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

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.

[1]  Qiang Wang,et al.  Optimizing the energy-spectrum efficiency of cellular systems by evolutionary multi-objective algorithm , 2019, Integr. Comput. Aided Eng..

[2]  Jian Zhang,et al.  A Quantum‐Inspired Genetic Algorithm‐Based Optimization Method for Mobile Impact Test Data Integration , 2018, Comput. Aided Civ. Infrastructure Eng..

[3]  Francisco Herrera,et al.  Mining association rules on Big Data through MapReduce genetic programming , 2017, Integr. Comput. Aided Eng..

[4]  Milan Curkovic,et al.  A novel projection of open geometry into rectangular domain for 3D shape parameterization , 2017, Integr. Comput. Aided Eng..

[5]  Václav Skala,et al.  Large scattered data interpolation with radial basis functions and space subdivision , 2017, Integr. Comput. Aided Eng..

[6]  Hyo Seon Park,et al.  Modal Identification for High‐Rise Building Structures Using Orthogonality of Filtered Response Vectors , 2017, Comput. Aided Civ. Infrastructure Eng..

[7]  Ka-Veng Yuen,et al.  Entropy‐Based Optimal Sensor Placement for Model Identification of Periodic Structures Endowed with Bolted Joints , 2017, Comput. Aided Civ. Infrastructure Eng..

[8]  Wei Li,et al.  Mountain Railway Alignment Optimization with Bidirectional Distance Transform and Genetic Algorithm , 2017, Comput. Aided Civ. Infrastructure Eng..

[9]  Hojjat Adeli,et al.  Nature-Inspired Chemical Reaction Optimisation Algorithms , 2017, Cognitive Computation.

[10]  Nazmul Siddique,et al.  Nature-Inspired Computing: Physics and Chemistry-Based Algorithms , 2017 .

[11]  María José del Jesús,et al.  A Pareto-based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets , 2017, Int. J. Neural Syst..

[12]  Hongzhe Dai,et al.  A Wavelet Support Vector Machine‐Based Neural Network Metamodel for Structural Reliability Assessment , 2017, Comput. Aided Civ. Infrastructure Eng..

[13]  Gabriele Comanducci,et al.  The Stretching Method for Vibration‐Based Structural Health Monitoring of Civil Structures , 2017, Comput. Aided Civ. Infrastructure Eng..

[14]  Juan Manuel Górriz,et al.  Independent Component Analysis-Support Vector Machine-Based Computer-Aided Diagnosis System for Alzheimer's with Visual Support , 2017, Int. J. Neural Syst..

[15]  Manuel Roveri,et al.  An Ensemble Approach for Cognitive Fault Detection and Isolation in Sensor Networks , 2017, Int. J. Neural Syst..

[16]  Francisco Sales,et al.  A Realistic Seizure Prediction Study Based on Multiclass SVM , 2017, Int. J. Neural Syst..

[17]  Faraz S. Tehrani,et al.  Assessing soil compaction using continuous compaction control and location-specific in situ tests , 2017 .

[18]  Alex Alexandridis,et al.  A particle swarm optimization approach in printed circuit board thermal design , 2017, Integr. Comput. Aided Eng..

[19]  Ivan Jordanov,et al.  Quantum inspired evolutionary algorithms with improved rotation gates for real-coded synthetic and real world optimization problems , 2017, Integr. Comput. Aided Eng..

[20]  Francisco Madeiro,et al.  Hybrid firefly-Linde-Buzo-Gray algorithm for Channel-Optimized Vector Quantization codebook design , 2017, Integr. Comput. Aided Eng..

[21]  Yasha Zeinali,et al.  Competitive probabilistic neural network , 2017, Integr. Comput. Aided Eng..

[22]  Paul Cahill,et al.  Effect of Road Surface, Vehicle, and Device Characteristics on Energy Harvesting from Bridge–Vehicle Interactions , 2016, Comput. Aided Civ. Infrastructure Eng..

[23]  Kris De Brabanter,et al.  Wavelet Filter Design for Pavement Roughness Analysis , 2016, Comput. Aided Civ. Infrastructure Eng..

[24]  Hojjat Adeli,et al.  Physics‐based search and optimization: Inspirations from nature , 2016, Expert Syst. J. Knowl. Eng..

[25]  Nazmul Siddiquea,et al.  Applications of gravitational search algorithm in engineering , 2016 .

[26]  Hojjat Adeli,et al.  Simulated Annealing, Its Variants and Engineering Applications , 2016, Int. J. Artif. Intell. Tools.

