Compressive strength of Foamed Cellular Lightweight Concrete simulation: New development of hybrid artificial intelligence model

Ashrafian, Ali; Shokri, Faranak; Amiri, Mohammad Javad Taheri; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher; Rezaie-Balf, Mohammad

doi:10.1016/j.conbuildmat.2019.117048

Cited by 121 publications

(38 citation statements)

References 56 publications

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“…Hence, reliable alternative model is always the inspiration of geotechnical scientists to explore and investigate [20]. Machine learning models exhibited a new era of modeling methodologies for various engineering applications [21][22][23][24][25][26][27]. Within the settlement determination, artificial neural network (ANN) models have been introduced to this field since about three decades ago [28][29][30][31][32].…”

Section: Literature Review and Research Motivationmentioning

confidence: 99%

Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro‐Fuzzy Inference System Model

Mohammed

Sharafati

Al‐Ansari

et al. 2020

Advances in Civil Engineering

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Settlement simulating in cohesion materials is a crucial issue due to complexity of cohesion soil texture. This research emphasis on the implementation of newly developed machine learning models called hybridized Adaptive Neuro-Fuzzy Inference System (ANFIS) with Particle Swarm Optimization (PSO) algorithm, Ant Colony optimizer (ACO), Differential Evolution (DE), and Genetic Algorithm (GA) as efficient approaches to predict settlement of shallow foundation over cohesion soil properties. The width of footing (B), pressure of footing (qa), geometry of footing (L/B), count of SPT blow (N), and ratio of footing embedment (Df/B) are considered as predictive variables. Nonhomogeneity and inconsistency of employed dataset is a major concern during prediction modeling. Hence, two different modeling scenarios (i) preprocessed dataset (PP) and (ii) nonprocessed (initial) dataset (NP) were inspected. To assess the accuracy of the applied hybrid models and standalone one, multiple statistical metrics were computed and analyzed over the training and testing phases. Results indicated ANFIS-PSO model exhibited an accurate and reliable prediction data intelligent and had the highest predictability performance against all employed models. In addition, results demonstrated that data preprocessing is highly essential to be performed prior to building the predictive models. Overall, ANFIS-PSO model showed a robust machine learning for settlement prediction.

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Section: Literature Review and Research Motivationmentioning

confidence: 99%

Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro‐Fuzzy Inference System Model

Mohammed

Sharafati

Al‐Ansari

et al. 2020

Advances in Civil Engineering

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“…Nevertheless, the range of input conditions is the main reason limiting the predicted CS of FC using these equations [25]. In addition, such semi-analytical equations require several constants that are not easy to obtain and highly depend on the complex relationships between the mixture constituents and the CS of FC [18][19][20]25,26]. Therefore, the development of an advanced numerical tool for prediction of the CS of FC is essential.…”

Section: Introductionmentioning

confidence: 99%

“…SVM using a radial basis function was proven more accurate than other functions and traditional regression functions [47]. Besides, various ML algorithms (MLR, ANN, SVR, MARS, and MARS-WCA) have been used to predict the CS of FC at different testing ages [26]. It is thus confirmed that ML algorithms are powerful numerical tools that can account for complex relationships between mixture components and help in optimizing the mixture to achieve the targeted mechanical properties, such as the CS of FC.…”

Section: Introductionmentioning

confidence: 99%

Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete

Dao

et al. 2020

Materials

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Development of Foamed Concrete (FC) and incessant increases in fabrication technology have paved the way for many promising civil engineering applications. Nevertheless, the design of FC requires a large number of experiments to determine the appropriate Compressive Strength (CS). Employment of machine learning algorithms to take advantage of the existing experiments database has been attempted, but model performance can still be improved. In this study, the performance of an Artificial Neural Network (ANN) was fully analyzed to predict the 28 days CS of FC. Monte Carlo simulations (MCS) were used to statistically analyze the convergence of the modeled results under the effect of random sampling strategies and the network structures selected. Various statistical measures such as Coefficient of Determination (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were used for validation of model performance. The results show that ANN is a highly efficient predictor of the CS of FC, achieving a maximum R2 value of 0.976 on the training part and an R2 of 0.972 on the testing part, using the optimized C-ANN-[3–4–5–1] structure, which compares with previous published studies. In addition, a sensitivity analysis using Partial Dependence Plots (PDP) over 1000 MCS was also performed to interpret the relationship between the input parameters and 28 days CS of FC. Dry density was found as the variable with the highest impact to predict the CS of FC. The results presented could facilitate and enhance the use of C-ANN in other civil engineering-related problems.

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“…In a study, Ashrafian et al [10] investigated their newly developed model to estimate the compressive strength of lightweight concrete and compared its results with those of classical data-driven modeling techniques including ANN, SVMs, and MLR. In this study, the appropriate combination of inputs was used in the dataset using Mallow's Cp evaluation and a suitable structure was chosen for the inputs.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Artificial Intelligence Model Development for Roller-compacted Concrete Compressive Strength Estimation

Ranjbar

Barahmand

Ghanbari

2020

IJE

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This study implemented the artificial bee colony (ABC) metaheuristic algorithm to optimize the Artificial Neural Network (ANN) values for improving the accuracy of model and evaluate the developed model. Compressive strength of RCC was investigated using mix design materials in three forms, namely volumetric weight input (cement, water, coarse aggregate, fine aggregate, and binder), value ratio (water to cement ratio, water to binder ratio, and coarse aggregate to fine aggregate ratio), as well as the percentage of mix design values of different ages. A comprehensive, proper-range dataset containing 333 mix designs was collected from various papers. The accuracy of the research models was investigated using error indices, namely correlation coefficient, root-mean-square-error (RMSE), mean absolute error (MAE), and developed hybrid models were compared. External validation and Monte Carlo simulation (MCS)-based uncertainty analysis was also used to validate the models and their results were reported. The experimental stage of the prediction of compressive strength values showed significant accuracy of the ANN-ABC model with (MAE=11.49, RMSE=0.920, RME=5.21) compared to other models in this study. Besides, the sensitivity analysis of predictor variables in this study revealed that the variables "specimen age," "binder," and "fine aggregate" were more effective and important in this research. Comparison of the results showed that the improved proposed model using the ABC algorithm was more capable and more accurate in reducing the error rate in providing computational relations compared to the default models examined in the prediction of the compressive strength of RCC and also tried in simplifying computational relations.

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Compressive strength of Foamed Cellular Lightweight Concrete simulation: New development of hybrid artificial intelligence model

Cited by 121 publications

References 56 publications

Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro‐Fuzzy Inference System Model

Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro‐Fuzzy Inference System Model

Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete

Hybrid Artificial Intelligence Model Development for Roller-compacted Concrete Compressive Strength Estimation

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