Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches

Dao, Dong Van; Trinh, Son Hoang; Ly, Haï‐Bang; Pham, Binh Thai

doi:10.3390/app9061113

Cited by 116 publications

(69 citation statements)

References 56 publications

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“…Precisely, the R values allow the statistical relationship between experimental results to be identified, and ML to predict the USC [147,148] by yielding a value between 0 and 1, where 0 is no correlation and 1 is a total correlation. In the cases of RMSE and MAE, which have the same units as the quantity being estimated [24,42], lower values of RMSE and MAE indicate a basically good accuracy of the prediction output using the ML models [149][150][151][152][153][154]. The values of R, RMSE, and MAE are estimated using the following equations [107,108,115,147]:…”

Section: Machine Learning Evaluation Criteriamentioning

confidence: 99%

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

et al. 2020

Sustainability

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Understanding shear behavior is crucial for the design of reinforced concrete beams and sustainability in construction and civil engineering. Although numerous studies have been proposed, predicting such behavior still needs further improvement. This study proposes a soft-computing tool to predict the ultimate shear capacities (USCs) of concrete beams reinforced with steel fiber, one of the most important factors in structural design. Two hybrid machine learning (ML) algorithms were created that combine neural networks (NNs) with two distinct optimization techniques (i.e., the Real-Coded Genetic Algorithm (RCGA) and the Firefly Algorithm (FFA)): the NN-RCGA and the NN-FFA. A database of 463 experimental data was gathered from reliable literature for the development of the models. After the construction, validation, and selection of the best model based on common statistical criteria, a comparison with the empirical equations available in the literature was carried out. Further, a sensitivity analysis was conducted to evaluate the importance of 16 inputs and reveal the dependency of structural parameters on the USC. The results showed that the NN-RCGA (R = 0.9771) was better than the NN-FFA and other analytical models (R = 0.5274–0.9075). The sensitivity analysis results showed that web width, effective depth, and a clear depth ratio were the most important parameters in modeling the shear capacity of steel fiber-reinforced concrete beams.

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Section: Machine Learning Evaluation Criteriamentioning

confidence: 99%

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

et al. 2020

Sustainability

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“…To validate the predictive capability of the models, Mean Absolute Error (MAE), the Pearson correlation coefficient (R), and Root Mean Squared Error (RMSE) were selected and used, as these validation criteria are popular in evaluating the ML models. Basically, R indicates the statistical relationship between the actual values of experiments and the predicted values of the models [41]. Its absolute values range from 0 to 1 where 0 shows an inaccurate correct model and 1 indicates an accurate model.…”

Section: Performance Evaluationmentioning

confidence: 99%

Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination

Pham

Nguyen-Thoi

et al. 2020

Sustainability

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Machine Learning (ML) has been applied widely in solving a lot of real-world problems. However, this approach is very sensitive to the selection of input variables for modeling and simulation. In this study, the main objective is to analyze the sensitivity of an advanced ML method, namely the Extreme Learning Machine (ELM) algorithm under different feature selection scenarios for prediction of shear strength of soil. Feature backward elimination supported by Monte Carlo simulations was applied to evaluate the importance of factors used for the modeling. A database constructed from 538 samples collected from Long Phu 1 power plant project was used for analysis. Well-known statistical indicators, such as the correlation coefficient (R), root mean squared error (RMSE), and mean absolute error (MAE), were utilized to evaluate the performance of the ELM algorithm. In each elimination step, the majority vote based on six elimination indicators was selected to decide the variable to be excluded. A number of 30,000 simulations were conducted to find out the most relevant variables in predicting the shear strength of soil using ELM. The results show that the performance of ELM is good but very different under different combinations of input factors. The moisture content, liquid limit, and plastic limit were found as the most critical variables for the prediction of shear strength of soil using the ML model. Sustainability 2020, 12, 2339 2 of 29 the laboratory, including direct shear test, triaxial compression tests, or unconfined compression tests which might increase the cost and prolong the time of completing the projects. Moreover, the test accuracy depends significantly on the instruments, the meticulous procedures, and the expertise of the experimenters [1]. Therefore, the development of new advanced techniques for quick and accurate prediction of shear strength of soil is essential and practical.Traditionally, the shear strength of soil is often predicted by using traditional formula-based methods. Garven and Vanapalli [2] summarized and evaluated nineteen empirical techniques that are available for the prediction of the shear strength of unsaturated soils. Out of these, six techniques used tool of the soil-water retention curve (SWRC) and the remainder thirteen procedures are based on mathematical formulations. In these empirical techniques, various parameters of soil were used to correlate with the shear strength in unsaturated soils such as the texture of soil surface, pore size distribution, residual suction. In another study, Sheng et al. [3] proposed different empirical equations for the prediction of shear strength of unsaturated soils using different approaches, which are based on the independent stress, Bishop's stress, and constitutive models. Vanapalli and Fredlund [4] compared different empirical approaches for the prediction of shear strength of unsaturated soils. Various parameters used for forming the correlation equations such as particle gain distribution, liquid limit, plasticity indices, water con...

