Multiobjective Optimization of a 3D Laser Scanning Scheme for Engineering Structures Based on RF-NSGA-II

[1]  Rosa M. Rodríguez,et al.  Expertise-Structure and Risk-Appetite-Integrated Two-Tiered Collective Opinion Generation Framework for Large-Scale Group Decision Making , 2022, IEEE Transactions on Fuzzy Systems.

[2]  Yang Liu,et al.  Prediction of the durability of high-performance concrete using an integrated RF-LSSVM model , 2022, Construction and Building Materials.

[3]  Yawei Qin,et al.  Prediction of the frost resistance of high-performance concrete based on RF-REF: A hybrid prediction approach , 2022, Construction and Building Materials.

[4]  Jiao Bai,et al.  Intelligent Prediction of Cryptogenic Stroke Using Patent Foramen Ovale from TEE Imaging Data and Machine Learning Methods , 2022, International Journal of Computational Intelligence Systems.

[5]  Yang Liu,et al.  Enhancing building energy efficiency using a random forest model: A hybrid prediction approach , 2021 .

[6]  Limao Zhang,et al.  RISK PREDICTION AND DIAGNOSIS OF WATER SEEPAGE IN OPERATIONAL SHIELD TUNNELS BASED ON RANDOM FOREST , 2021, JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT.

[7]  Mokhtar Mohammadi,et al.  Forecasting sidewall displacement of underground caverns using machine learning techniques , 2021 .

[8]  Hongjun Lin,et al.  New methods based on back propagation (BP) and radial basis function (RBF) artificial neural networks (ANNs) for predicting the occurrence of haloketones in tap water. , 2021, The Science of the total environment.

[9]  Limao Zhang,et al.  Multi-objective optimization in tunnel line alignment under uncertainty , 2021 .

[10]  Jiaming Chen,et al.  Research on improved wavelet convolutional wavelet neural networks , 2020, Applied Intelligence.

[11]  K. Ghosh,et al.  Assessing the soil quality of Bansloi river basin, eastern India using soil-quality indices (SQIs) and Random Forest machine learning technique , 2020 .

[12]  Hongyu Chen,et al.  Research on green renovations of existing public buildings based on a cloud model –TOPSIS method , 2020 .

[13]  Valério Rosset,et al.  Determining the trade-offs between data delivery and energy consumption in large-scale WSNs by multi-objective evolutionary optimization , 2020, Comput. Networks.

[14]  S. Hsu,et al.  Detecting corporate misconduct through random forest in China’s construction industry , 2020 .

[15]  Esmatullah Noorzai,et al.  Implementing the NSGA-II genetic algorithm to select the optimal repair and maintenance method of jack-up drilling rigs in Iranian shipyards , 2020 .

[16]  Xianguo Wu,et al.  Prediction of impermeability of the concrete structure based on random forest and support vector machine , 2020, IOP Conference Series: Earth and Environmental Science.

[17]  Xianguo Wu,et al.  Energy consumption prediction and diagnosis of public buildings based on support vector machine learning: A case study in China , 2020 .

[18]  Ajay K Sharma,et al.  NSGA-II with ENLU inspired clustering for wireless sensor networks , 2020, Wirel. Networks.

[19]  Hongyu Lin,et al.  Combined electricity-heat-cooling-gas load forecasting model for integrated energy system based on multi-task learning and least square support vector machine , 2020 .

[20]  Hamid Eskandari-Naddaf,et al.  Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete , 2020 .

[21]  Dietmar Stephan,et al.  Ensemble data mining modeling in corrosion of concrete sewer: A comparative study of network-based (MLPNN & RBFNN) and tree-based (RF, CHAID, & CART) models , 2020, Adv. Eng. Informatics.

[22]  Hongxing Yang,et al.  Approaching low-energy high-rise building by integrating passive architectural design with photovoltaic application , 2019, Journal of Cleaner Production.

[23]  Qi Lu,et al.  Cutting parameter optimization for machining operations considering carbon emissions , 2019, Journal of Cleaner Production.

