Mixture Proportioning for Oil Palm Kernel Shell

Gibigaye, Mohamed; Godonou, Gildas Fructueux

doi:10.5772/intechopen.75601

Cited by 5 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The compressive strength data of 120 sets of recycled aggregate concrete used in this experiment are compiled from the collation of internationally published papers [22][23][24][25][26][27] . The data were divided into 96 sets of training samples and 24 sets of test samples according to the ratio of 8:2, of which the training samples were used to train the model and the test samples were used to test the model and verify the model performance.…”

Section: Discussionmentioning

confidence: 99%

Compressive strength prediction of recycled aggregate concrete based on DE-XGBoost algorithm model and explainable machine learning

Guo,

luo,

Tian

et al. 2023

Preprint

View full text Add to dashboard Cite

In the current concrete performance prediction research, common machine learning algorithms include random forest (RF), gradient boosting decision tree (GBDT), LightGBM (Light Gradient Boosting Machine), extreme gradient boosting tree (XGBoost), etc. Based on the compressive strength data of recycled aggregate concrete at different ages under different mixing ratios, this paper establishes a combined model (DE-XGBoost) of differential evolution algorithm (DE) and extreme gradient boosting tree algorithm (XGBoost), using XGBoost, Light GBM, GBDT, RF models as comparisons, and R², RMSE, MSE and MAE as model evaluation indexes. Use interpretability machine learning algorithms (SHAPs) to explore the feature importance of model input features to output results. The results show that: (1) In a single model, the prediction accuracy of XGBoost model (R²=0.9599) is better than that of Light GBM (R²=0.9493), GBDT (R²=0.9459), RF (R²=0.9321) model; (2) The prediction accuracy of the DE-XGBoost combination model is improved by 1.12% compared with the XGBoost model, and the RMSE, MSE and MAE values are reduced by 16.09%, 29.60% and 27.77%, respectively.

show abstract

Section: Discussionmentioning

confidence: 99%

Compressive strength prediction of recycled aggregate concrete based on DE-XGBoost algorithm model and explainable machine learning

Guo,

luo,

Tian

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…For this study, we used an OPKS concrete grade-20. e constituents used in the mix were those indicated in the study [21]. e properties of the constituents used are presented in Table 1.…”

Section: Materials and MIXmentioning

confidence: 99%

“…We used the mix proportion of cement : sand : OPKS in weights of 1 : 1.53 : 0.99 with a cement content of 450 kg/m 3 and water per cement equal to 0.45 [21].…”

Section: Materials and MIXmentioning

confidence: 99%

“…is implies that if the results of a theoretical mixture proportioning of OPKSC are available, as in the approach proposed in [21], it is possible to estimate Young's modulus of the concrete; this enables the easy execution of the analysis of structural elements in lightly loaded buildings, based on OPKSC.…”

Section: Influence Of the Volume Fraction Of Opks On Young's Modulus mentioning

confidence: 99%

See 1 more Smart Citation

Estimation of Elastic Properties of Oil Palm Kernel Shell Concrete by Semianalytical Methods of Homogenisation

Gibigaye

Godonou

Yabi

et al. 2019

Advances in Civil Engineering

Self Cite

View full text Add to dashboard Cite

Despite its importance as one of the key parameters in the design of structural elements, the modulus of elasticity (MoE) is one of the least researched areas in oil palm kernel shell (OPKS) concrete. In the present study, we determined the MoE of OPKS concrete, using micromechanical models based on the classic approaches of homogenisation of Hashin–Shtrikman (HS) and Mori–Tanaka (MT). The MoE values for OPKSC (fcu < 35 MPa and OPKS volume fraction < 42%) estimated using the HS and MT models in the literature showed a good correlation with the experimental values. An empirical linear correlation between the volume fraction of inclusions and the MoE was proposed. The results obtained can enable better control of the mix design of structural concrete based on the proportion of OPKS coarse aggregate in tropical countries producing palm oil.

show abstract

Conventional and Ensemble Machine Learning Techniques to Predict the Compressive Strength of Sustainable Concrete

Ansari,

Ibrahim,

Hasan

et al. 2024

Lecture Notes in Civil Engineering

View full text Add to dashboard Cite

Mixture Proportioning for Oil Palm Kernel Shell

Cited by 5 publications

References 12 publications

Compressive strength prediction of recycled aggregate concrete based on DE-XGBoost algorithm model and explainable machine learning

Compressive strength prediction of recycled aggregate concrete based on DE-XGBoost algorithm model and explainable machine learning

Estimation of Elastic Properties of Oil Palm Kernel Shell Concrete by Semianalytical Methods of Homogenisation

Conventional and Ensemble Machine Learning Techniques to Predict the Compressive Strength of Sustainable Concrete

Contact Info

Product

Resources

About