Particle Swarm Optimization Algorithm-Extreme Learning Machine (PSO-ELM) Model for Predicting Resilient Modulus of Stabilized Aggregate Bases

Kaloop, Mosbeh R.; Kumar, Deepak; Samui, Pijush; Gabr, Alaa R.; Hu, Jong Wan; Jin, Xinghan; Roy, Bappadittya

doi:10.3390/app9163221

Cited by 72 publications

(26 citation statements)

References 45 publications

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“…The ANFIS model was introduced as a combination of the neural network model and fuzzy logic [33][34][35][36][37][38]. The ANFIS model showed a better performance in processing a small size of training datasets when compared to ANN [39].…”

Section: Anfis Modelmentioning

confidence: 99%

Prediction of Arsenic Removal from Contaminated Water Using Artificial Neural Network Model

2022

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Arsenic is a deleterious heavy metal that is usually removed from polluted water based on adsorption processes. The latest mode of modeling such a process is to implement artificial intelligence (AI). In the current work, a new artificial neural network (ANN) model was developed to predict the adsorption efficiency of arsenate (As(III)) from contaminated water by analyzing different architectures of an adaptive network-based fuzzy inference system (ANFIS). The database for the current study consisted of the experimental data of the adsorption of As(III) by different adsorbents/biosorbents. The data were randomly divided into two sets: 70% for the training phase and 30% for the testing phase. Four statistical evaluation metrics, namely, mean square error (MSE), root-mean-square error (RMSE), Pearson’s correlation coefficient (R%), and the determination coefficient (R2) were used for the analysis. The best performing ANFIS model was characterized with the average values of 97.72%, 0.9333, 0.137, and 0.274 of R%, R2, MSE, and RMSE, respectively. In addition, a parametric investigation revealed that the most dominating parameters on the adsorption process efficiency were in the following order: pH, As initial concentration, contact time, adsorbent dosage, inoculum size, and temperature. The results of the current study would be useful in the adsorption process scale-up and optimization.

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Section: Anfis Modelmentioning

confidence: 99%

Prediction of Arsenic Removal from Contaminated Water Using Artificial Neural Network Model

2022

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“…In this section, an RF model and a HHO-RF model were developed to establish a stable and accurate relationship between the input variables (f, B, T, D V , D H , Q total , Q max ) and output variable (PPV). To evaluate the predictive performance, three performance indices including RMSE, R 2 and MAE, were introduced and utilized here [64][65][66][67][68].…”

Section: Performance Metricsmentioning

confidence: 99%

Effective Assessment of Blast-Induced Ground Vibration Using an Optimized Random Forest Model Based on a Harris Hawks Optimization Algorithm

Shi

Zhou

et al. 2020

Applied Sciences

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Most mines choose the drilling and blasting method which has the characteristics of being a cheap and efficient method to fragment rock mass, but blast-induced ground vibration damages the surrounding rock mass and structure and is a drawback. To predict, analyze and control the blast-induced ground vibration, the random forest (RF) model, Harris hawks optimization (HHO) algorithm and Monte Carlo simulation approach were utilized. A database consisting of 137 datasets was collected at different locations around the Tonglvshan open-cast mine, China. Seven variables were selected and collected as the input variables, and peak particle velocity was chosen as the output variable. At first, an RF model and a hybrid model, namely a HHO-RF model, were developed, and the prediction results checked by 3 performance indices to show that the proposed HHO-RF model can provide higher prediction performance. Then blast-induced ground vibration was simulated by using the Monte Carlo simulation approach and the developed HHO-RF model. After analyzing, the mean peak particle velocity value was 0.98 cm/s, and the peak particle velocity value did not exceed 1.95 cm/s with a probability of 90%. The research results of this study provided a simple, accurate method and basis for predicting, evaluating blast-induced ground vibration and optimizing the blast design before blast operation.

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“…Early introduced in the 1990s by Jang [37], ANFIS is well-known as a hybrid AI model in merging ANN [38][39][40][41] and Fuzzy Logic (FL) [42]. The ANFIS architecture consists of five principal layers such as fuzzification, rule, normalization, defuzzification and aggregation [37,[43][44][45][46].…”

Section: Adaptive Neuro Fuzzy Inference System (Anfis)mentioning

confidence: 99%

Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete

Pham

Dao

et al. 2019

Applied Sciences

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Use of manufactured sand to replace natural sand is increasing in the last several decades. This study is devoted to the assessment of using Principal Component Analysis (PCA) together with Teaching-Learning-Based Optimization (TLBO) for enhancing the prediction accuracy of individual Adaptive Neuro Fuzzy Inference System (ANFIS) in predicting the compressive strength of manufactured sand concrete (MSC). The PCA technique was applied for reducing the noise in the input space, whereas, TLBO was employed to increase the prediction performance of single ANFIS model in searching the optimal weights of input parameters. A number of 289 configurations of MSC were used for the simulation, especially including the sand characteristics and the MSC long-term compressive strength. Using various validation criteria such as Correlation Coefficient (R), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), the proposed method was validated and compared with several models, including individual ANFIS, Artificial Neural Networks (ANN) and existing empirical equations. The results showed that the proposed model exhibited great prediction capability compared with other models. Thus, it appeared as a robust alternative computing tool or an efficient soft computing technique for quick and accurate prediction of the MSC compressive strength.

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Particle Swarm Optimization Algorithm-Extreme Learning Machine (PSO-ELM) Model for Predicting Resilient Modulus of Stabilized Aggregate Bases

Cited by 72 publications

References 45 publications

Prediction of Arsenic Removal from Contaminated Water Using Artificial Neural Network Model

Prediction of Arsenic Removal from Contaminated Water Using Artificial Neural Network Model

Effective Assessment of Blast-Induced Ground Vibration Using an Optimized Random Forest Model Based on a Harris Hawks Optimization Algorithm

Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete

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