An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine

Tran, Hong-Hai; Hoang, Nhat‐Duc

doi:10.1155/2014/109184

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…This prediction model integrates the merits of the empirical wavelet transform (EWT), the With the rapid development of science and technology, intelligent algorithms have been introduced into the prediction research of grouting projects. Tran and Hoang [4] developed a groutability prediction model by utilizing support vector regression (SVR) and the differential evolution (DE) algorithm. Hoang et al [5] combined SVR with differential flower pollination (DFP) for the prediction of groutability.…”

Section: Introductionmentioning

confidence: 99%

Grouting Power Prediction Using a Hybrid Model Based on Support Vector Regression Optimized by an Improved Jaya Algorithm

et al. 2020

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Grouting power is a vital parameter that can be used as an indicator for simultaneously controlling grouting pressure and injection rate. Accurate grouting power prediction contributes to the real-time optimization of the grouting process, guaranteeing grouting safety and quality. However, the strong nonlinearity of the grouting power time series makes the forecasting task challenging. Hence, this paper proposes a novel hybrid model for accurate grouting power forecasting. First, empirical wavelet transform (EWT) is employed to decompose the original grouting series into several subseries and one residual adaptively. Second, partial autocorrelation function (PACF) is applied to identify the optimal input variables objectively. Then, support vector regression (SVR) is adopted to obtain prediction outcomes of each subseries, while an improved Jaya (IJaya) algorithm by coupling chaos theory and Lévy flights to improve the algorithm’s accuracy performance is proposed to optimize the SVR hyperparameters. Finally, the prediction results of decomposed subseries are superimposed to produce the final results. A consolidation grouting project is taken as a case study and the computation results with the RMSE = 0.2672 MPa·L/min, MAE = 0.2165 MPa·L/min, MAPE = 3.85% and EC = 0.9815 demonstrate that the proposed model exhibits superior forecasting ability and can provide a viable reference for grouting construction.

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Section: Introductionmentioning

confidence: 99%

Grouting Power Prediction Using a Hybrid Model Based on Support Vector Regression Optimized by an Improved Jaya Algorithm

et al. 2020

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“…Although various studies have indicated the superior performance of the SVC in the geotechnical field (Cheng and Hoang 2014a, b;Goh and Goh 2007;Kavzoglu et al 2014;Pal 2006;Samui 2011Samui , 2008Tran and Hoang 2014;Yao et al 2008), none of previous research works has evaluated the potentiality of SVC for the problem of typhoon-induced slope collapsed prediction. Therefore, our current study is an attempt to fill this gap.…”

Section: Introductionmentioning

confidence: 99%

Typhoon-induced slope collapse assessment using a novel bee colony optimized support vector classifier

Cheng

Hoang

2015

Nat Hazards

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This research proposes a novel bee colony optimized support vector classifier (BeeSVC) for predicting typhoon-induced slope collapses. The BeeSVC employs the support vector classifier (SVC) as a machine learning method to classify a slope into two classes: ''stable slope'' and ''collapsed slope.'' Furthermore, the artificial bee colony algorithm is used as a metaheuristic to determine the hyper-parameters of the SVC appropriately. The contribution of the proposed method to the body of knowledge is multifold. First, the combined framework of the BeeSVC allows the assessment process to be operated automatically. Second, since the number of the ''collapsed'' class occupied more than 70 % of the historical cases, a repeated random sub-sampling procedure with the Student's t test is put forward to alleviate the class-imbalanced problem and reliably evaluate the model performance. Third, the mutual information between the input features and the slope performance is employed to reflect the contribution of each feature to the slope failure. Lastly, the superior performance has proved that the BeeSVC can be a very effective tool for decision-makers to forecast typhoon-induced slope collapses.

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“…It uses mutation, crossover, and selection operators at each generation to move its population toward the global optimum [14]. Superior performance of the DE over other algorithms has been verified in optimization problems that span many fields [15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

NIDE: A Novel Improved Differential Evolution for Construction Project Crashing Optimization

Hoang

2014

Journal of Construction Engineering

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In the field of construction management, project crashing is an approach to shortening the project duration by reducing the duration of several critical project activities to less than their normal activity duration. The goal of crashing is to shorten the project duration while minimizing the crashing cost. In this research, a novel method for construction project crashing is proposed. The method is named as novel improved differential evolution (NIDE). The proposed NIDE is developed by an integration of the differential evolution (DE) and a new probabilistic similarity-based selection operator (PSSO) that aims at improving the DE’s selection process. The PSSO has the role as a scheme for preserving the population diversity and fending off the premature convergence. The experimental result has demonstrated that the newly established NIDE can successfully escape from local optima and achieve a significantly better optimization performance.

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An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine

Cited by 7 publications

References 22 publications

Grouting Power Prediction Using a Hybrid Model Based on Support Vector Regression Optimized by an Improved Jaya Algorithm

Grouting Power Prediction Using a Hybrid Model Based on Support Vector Regression Optimized by an Improved Jaya Algorithm

Typhoon-induced slope collapse assessment using a novel bee colony optimized support vector classifier

NIDE: A Novel Improved Differential Evolution for Construction Project Crashing Optimization

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