Rubberized geopolymer composites: A comprehensive review

Ceramics International, Год журнала: 2022, Номер 48(17), С. 24234 - 24259

Опубликована: Июнь 13, 2022

Язык: Английский

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Prediction of concrete materials compressive strength using surrogate models

Structures, Год журнала: 2022, Номер 46, С. 1243 - 1267

Опубликована: Ноя. 10, 2022

Язык: Английский

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Metamodel techniques to estimate the compressive strength of UHPFRC using various mix proportions and a high range of curing temperatures

Construction and Building Materials, Год журнала: 2022, Номер 349, С. 128737 - 128737

Опубликована: Авг. 18, 2022

Язык: Английский

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Predicting uniaxial compressive strength of rocks using ANN models: Incorporating porosity, compressional wave velocity, and schmidt hammer data

Ultrasonics, Год журнала: 2024, Номер 141, С. 107347 - 107347

Опубликована: Май 20, 2024

Язык: Английский

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Mapping the strength of agro-ecological lightweight concrete containing oil palm by-product using artificial intelligence techniques

Structures, Год журнала: 2023, Номер 48, С. 1209 - 1229

Опубликована: Янв. 11, 2023

Язык: Английский

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Estimating compressive strength of concrete containing rice husk ash using interpretable machine learning-based models

Case Studies in Construction Materials, Год журнала: 2024, Номер 20, С. e02901 - e02901

Опубликована: Янв. 19, 2024

The construction sector is a major contributor to global greenhouse gas emissions. Using recycled and waste materials in concrete practical solution address environmental challenges. Currently, agricultural widely used as substitute for cement the production of eco-friendly concrete. However, traditional methods assessing strength such are both expensive time-consuming. Therefore, this study uses machine learning techniques develop prediction models compressive (CS) rice husk ash (RHA) ML present include random forest (RF), light gradient boosting (LightGBM), ridge regression, extreme (XGBoost). A total 348 values CS were collected from experimental studies, five characteristics RHA taken input variables. For performance assessment models, multiple statistical metrics used. During training phase, correlation coefficients (R) obtained RF, XGBoost, LightGBM 0.943, 0.981, 0.985, 0.996, respectively. In testing set, these demonstrated even higher performance, with 0.971, 0.993, 0.992, 0.998 LightGBM, analysis revealed that model outperformed other whereas regression exhibited comparatively lower accuracy. SHapley Additive exPlanation (SHAP) method was employed interpretability developed model. SHAP water-to-cement controlling parameter estimating conclusion, provides valuable guidance builders researchers estimate it suggested more variables be incorporated hybrid utilized further enhance reliability precision models.

Язык: Английский

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Hybrid nonlinear regression model versus MARS, MEP, and ANN to evaluate the effect of the size and content of waste tire rubber on the compressive strength of concrete

Heliyon, Год журнала: 2024, Номер 10(4), С. e25997 - e25997

Опубликована: Фев. 1, 2024

Tire rubber waste is globally accumulated every year. Therefore, a solution to this problem should be found since, if landfilled, it not biodegradable and causes environmental issues. One of the most effective ways recycling those wastes or using them as replacement for normal aggregate in concrete mixture, which has high impact resistance toughness; thus, will good choice. In study, 135 data were collected from previous literature develop model prediction rubberized compressive strength; database comprised different mixture proportions, maximum size (1-40 mm), percentage (0-100%) replacing natural fine coarse aggregates among input parameters addition cement content (380-500 kg/m

Язык: Английский

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State-of-the-art XGBoost, RF and DNN based soft-computing models for PGPN piles

Geomechanics and Geoengineering, Год журнала: 2024, Номер 19(6), С. 975 - 990

Опубликована: Апрель 17, 2024

Machine learning (ML) has made significant advancements in predictive modelling across many engineering sectors. However, predicting the bearing capacity of pre-bored grouted planted nodular (PGPN) piles remains a relatively unexplored area due to complexity load-bearing mechanism, pile-soil interactions, and multiple variables involved. The study utilises state-of-the-art ML techniques such as extreme gradient boosting (XGBoost), random forest (RF), machines (GBMs), deep learning-based simulation models. dataset fed into model comprises 81 case histories static pile load tests conducted various regions Vietnam. data was validated using descriptive statistics, sensitivity analysis, correlation matrix displays, SHAP plot regression curves, with performance through k-fold cross-validation. Among all models tested, XGBoost (R2 = 0.91, RMSE 0.09) RF 0.82, performed best, while neural network also yielded satisfactory results. GBM found not be robust for this analysis. visually analysed Violin comparisons Taylor diagrams. outcome facilitates safe economical designs eco-friendly pile.

Язык: Английский

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Assessment of the uniaxial compressive strength of intact rocks: an extended comparison between machine and advanced machine learning models

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2024, Номер 7(4), С. 3301 - 3325

Опубликована: Март 26, 2024

Язык: Английский

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Deep Learning and Genetic Programming-Based Soft-Computing Prediction Models for Metakaolin Mortar

Transportation Infrastructure Geotechnology, Год журнала: 2025, Номер 12(1)

Опубликована: Янв. 1, 2025

Язык: Английский

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