Rubberized geopolymer composites: A comprehensive review

Ceramics International, Journal Year: 2022, Volume and Issue: 48(17), P. 24234 - 24259

Published: June 13, 2022

Language: Английский

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Closed-Form Equation for Estimating Unconfined Compressive Strength of Granite from Three Non-destructive Tests Using Soft Computing Models

Rock Mechanics and Rock Engineering, Journal Year: 2022, Volume and Issue: 56(1), P. 487 - 514

Published: Oct. 11, 2022

Abstract The use of three artificial neural network (ANN)-based models for the prediction unconfined compressive strength (UCS) granite using non-destructive test indicators, namely pulse velocity, Schmidt hammer rebound number, and effective porosity, has been investigated in this study. For purpose, a sum 274 datasets was compiled used to train validate ANN including constructed Levenberg–Marquardt algorithm (ANN-LM), combination particle swarm optimization (ANN-PSO), imperialist competitive (ANN-ICA). ANN-LM model proven be most accurate based on experimental findings. In validation phase, achieved best predictive performance with R = 0.9607 RMSE 14.8272. Experimental results show that developed outperforms number existing available literature. Furthermore, Graphical User Interface (GUI) which can readily estimate UCS through model. GUI is made as supplementary material.

Language: Английский

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Artificial Intelligence and Structural Health Monitoring of Bridges: A Review of the State-of-the-Art

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 88058 - 88078

Published: Jan. 1, 2022

In the age of smart city, things like Internet Things (IoT) and big data analytics are making changes to way traditional structural health monitoring (SHM) is done. Also, capacity, flexibility, robustness artificial intelligence (AI) techniques for solving complex real-world problems have led an increasing interest in applying these methods SHM systems infrastructures recent years. Therefore, analytical evaluation advancements appears be important. The bridge one significant transportation where existing environmental destructive variables can a negative impact on structure's life health. system bridges different stages their cycle, such as construction, development, management, maintenance, seen complementary part intelligent (ITS). main goal this study look at how AI used improve current state art data-driven bridges, including conceptual frameworks, advantages, challenges, well approaches. This article presents overview role future. Finally, some potential research possibilities AI-assisted also emphasized detailed.

Language: Английский

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

Ultrasonics, Journal Year: 2024, Volume and Issue: 141, P. 107347 - 107347

Published: May 20, 2024

Language: Английский

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Use of interpretable machine learning approaches for quantificationally understanding the performance of steel fiber-reinforced recycled aggregate concrete: From the perspective of compressive strength and splitting tensile strength

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 137, P. 109170 - 109170

Published: Aug. 27, 2024

Language: Английский

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Comparative analysis of machine learning models for predicting dielectric properties in MoS2 nanofiller-reinforced epoxy composites

Ain Shams Engineering Journal, Journal Year: 2024, Volume and Issue: 15(6), P. 102754 - 102754

Published: March 14, 2024

This research investigates the dielectric properties of nano epoxy composites by incorporating various concentrations MoS2 into resin. The study explores impact synthesized nanoparticles on undoped composites, specifically focusing their potential applications in materials. experimental synthesis and characterization nanoepoxy typically involve time-consuming expensive methods. compares five machine learning (ML) models—random forests, decision trees, extra XGBoost, gradient boosting—in order to predict frequency-dependent constants these under different nanofiller variations address this challenge. To ensure robust model performance, training is carried out subsets dataset, ranging from 60% 30%, while remaining portions are reserved for testing purposes (40% 70%). main objective assess performance each regressor technique using metrics such as adjusted R2 score, MSE, RMSE, MAE, which ET excels. method demonstrates exceptional achieving an value 0.9977 0.9912 target variables ε′ ε′′, respectively when tested with a size 0.4. findings underscore ML models precise efficient prediction nanofillers, offering alternative laboratory work.

