Fei Lv
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Fei Lv has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Denghua Zhong
State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, China
1 shared publication - Jia Yu
State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300350, China
1 shared publication - Jiajun Wang
State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation, Tianjin University, Tianjin, China
1 shared publication - Jun Zhang
State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation, Tianjin University, Tianjin 300350, China
1 shared publication - Mengqi Zhao
State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300350, China
1 shared publication - Shaohui Deng
Ph.D. Candidate, State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin Univ., Tianjin 300072, China
1 shared publication
Latest Publications
- 1 March 2019
- Published by American Society of Civil Engineers (ASCE) in Journal of Computing in Civil Engineering
- Vol. 33 (2)
Hybrid Grey Wolf Optimization Algorithm–Based Support Vector Machine for Groutability Prediction of Fractured Rock Mass
Groutability determination is a very important task in grouting quality control. There is little research on the groutability of cement-based grout in a fractured rock mass, and the prediction is hampered by a small number of samples along with multidimensional and nonlinear problems. This study proposes an intelligent predictive model that integrates hybrid grey wolf optimization (HGWO) and a support vector machine (SVM) to predict the groutability. The model was built in three steps: HGWO was embedded in a SVM to search for the best hyperparameters ( , ); crossvalidation and error analysis were introduced into the HGWO-SVM model to ensure the generalization performance and prediction accuracy; and the classification and regression prediction of groutability with cement-based grout in a fractured rock mass were predicted by the established HGWO-SVM intelligent prediction method. Taking a curtain grouting project as a case, the applicability of the method was verified. The performance of the proposed prediction model is improved compared with other methods, and the prediction accuracy meets engineering needs. The results show that this method can accurately and conveniently predict the groutability of cement-based grout in a fractured rock mass and provide practical assistance to field projects.- 1 March 2019
- Published by American Society of Civil Engineers (ASCE) in Journal of Computing in Civil Engineering
- Vol. 33 (2)
Hybrid Grey Wolf Optimization Algorithm–Based Support Vector Machine for Groutability Prediction of Fractured Rock Mass
Groutability determination is a very important task in grouting quality control. There is little research on the groutability of cement-based grout in a fractured rock mass, and the prediction is hampered by a small number of samples along with multidimensional and nonlinear problems. This study proposes an intelligent predictive model that integrates hybrid grey wolf optimization (HGWO) and a support vector machine (SVM) to predict the groutability. The model was built in three steps: HGWO was embedded in a SVM to search for the best hyperparameters ( , ); crossvalidation and error analysis were introduced into the HGWO-SVM model to ensure the generalization performance and prediction accuracy; and the classification and regression prediction of groutability with cement-based grout in a fractured rock mass were predicted by the established HGWO-SVM intelligent prediction method. Taking a curtain grouting project as a case, the applicability of the method was verified. The performance of the proposed prediction model is improved compared with other methods, and the prediction accuracy meets engineering needs. The results show that this method can accurately and conveniently predict the groutability of cement-based grout in a fractured rock mass and provide practical assistance to field projects.- Open Access
An Optimization Model for Construction Stage and Zone Plans of Rockfill Dams Based on the Enhanced Whale Optimization Algorithm
Rockfill dams are among the most complex, significant, and costly infrastructure projects of great national importance. A key issue in their design is the construction stage and zone optimization. However, a detailed flow shop construction scheme that considers the opinions of decision makers cannot be obtained using the current rock-fill dam construction stage and zone optimization methods, and the robustness and efficiency of existing construction stage and zone optimization approaches are not sufficient. This research presents a construction stage and zone optimization model based on a data-driven analytical hierarchy process extended by D numbers (D-AHP) and an enhanced whale optimization algorithm (EWOA). The flow shop construction scheme is optimized by presenting an automatic flow shop construction scheme multi-criteria decision making (MCDM) method, which integrates the data-driven D-AHP with an improved construction simulation of a high rockfill dam (CSHRD). The EWOA, which uses Levy flight to improve the robustness and efficiency of the whale optimization algorithm (WOA), is adopted for optimization. This proposed model is implemented to optimize the construction stages and zones while obtaining a preferable flow shop construction scheme. The effectiveness and advantages of the model are proven by an example of a large-scale rockfill dam.
Open AccessAn Optimization Model for Construction Stage and Zone Plans of Rockfill Dams Based on the Enhanced Whale Optimization Algorithm
Rockfill dams are among the most complex, significant, and costly infrastructure projects of great national importance. A key issue in their design is the construction stage and zone optimization. However, a detailed flow shop construction scheme that considers the opinions of decision makers cannot be obtained using the current rock-fill dam construction stage and zone optimization methods, and the robustness and efficiency of existing construction stage and zone optimization approaches are not sufficient. This research presents a construction stage and zone optimization model based on a data-driven analytical hierarchy process extended by D numbers (D-AHP) and an enhanced whale optimization algorithm (EWOA). The flow shop construction scheme is optimized by presenting an automatic flow shop construction scheme multi-criteria decision making (MCDM) method, which integrates the data-driven D-AHP with an improved construction simulation of a high rockfill dam (CSHRD). The EWOA, which uses Levy flight to improve the robustness and efficiency of the whale optimization algorithm (WOA), is adopted for optimization. This proposed model is implemented to optimize the construction stages and zones while obtaining a preferable flow shop construction scheme. The effectiveness and advantages of the model are proven by an example of a large-scale rockfill dam.