ORIGINAL ARTICLE
Smart bacteria-foraging algorithm-based customized kernel support vector regression and enhanced probabilistic neural network for compaction quality assessment and control of earth-rock dam
Jiajun Wang,
Denghua Zhong,
Hojjat Adeli,
Dong Wang,
Minghui Liu,
Jiajun Wang
State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, China
Search for more papers by this authorCorresponding Author
Denghua Zhong
State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, China
Correspondence
Denghua Zhong, State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, 92Weijin Road, Nankai District, Tianjin 300072, China.
Email: [email protected]
Search for more papers by this authorHojjat Adeli
Department of Biomedical Engineering; College of Engineering Department of Biomedical Informatics; Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, Ohio, USA
Search for more papers by this authorDong Wang
State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, China
Search for more papers by this authorMinghui Liu
State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, China
Search for more papers by this authorJiajun Wang,
Denghua Zhong,
Hojjat Adeli,
Dong Wang,
Minghui Liu,
Jiajun Wang
State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, China
Search for more papers by this authorCorresponding Author
Denghua Zhong
State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, China
Correspondence
Denghua Zhong, State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, 92Weijin Road, Nankai District, Tianjin 300072, China.
Email: [email protected]
Search for more papers by this authorHojjat Adeli
Department of Biomedical Engineering; College of Engineering Department of Biomedical Informatics; Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, Ohio, USA
Search for more papers by this authorDong Wang
State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, China
Search for more papers by this authorMinghui Liu
State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, China
Search for more papers by this authorShort Informative: A novel smart bacteria-foraging algorithm (SBFA) is proposed for optimization in this paper, and a customized kernel support vector regression (CKSVR) is proposed for approximate function in L2(R). SBFA-based CKSVR (SBFA-CKSVR) is implemented using C# and verified by datasets from UCI; and a three-dimensional real-time compaction quality assessment and control system has been firstly developed based on SBFA-CKSVR and EPNN.
Abstract
Compaction quality assessment and control for an earth-rock dam is the key measure to ensure dam safety. However, to date, the compaction quality assessment model has not been accurate enough, and no effective feedback control measures have been developed. Hybrid data mining algorithms have great potential for solving this problem. In this study, smart bacteria-foraging algorithm-based customized kernel support vector regression (SBFA-CKSVR) is proposed for compaction quality assessment, whereas an enhanced probabilistic neural network (EPNN) is adopted for compaction quality control. SBFA integrates a bacteria-foraging algorithm, chaos mapping, and adaptive and quantum computing to solve the high-dimensional complex problem effectively. CKSVR is proposed to approximate a function in quadratic continuous integral space L2(R) where its hyperparameters are optimized by SBFA. Finally, SBFA-CKSVR is used to establish a high-precision compaction quality assessment model whereas the EPNN is adopted to realize the compaction quality feedback control. A three-dimensional real-time monitoring system for the earth-rock dam is also developed based on SBFA-CKSVR and EPNN. A large-scale hydraulic engineering application proves the effectiveness and superiority of this research compared with the previous work.
CONFLICTS OF INTEREST
None.
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