[27]  Hojjat Adeli,et al.  Gravitational Search Algorithm and Its Variants , 2016, Int. J. Pattern Recognit. Artif. Intell..

[28]  Andrey Dimitrov,et al.  Non‐Uniform B‐Spline Surface Fitting from Unordered 3D Point Clouds for As‐Built Modeling , 2016, Comput. Aided Civ. Infrastructure Eng..

[29]  Joseph Y.-T. Leung,et al.  Worker assignment and production planning with learning and forgetting in manufacturing cells by hybrid bacteria foraging algorithm , 2016, Comput. Ind. Eng..

[30]  Reginald R. Souleyrette,et al.  A Generic Approach to Estimate Freeway Traffic Time Using Vehicle ID‐Matching Technologies , 2016, Comput. Aided Civ. Infrastructure Eng..

[31]  R. Jayakrishnan,et al.  Dynamic Shared‐Taxi Dispatch Algorithm with Hybrid‐Simulated Annealing , 2016, Comput. Aided Civ. Infrastructure Eng..

[32]  Nazmul Siddique,et al.  Brief history of natural sciences for nature-inspired computing in engineering , 2016 .

[33]  George K. Chang,et al.  Adaptive quality control and acceptance of pavement material density for intelligent road construction , 2016 .

[34]  Donghai Liu,et al.  Real-Time Quality Monitoring and Control of Highway Compaction , 2016 .

[35]  G. I. Giannakis,et al.  Sense‐Think‐Act Framework for Intelligent Building Energy Management , 2016, Comput. Aided Civ. Infrastructure Eng..

[36]  S. Varadarajan,et al.  Image Processing of Natural Calamity Images Using Healthy Bacteria Foraging Optimization Algorithm , 2016 .

[37]  Georgios Dounias,et al.  Evolutionary computation for resource leveling optimization in project management , 2016, Integr. Comput. Aided Eng..

[38]  Anastasios Rigos,et al.  A Chebyshev polynomial radial basis function neural network for automated shoreline extraction from coastal imagery , 2016, Integr. Comput. Aided Eng..

[39]  Ferrante Neri,et al.  Covariance matrix adaptation pareto archived evolution strategy with hypervolume-sorted adaptive grid algorithm , 2016, Integr. Comput. Aided Eng..

[40]  Hojjat Adeli,et al.  Applications of Harmony Search Algorithms in Engineering , 2015, Int. J. Artif. Intell. Tools.

[41]  Hojjat Adeli,et al.  Hybrid Harmony Search Algorithms , 2015, Int. J. Artif. Intell. Tools.

[42]  Hojjat Adeli,et al.  Central force metaheuristic optimisation , 2015 .

[43]  Hojjat Adeli,et al.  Nature Inspired Computing: An Overview and Some Future Directions , 2015, Cognitive Computation.

[44]  Hojjat Adeli,et al.  Harmony Search Algorithm and its Variants , 2015, Int. J. Pattern Recognit. Artif. Intell..

[45]  Jui-Sheng Chou,et al.  Smart Artificial Firefly Colony Algorithm‐Based Support Vector Regression for Enhanced Forecasting in Civil Engineering , 2015, Comput. Aided Civ. Infrastructure Eng..

[46]  Matjaz Perc,et al.  A review of chaos-based firefly algorithms: Perspectives and research challenges , 2015, Appl. Math. Comput..

[47]  Hojjat Adeli,et al.  Water Drop Algorithms , 2014, Int. J. Artif. Intell. Tools.

[48]  Hojjat Adeli,et al.  Spiral Dynamics Algorithm , 2014, Int. J. Artif. Intell. Tools.

[49]  George K. Chang,et al.  Experimental and numerical study of asphalt material geospatial heterogeneity with intelligent compaction technology on roads , 2014 .

[50]  Li Zilong,et al.  Compaction quality assessment of earth-rock dam materials using roller-integrated compaction monitoring technology , 2014 .

[51]  Sesh Commuri,et al.  Dynamical Response of Vibratory Rollers during the Compaction of Asphalt Pavements , 2014 .

[52]  Idel Montalvo,et al.  Water Distribution System Computer‐Aided Design by Agent Swarm Optimization , 2014, Comput. Aided Civ. Infrastructure Eng..

[53]  Qian Mi Yu,et al.  Analysis of Application Situation of Continuous Compaction Control (CCC) , 2014 .