[24]  Qian Wang,et al.  Applications of 3D point cloud data in the construction industry: A fifteen-year review from 2004 to 2018 , 2019, Adv. Eng. Informatics.

[25]  Enrique Valero,et al.  Automatic segmentation of 3D point clouds of rubble masonry walls, and its application to building surveying, repair and maintenance , 2018, Automation in Construction.

[26]  Yong Wang,et al.  Economic and environmental evaluations in the two-echelon collaborative multiple centers vehicle routing optimization , 2018, Journal of Cleaner Production.

[27]  Ian J. Davies,et al.  Rapid mapping and analysing rock mass discontinuities with 3D terrestrial laser scanning in the underground excavation , 2018, International Journal of Rock Mechanics and Mining Sciences.

[28]  Wen Xiong,et al.  A multi-level 3D data registration approach for supporting reliable spatial change classification of single-pier bridges , 2018, Adv. Eng. Informatics.

[29]  Amos Darko,et al.  Sensitivity analysis of wind pressure coefficients on CAARC standard tall buildings in CFD simulations , 2018 .

[30]  Tao Zhang,et al.  On the effect of reference point in MOEA/D for multi-objective optimization , 2017, Appl. Soft Comput..

[31]  Yacine Rezgui,et al.  Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption , 2017 .

[32]  Ali Azadeh,et al.  Evolutionary multi-objective optimization of environmental indicators of integrated crude oil supply chain under uncertainty , 2017 .

[33]  V. Rodriguez-Galiano,et al.  Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines , 2015 .

[34]  Jules Thibault,et al.  A new algorithm using front prediction and NSGA-II for solving two and three-objective optimization problems , 2015 .

[35]  Jian Su,et al.  An optimization solution of a laser plane in vision measurement with the distance object between global origin and calibration points , 2015, Scientific Reports.

[36]  Javier Roca-Pardiñas,et al.  Analysis of the influence of range and angle of incidence of terrestrial laser scanning measurements on tunnel inspection , 2014 .

[37]  Moises Rivas-Lopez,et al.  Optimization of 3D laser scanning speed by use of combined variable step , 2014 .

[38]  Aniruddha Ghosh,et al.  A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[39]  Jos Boekhorst,et al.  Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle? , 2012, Briefings Bioinform..

[40]  Peng Yan,et al.  Ill-conditioned problems of dam safety monitoring models and their processing methods , 2011 .

[41]  M. Menenti,et al.  Scanning geometry: Influencing factor on the quality of terrestrial laser scanning points , 2011 .

[42]  Jean-François Fontaine,et al.  Systematic error correction of a 3D laser scanning measurement device , 2011 .

[43]  M. Vastaranta,et al.  Predicting individual tree attributes from airborne laser point clouds based on the random forests technique , 2011 .

[44]  Caijun Shi,et al.  Prediction of elastic modulus of normal and high strength concrete by support vector machine , 2010 .

[45]  Marjan Korosec,et al.  Identification and optimization of key process parameters in noncontact laser scanning for reverse engineering , 2010, Comput. Aided Des..

[46]  Jorge L. Martínez,et al.  Fast range-independent spherical subsampling of 3D laser scanner points and data reduction performance evaluation for scene registration , 2010, Pattern Recognit. Lett..

[47]  Eric W. T. Ngai,et al.  Customer churn prediction using improved balanced random forests , 2009, Expert Syst. Appl..

[48]  N. Yastikli Documentation of cultural heritage using digital photogrammetry and laser scanning , 2007 .

[49]  Lian Ding,et al.  Global optimization of a feature-based process sequence using GA and ANN techniques , 2005 .

[50]  Weiguo Liu,et al.  Comparison of non-linear mixture models: sub-pixel classification , 2005 .

[51]  Robert P. Sheridan,et al.  Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..

[52]  B. Kusnoto,et al.  Reliability of a 3D surface laser scanner for orthodontic applications. , 2002, American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics.

[53]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[54]  Tong Heng Lee,et al.  Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization , 2001, IEEE Trans. Evol. Comput..

[55]  Gary B. Lamont,et al.  Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art , 2000, Evolutionary Computation.

[56]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[57]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..