Novel Approach to Swell Mitigation: Machine-Learning-Powered Optimal Unit Weight and Stress Prediction in Expansive Soils

Alnmr,

Ray,

Alzawi

2024

Applied Sciences

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Expansive soils pose significant challenges to structural integrity, primarily due to volumetric changes that can lead to detrimental consequences and substantial economic losses. This study delves into the intricate dynamics of expansive soils through loaded swelling pressure experiments conducted under diverse conditions, encompassing variations in the sand content, initial dry unit weight, and initial degree of saturation. The findings underscore the pronounced influence of these factors on soil swelling. To address these challenges, a novel method leveraging machine learning prediction models is introduced, offering an efficient and cost-effective framework to mitigate potential hazards associated with expansive soils. Employing advanced algorithms such as decision tree regression (DTR), random forest regression (RFR), gradient boosting regression (GBR), extreme gradient boosting (XGBoost), support vector regression (SVR), and artificial neural networks (ANN) in the Python software 3.11 environment, this study aims to predict the optimal applied stress and dry unit weight required for soil swelling mitigation. Results reveal that XGBoost and ANN stand out for their precision and superior metrics. While both performed well, ANN demonstrated exceptional consistency across training and testing phases, making it the preferred choice. In the tested dataset, ANN achieved the highest R-squared values (0.9917 and 0.9954), lowest RMSE (7.92 and 0.086), and lowest MAE (5.872 and 0.0488) for predicting optimal applied stress and dry unit weight, respectively.

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Novel integration of extreme learning machine and improved Harris hawks optimization with particle swarm optimization-based mutation for predicting soil consolidation parameter

Cited by 34 publications

References 71 publications

Probabilistic analysis of pile foundations using Monte Carlo and Subset simulations compared with FOSM-based hybrid ANN paradigm

Probabilistic analysis of pile foundations using Monte Carlo and Subset simulations compared with FOSM-based hybrid ANN paradigm

Modelling Compression Strength of Waste PET and SCM Blended Cementitious Grout Using Hybrid of LSSVM Models

A Novel Approach to Swell Mitigation: Machine-Learning-Powered Optimal Unit Weight and Stress Prediction in Expansive Soils

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