Peak and ultimate stress-strain model of confined ultra-high-performance concrete (UHPC) using hybrid machine learning model with conditional tabular generative adversarial network

Wakjira, Tadesse G.; Alam, M. Shahria

doi:10.1016/j.asoc.2024.111353

Cited by 24 publications

(2 citation statements)

References 39 publications

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“…is the activation function, and h is the output vector of the activation function. Support Vector Regression (SVR) is a commonly used supervised machine learning algorithm that makes predictions through the optimization of structural risk [28]. The SVR model was proposed by Vapnik in 1992 [29].…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Predicting Rutting Development Using Machine Learning Methods Based on RIOCHTrack Data

Cheng,

Wang,

Zhou

et al. 2024

Applied Sciences

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As the main cause of asphalt pavement distress, rutting severely affects pavement safety. Establishing an accurate rutting prediction model is crucial for asphalt pavement maintenance, pavement structure design, and pavement repair. This study explores five machine learning methods, namely Support Vector Regression (SVR), Artificial Neural Network (ANN), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Extra Trees, to predict the development of rutting depth using data from RIOHTRack. The model’s performance is measured by comparing the performance evaluation indicators of different models, such as the coefficient of determination, root mean square error, mean absolute error, and mean absolute percentage error. The results demonstrate that integrated learning techniques such as RF, GBDT, and Extra Trees works best with R2 = 0.9761, 0.9833, and 0.9747. Moreover, the GBFT model can capture the trend of the measured rutting progression curve better than the mechanistic-empirical (M-E) model. The analysis of feature importance reveals that, in addition to external factors such as temperature and axle load, the aggregate of the asphalt concrete layer and air void crucially affect rutting. The higher the base strength, the smaller the rutting depth. The proposed model is highly straightforward and serves as an accessible analysis tool for engineers in practice.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Predicting Rutting Development Using Machine Learning Methods Based on RIOCHTrack Data

Cheng,

Wang,

Zhou

et al. 2024

Applied Sciences

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show abstract

“…In future research, we will include different optimization algorithms in the comparison to create more comprehensive predictive models. Moreover, a next-generation hyperparameter optimization framework (e.g., Optuna [88][89][90][91]) can be considered for the compressive strength modeling of geopolymer concrete, which will be of great significance for the strength prediction of geopolymer concrete and more reliable material design in the future.…”

mentioning

confidence: 99%

Towards a Reliable Design of Geopolymer Concrete for Green Landscapes: A Comparative Study of Tree-Based and Regression-Based Models

Wang,

Zhang,

et al. 2024

Buildings

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The design of geopolymer concrete must meet more stringent requirements for the landscape, so understanding and designing geopolymer concrete with a higher compressive strength challenging. In the performance prediction of geopolymer concrete compressive strength, machine learning models have the advantage of being more accurate and faster. However, only a single machine learning model is usually used at present, there are few applications of ensemble learning models, and model optimization processes is lacking. Therefore, this paper proposes to use the Firefly Algorithm (AF) as an optimization tool to perform hyperparameter tuning on Logistic Regression (LR), Multiple Logistic Regression (MLR), decision tree (DT), and Random Forest (RF) models. At the same time, the reliability and efficiency of four integrated learning models were analyzed. The model was used to analyze the influencing factors of geopolymer concrete and determine the strength of their influencing ability. According to the experimental data, the RF-AF model had the lowest RMSE value. The RMSE value of the training set and test set were 4.0364 and 8.7202, respectively. The R value of the training set and test set were 0.9774 and 0.8915, respectively. Therefore, compared with the other three models, RF-AF has a stronger generalization ability and higher prediction accuracy. In addition, the molar concentration of NaOH was the most important influencing factors, and its influence was far greater than the other possible factors including NaOH content. Therefore, it is necessary to pay more attention to NaOH molarity when designing geopolymer concrete.

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Enhancing network intrusion detection: a dual-ensemble approach with CTGAN-balanced data and weak classifiers

Soflaei,

Salehpour,

Samadzamini

2024

J Supercomput

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Peak and ultimate stress-strain model of confined ultra-high-performance concrete (UHPC) using hybrid machine learning model with conditional tabular generative adversarial network

Cited by 24 publications

References 39 publications

Predicting Rutting Development Using Machine Learning Methods Based on RIOCHTrack Data

Predicting Rutting Development Using Machine Learning Methods Based on RIOCHTrack Data

Towards a Reliable Design of Geopolymer Concrete for Green Landscapes: A Comparative Study of Tree-Based and Regression-Based Models

Enhancing network intrusion detection: a dual-ensemble approach with CTGAN-balanced data and weak classifiers

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