EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 203 (2018) MATEC Web Conf., 203 (2018) 06006 References
Open Access
Issue
MATEC Web Conf.
Volume 203, 2018
International Conference on Civil, Offshore & Environmental Engineering 2018 (ICCOEE 2018)
Article Number 06006
Number of page(s) 12
Section Structures and Materials
DOI https://doi.org/10.1051/matecconf/201820306006
Published online 17 September 2018
  1. M.-Y. Cheng, P.M. Firdausi, D. Prayogo, High-performance concrete compressive strength prediction using Genetic Weighted Pyramid Operation Tree (GWPOT), Eng. Appl. Artif. Intell. 29, 104-113 (2014) [Google Scholar]
  2. M.-Y. Cheng, D. Prayogo, Y.-W. Wu, Novel Genetic Algorithm-Based Evolutionary Support Vector Machine for Optimizing High-Performance Concrete Mixture, J. Comput. Civ. Eng. 28, 4, 06014003 (2014) [Google Scholar]
  3. I.C. Yeh, Modeling of strength of high-performance concrete using artificial neural networks, Cem. Concr. Res. 28, 12, 1797-1808 (1998) [CrossRef] [Google Scholar]
  4. J.-S. Chou, C.-K. Chiu, M. Farfoura, I. Al-Taharwa, Optimizing the Prediction Accuracy of Concrete Compressive Strength Based on a Comparison of Data-Mining Techniques, J. Comput. Civ. Eng. 25, 3, 242-253 (2011) [CrossRef] [Google Scholar]
  5. N.-D. Hoang, D.T. Bui, Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi-dataset study, B. Eng. Geol. Environ. 77, 1, 191-204 (2016) [CrossRef] [Google Scholar]
  6. D. Prayogo, Metaheuristic-Based Machine Learning System for Prediction of Compressive Strength based on Concrete Mixture Properties and Early-Age Strength Test Results, Civ. Eng. Dimens. 20, 1, 21-29 (2018) [Google Scholar]
  7. D. Prayogo, Y.T.T. Susanto, Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self-Tuning Least Squares Support Vector Machine, Adv. Civ. Eng. 2018 (2018) [Google Scholar]
  8. D. Tien Bui, B.T. Pham, Q.P. Nguyen, N.-D. Hoang, Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization: a case study in Central Vietnam, Int. J. Digit. Earth 9, 11, 1077-1097 (2016) [CrossRef] [Google Scholar]
  9. J.A.K. Suykens, J. Vandewalle, Least Squares Support Vector Machine Classifiers, Neural Process. Lett. 9, 3, 293-300 (1999) [Google Scholar]
  10. M.-Y. Cheng, D. Prayogo, Symbiotic Organisms Search: A new metaheuristic optimization algorithm, Comput. Struct. 139, 98-112 (2014) [CrossRef] [Google Scholar]
  11. M.-Y. Cheng, D. Prayogo, D.-H. Tran, Optimizing Multiple-Resources Leveling in Multiple Projects Using Discrete Symbiotic Organisms Search, J. Comput. Civ. Eng. 30, 3, 04015036 (2016) [CrossRef] [Google Scholar]
  12. D.-H. Tran, M.-Y. Cheng, D. Prayogo, A novel Multiple Objective Symbiotic Organisms Search (MOSOS) for time-cost-labor utilization tradeoff problem, Knowl.-Based Syst. 94, 132-145 (2016) [Google Scholar]
  13. D. Prayogo, M.-Y. Cheng, H. Prayogo, A Novel Implementation of Nature-inspired Optimization for Civil Engineering: A Comparative Study of Symbiotic Organisms Search, Civ. Eng. Dimens. 19, 1, 36-43 (2017) [Google Scholar]
  14. A. Panda, S. Pani, A Symbiotic Organisms Search algorithm with adaptive penalty function to solve multi-objective constrained optimization problems, Appl. Soft Comput. 46, 344-360 (2016) [CrossRef] [Google Scholar]
  15. V.F. Yu, A.A.N.P. Redi, C.-L. Yang, E. Ruskartina, B. Santosa, Symbiotic organisms search and two solution representations for solving the capacitated vehicle routing problem, Appl. Soft Comput. 52, 657-672 (2017) [Google Scholar]
  16. G.G. Tejani, V.J. Savsani, S. Bureerat, V.K. Patel, Topology and Size Optimization of Trusses with Static and Dynamic Bounds by Modified Symbiotic Organisms Search, J. Comput. Civ. Eng. 32, 2, 04017085 (2018) [CrossRef] [Google Scholar]
  17. G.G. Tejani, V.J. Savsani, V.K. Patel, Adaptive symbiotic organisms search (SOS) algorithm for structural design optimization, Journal of Computational Design and Engineering 3, 3, 226-249 (2016) [CrossRef] [Google Scholar]
  18. R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection (International Joint Conference on Artificial Intelligence, 14, Stanford, CA, 1995) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.