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“…AFNIS is a multilayered network based on neural network learning algorithms and fuzzy reasoning [38][39][40][41]. Thanks to its ability to combine the a fuzzy system with numerical power of adaptive neural networks, ANFIS has demonstrated its ability to model many problems in the field of science such as prediction of the geopolymer concrete compressive strength [42], the Marshal parameters of stone matrix asphalt mixtures [43], the buckling damage of structural members [9], the dynamic loadings of power systems [44,45], the buckling damage of steel columns under axial compression [8], the International Roughness Index of pavements [46], the blast-induced air-overpressure in quarry blasting sites [47], the traffic air pollution [48], and the flocculation-dewatering performance of fine mineral tailings [49]. ANFIS has a good ability to learn, build, expand and classify as the advantage of ANFIS is that it allows the fuzzy rules to be extracted from numerical data and is appropriate for formulating a rule base.…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Development of Hybrid Machine Learning Models for Predicting the Critical Buckling Load of I-Shaped Cellular Beams

et al. 2019

Applied Sciences

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The principal purpose of this work is to develop three hybrid machine learning (ML) algorithms, namely ANFIS-RCSA, ANFIS-CA, and ANFIS-SFLA which are a combination of adaptive neuro-fuzzy inference system (ANFIS) with metaheuristic optimization techniques such as real-coded simulated annealing (RCSA), cultural algorithm (CA) and shuffled frog leaping algorithm (SFLA), respectively, to predict the critical buckling load of I-shaped cellular steel beams with circular openings. For this purpose, the existing database of buckling tests on I-shaped steel beams were extracted from the available literature and used to generate the datasets for modeling. Eight inputs, considered as independent variables, including the beam length, beam end-opening distance, opening diameter, inter-opening distance, section height, web thickness, flange width, and flange thickness, as well as one output of the critical buckling load of cellular steel beams considered as a dependent variable, were used in the datasets. Three quality assessment criteria, namely correlation coefficient (R), root mean squared error (RMSE) and mean absolute error (MAE) were employed for assessment of three developed hybrid ML models. The obtained results indicate that all three hybrid ML models have a strong ability to predict the buckling load of steel beams with circular openings, but ANFIS-SFLA (R = 0.960, RMSE = 0.040 and MAE = 0.017) exhibits the best effectiveness as compared with other hybrid models. In addition, sensitivity analysis was investigated and compared with linear statistical correlation between inputs and output to validate the importance of input variables in the models. The sensitivity results show that the most influenced variable affecting beam buckling capacity is the beam length, following by the flange width, the flange thickness, and the web thickness, respectively. This study shows that the hybrid ML techniques could help in establishing a robust numerical tool for beam buckling analysis. The proposed methodology is also promising to predict other types of failure, as well as other types of perforated beams.

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Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches

Cited by 116 publications

References 56 publications

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination

Development of Hybrid Machine Learning Models for Predicting the Critical Buckling Load of I-Shaped Cellular Beams

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