Strength prediction models for recycled aggregate concrete using Random Forests, ANN and LASSO

Rizvon, Shamili Syed; Karthikeyan, J.

doi:10.1007/s41024-021-00145-y

Cited by 16 publications

(3 citation statements)

References 28 publications

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“…This study covered the literature review about ANN classifier from previous studies as follow: (Mishra et al, 2017;Kadavi and Lee, 2018;Dibs et al, 2020;Dixit and Agarwal, 2020;Ekumah et al, 2020;Hamad, 2020;Kaya and Görgün, 2020;MohanRajan et al, 2020;Navin and Agilandeeswari, 2020;Rojas et al, 2020;Saddique et al, 2020;Xu et al, 2020;Angessa et al, 2021;Bhattacharya et al, 2021;Dede et al, 2021;Ghayour et al, 2021;Sang et al, 2021;Xie et al, 2021;Yusof et al, 2021;Ambinakudige and Intsiful, 2022;Fantinel et al, 2022;Gogumalla et al, 2022;Rizvon and Jayakumar, 2022;Theres and Selvakumar, 2022).…”

Section: Ann Classifiermentioning

confidence: 99%

Classification of land use/land cover using artificial intelligence (ANN-RF)

Alshari

Abdulkareem

Gawali

2023

Front. Artif. Intell.

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Because deep learning has various downsides, such as complexity, expense, and the need to wait longer for results, this creates a significant incentive and impetus to invent and adopt the notion of developing machine learning because it is simple. This study intended to increase the accuracy of machine-learning approaches for land use/land cover classification using Sentinel-2A, and Landsat-8 satellites. This study aimed to implement a proposed method, neural-based with object-based, to produce a model addressed by artificial neural networks (limited parameters) with random forest (hyperparameter) called ANN_RF. This study used multispectral satellite images (Sentinel-2A and Landsat-8) and a normalized digital elevation model as input datasets for the Sana'a city map of 2016. The results showed that the accuracy of the proposed model (ANN_RF) is better than the ANN classifier with the Sentinel-2A and Landsat-8 satellites individually, which may contribute to the development of machine learning through newer researchers and specialists; it also conventionally developed traditional artificial neural networks with seven to ten layers but with access to 1,000's and millions of simulated neurons without resorting to deep learning techniques (ANN_RF).

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Section: Ann Classifiermentioning

confidence: 99%

Classification of land use/land cover using artificial intelligence (ANN-RF)

Alshari

Abdulkareem

Gawali

2023

Front. Artif. Intell.

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“…It also achieves a higher R-squared and a lower RMSE than ANN. This is because RFR is less prone to overfitting compared with ANN [39].…”

Section: Random Forest Regressionmentioning

confidence: 99%

Utilisation of Machine Learning Techniques to Model Creep Behaviour of Low-Carbon Concretes

Bouras,

2023

Buildings

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Low-carbon concrete mixes that incorporate high volumes of fly ash and slag as cement replacements are becoming increasingly more common as part of efforts to decarbonise the construction industry. Though environmental benefits are offered, concretes containing supplementary cementitious materials exhibit different creep behaviour when compared to conventional concrete. Creep can significantly impact long-term structural behaviour and influence the overall serviceability and durability of concrete structures. This paper develops a creep compliance prediction model using supervised machine learning techniques for concretes containing fly ash and slag as cement substitutes. Gaussian process regression (GPR), artificial neural networks (ANN), random forest regression (RFR) and decision tree regression (DTR) models were all considered. The dataset for model training was developed by mining relevant data from the Infrastructure Technology Institute of Northwestern University’s comprehensive creep dataset in addition to extracting data from the literature. Holdout validation was adopted with the data partitioned into training (70%) and validation (30%) sets. Based on statistical indicators, all machine learning models can accurately model creep compliance with the RFR and GPR found to be the best-performing models. The sensitivity of the GPR model’s performance to training repetitions, input variable selection and validation methodology was assessed, with the results indicating small variability. The importance of the selected input variables was analysed using the Shapley additive explanation. It was found that time was the most significant parameter, with loading age, compressive strength, elastic modulus, volume-to-surface ratio and relative humidity also showing high importance. Fly ash and silica fume content featured the least influence on creep prediction. Furthermore, the predictions of the trained models were compared to experimental data, which showed that the GPR, RFR and ANN models can accurately reflect creep behaviour and that the DTR model does not give accurate predictions.

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“…Rizvon et al [59] evaluated the applicability of three machine learning techniques for predicting the RAC's compressive strength: random forest, ANN, and least absolute shrinkage and selection operator (LASSO). The networks were trained using a single hidden layer, seven neurons in the input layer, and a single output.…”

Section: Introductionmentioning

confidence: 99%

Influence of the ANN Hyperparameters on the Forecast Accuracy of RAC’s Compressive Strength

Almeida,

Felix,

de Sousa

et al. 2023

Materials

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The artificial neural networks (ANNs)-based model has been used to predict the compressive strength of concrete, assisting in creating recycled aggregate concrete mixtures and reducing the environmental impact of the construction industry. Thus, the present study examines the effects of the training algorithm, topology, and activation function on the predictive accuracy of ANN when determining the compressive strength of recycled aggregate concrete. An experimental database of compressive strength with 721 samples was defined considering the literature. The database was used to train, validate, and test the ANN-based models. Altogether, 240 ANNs were trained, defined by combining three training algorithms, two activation functions, and topologies with a hidden layer containing 1–40 neurons. The ANN with a single hidden layer including 28 neurons, trained with the Levenberg–Marquardt algorithm and the hyperbolic tangent function, achieved the best level of accuracy, with a coefficient of determination equal to 0.909 and a mean absolute percentage error equal to 6.81%. Furthermore, the results show that it is crucial to avoid the use of overly complex models. Excessive neurons can lead to exceptional performance during training but poor predictive ability during testing.

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Strength prediction models for recycled aggregate concrete using Random Forests, ANN and LASSO

Cited by 16 publications

References 28 publications

Classification of land use/land cover using artificial intelligence (ANN-RF)

Classification of land use/land cover using artificial intelligence (ANN-RF)

Utilisation of Machine Learning Techniques to Model Creep Behaviour of Low-Carbon Concretes

Influence of the ANN Hyperparameters on the Forecast Accuracy of RAC’s Compressive Strength

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