Language: Английский

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Prediction of Compressive Strength of Geopolymer Concrete Landscape Design: Application of the Novel Hybrid RF–GWO–XGBoost Algorithm

Buildings, Journal Year: 2024, Volume and Issue: 14(3), P. 591 - 591

Published: Feb. 22, 2024

Landscape geopolymer concrete (GePoCo) with environmentally friendly production methods not only has a stable structure but can also effectively reduce environmental damage. Nevertheless, GePoCo poses challenges its intricate cementitious matrix and vague mix design, where the components their relative amounts influence compressive strength. In response to these challenges, application of accurate applicable soft computing techniques becomes imperative for predicting strength such composite matrix. This research aimed predict using waste resources through novel ensemble ML algorithm. The dataset comprised 156 statistical samples, 15 variables were selected prediction. model employed combination RF, GWO algorithm, XGBoost. A stacking strategy was implemented by developing multiple RF models different hyperparameters, combining outcome predictions into new dataset, subsequently XGBoost model, termed RF–XGBoost model. To enhance accuracy errors, algorithm optimized hyperparameters resulting in RF–GWO–XGBoost proposed compared stand-alone models, hybrid GWO–XGBoost system. results demonstrated significant performance improvement strategies, particularly assistance exhibited better effectiveness, an RMSE 1.712 3.485, R2 0.983 0.981. contrast, (RF XGBoost) lower performance.

Language: Английский

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Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 28, 2024

Abstract The present research employs new boosting-based ensemble machine learning models i.e., gradient boosting (GB) and adaptive (AdaBoost) to predict the unconfined compressive strength (UCS) of geopolymer stabilized clayey soil. GB AdaBoost were developed validated using 270 soil samples with geopolymer, ground-granulated blast-furnace slag fly ash as source materials sodium hydroxide solution alkali activator. database was randomly divided into training (80%) testing (20%) sets for model development validation. Several performance metrics, including coefficient determination (R 2 ), mean absolute error (MAE), root square (RMSE), squared (MSE), utilized assess accuracy reliability models. statistical results this showed that are reliable based on obtained values R (= 0.980, 0.975), MAE 0.585, 0.655), RMSE 0.969, 1.088), MSE 0.940, 1.185) dataset, respectively compared widely used artificial neural network, random forest, extreme boosting, multivariable regression, multi-gen genetic programming Furthermore, sensitivity analysis result shows content key parameter affecting UCS.

Language: Английский

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A case study of resilient modulus prediction leveraging an explainable metaheuristic-based XGBoost

Transportation Geotechnics, Journal Year: 2024, Volume and Issue: 45, P. 101216 - 101216

Published: Feb. 18, 2024

Language: Английский

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Towards Designing Durable Sculptural Elements: Ensemble Learning in Predicting Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete

Buildings, Journal Year: 2024, Volume and Issue: 14(2), P. 396 - 396

Published: Feb. 1, 2024

Fiber-reinforced nano-silica concrete (FrRNSC) was applied to a sculpture address the issue of brittle fracture, and primary objective this study explore potential hybridizing Grey Wolf Optimizer (GWO) with four robust intelligent ensemble learning techniques, namely XGBoost, LightGBM, AdaBoost, CatBoost, anticipate compressive strength fiber-reinforced for sculptural elements. The optimization hyperparameters these techniques performed using GWO metaheuristic algorithm, enhancing accuracy through creation hybrid models: GWO-XGBoost, GWO-LightGBM, GWO-AdaBoost, GWO-CatBoost. A comparative analysis conducted between results obtained from models their conventional counterparts. evaluation is based on five key indices: R2, RMSE, VAF, MAE, bias, addressing an assessment predictive models’ performance capabilities. outcomes reveal that exhibiting R2 values (0.971 0.978) train test stages, respectively, emerges as best model estimating compared other models. Consequently, proposed GWO-XGBoost algorithm proves be efficient tool anticipating CSFrRNSC.

Language: Английский

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