[54]  M. Tripathy,et al.  Optimal power flow solution of wind integrated power system using modified bacteria foraging algorithm , 2014 .

[55]  Sesh Commuri,et al.  Viscoelastic-Plastic Model of Asphalt-Roller Interaction , 2013 .

[56]  Nazmul Siddique,et al.  Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing , 2013 .

[57]  Min-Yuan Cheng,et al.  Improving classification accuracy of project dispute resolution using hybrid artificial intelligence and support vector machine models , 2013, Expert Syst. Appl..

[58]  Dimitrios V. Rovas,et al.  A Sense-think-act Methodology for Intelligent Building Energy Management , 2013 .

[59]  George K. Chang,et al.  Development of a systematic method for intelligent compaction data analysis and management , 2012 .

[60]  Arturo Garcia-Perez,et al.  MUSIC‐ANN Analysis for Locating Structural Damages in a Truss‐Type Structure by Means of Vibrations , 2012, Comput. Aided Civ. Infrastructure Eng..

[61]  Ali Akbar Ramezanianpour,et al.  Hybrid support vector regression – Particle swarm optimization for prediction of compressive strength and RCPT of concretes containing metakaolin , 2012 .

[62]  Sesh Commuri,et al.  Quality Assurance of Hot Mix Asphalt Pavements Using the Intelligent Asphalt Compaction Analyzer , 2012 .

[63]  Luo You,et al.  Review of Dam-break Research of Earth-rock Dam Combining with Dam Safety Management , 2012 .

[64]  I-Tung Yang,et al.  Reliability-based design optimization with cooperation between support vector machine and particle swarm optimization , 2012, Engineering with Computers.

[65]  Sesh Commuri,et al.  Intelligent Asphalt Compaction Analyzer , 2011 .

[66]  Qi Wu,et al.  Hybrid wavelet ν-support vector machine and chaotic particle swarm optimization for regression estimation , 2011, Expert Syst. Appl..

[67]  Simon Fong,et al.  Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications , 2011, NDT.

[68]  Bo Cui,et al.  Real-time compaction quality monitoring of high core rockfill dam , 2011 .

[69]  Wei-Chiang Hong,et al.  SVR with hybrid chaotic genetic algorithms for tourism demand forecasting , 2011, Appl. Soft Comput..

[70]  Hojjat Adeli,et al.  Enhanced probabilistic neural network with local decision circles: A robust classifier , 2010, Integr. Comput. Aided Eng..

[71]  David White,et al.  Geostatistical Analysis for Spatially Referenced Roller-Integrated Compaction Measurements , 2010 .

[72]  Bo Cui,et al.  Theoretical research on construction quality real-time monitoring and system integration of core rockfill dam , 2009 .

[73]  Hojjat Adeli,et al.  A probabilistic neural network for earthquake magnitude prediction , 2009, Neural Networks.

[74]  Li Wang,et al.  Knowledge portal construction and resources integration for a large scale hydropower dam , 2009 .

[75]  G. Tutz,et al.  An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. , 2009, Psychological methods.

[76]  Mark J. Thompson,et al.  Relationships between In Situ and Roller-Integrated Compaction Measurements for Granular Soils , 2008 .

[77]  Sesh Commuri,et al.  Neural Network-based Intelligent Compaction Analyzer for Estimating Compaction Quality of Hot Asphalt Mixes , 2008 .

[78]  D. R. Cutler,et al.  Utah State University From the SelectedWorks of , 2017 .

[79]  Hojjat Adeli,et al.  Pseudospectra, MUSIC, and dynamic wavelet neural network for damage detection of highrise buildings , 2007 .

[80]  Michiel C. van Wezel,et al.  Improved customer choice predictions using ensemble methods , 2005, Eur. J. Oper. Res..

[81]  L. Buydens,et al.  Determination of optimal support vector regression parameters by genetic algorithms and simplex optimization , 2005 .

[82]  George Morcous,et al.  Prediction of Onset of Corrosion in Concrete Bridge Decks Using Neural Networks and Case‐Based Reasoning , 2005 .

[83]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[84]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[85]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[86]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[87]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[88]  Vladimir Naumovich Vapni.  The Nature of Statistical Learning Theory , 1995 .

[89]  Hojjat Adeli,et al.  Machine Learning: Neural Networks, Genetic Algorithms, and Fuzzy Systems , 1994 .

[90]  H. Adeli,et al.  OBJECT-ORIENTED FINITE ELEMENT ANALYSIS USING EER MODEL , 1993 .