EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 258 (2019) MATEC Web Conf., 258 (2019) 02010 References
Open Access
Issue
MATEC Web Conf.
Volume 258, 2019
International Conference on Sustainable Civil Engineering Structures and Construction Materials (SCESCM 2018)
Article Number 02010
Number of page(s) 6
Section Construction Management, Construction Method and System, Optimization and Innovation in Structural Design
DOI https://doi.org/10.1051/matecconf/201925802010
Published online 25 January 2019
  1. U. Smoltczyk, Geotechnical Engineering Handbook, Procedures, John Wiley & Sons,. ISBN:3433014507 (2003) [Google Scholar]
  2. D. Tjandra Indarto, R.A.A. Soemitro, Behavior of expansive soil under water content variation and its impact to adhesion factor on friction capacity of pile foundation, International Journal of Applied Engineering Research 10, 38913–38917 (2015) [in Indonesian] [Google Scholar]
  3. D. Tjandra Indarto, R.A.A. Soemitro, Effect of drying-wetting process on friction capacity and adhesion factor of pile foundation in clayey soil, Jurnal Teknologi 77, 145–150 (2015) [in Indonesian] [Google Scholar]
  4. H.G. Poulos, E.H. Davis, Pile foundation analysis and design, ISBN:0471020842 (1980) [Google Scholar]
  5. 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, 242–253 (2011) [CrossRef] [Google Scholar]
  6. S.-H. Liao, P.-H. Chu, P.-Y. Hsiao, Data mining techniques and applications - A decade review from 2000 to 2011, Expert Systems with Applications 39, 11303–11311 (2012) [CrossRef] [Google Scholar]
  7. M.A. Shahin, Load-settlement modeling of axially loaded steel driven piles using CPT-based recurrent neural networks, Soils and Foundations 54, 515–522 (2014) [CrossRef] [Google Scholar]
  8. J.A.K. Suykens, J. Vandewalle, Least Squares Support Vector Machine Classifiers, Neural Process. Lett. 9, 293–300 (1999) [Google Scholar]
  9. P. Samui, Least square support vector machine and relevance vector machine for evaluating seismic liquefaction potential using SPT, Natural Hazards 59, 811–822 (2011) [CrossRef] [Google Scholar]
  10. M.-Y. Cheng, P.M. Firdausi, D. Prayogo, High- performance concrete compressive strength prediction using Genetic Weighted Pyramid Operation Tree (GWPOT), Engineering Applications of Artificial Intelligence 29, 104–113 (2014) [CrossRef] [Google Scholar]
  11. M.-Y. Cheng, D. Prayogo, Y.-W. Wu, Novel Genetic Algorithm-Based Evolutionary Support Vector Machine for Optimizing High-Performance Concrete Mixture, Journal of Computing in Civil Engineering 28, 06014003 (2014) [CrossRef] [Google Scholar]
  12. M.-Y. Cheng, D.K. Wibowo, D. Prayogo, A.F.V. Roy, Predicting productivity loss caused by change orders using the evolutionary fuzzy support vector machine inference model, Journal of Civil Engineering and Management 21, 881–892 (2015) [CrossRef] [Google Scholar]
  13. M.-Y. Cheng, D. Prayogo, Modeling the permanent deformation behavior of asphalt mixtures using a novel hybrid computational intelligence, ISARC 2016-33 rd International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction, Auburn, USA, 1009–1015 (2016) [Google Scholar]
  14. D. Prayogo, M.Y. Cheng, J. Widjaja, H. Ongkowijoyo, H. Prayogo, Prediction of concrete compressive strength from early age test result using an advanced metaheuristic-based machine learning technique ISARC 2017 - Proceedings of the 34th International Symposium on Automation and Robotics in Construction (2017) [Google Scholar]
  15. M.-Y. Cheng, D. Prayogo, Y.-W. Wu, Prediction of permanent deformation in asphalt pavements using a novel symbiotic organisms search-least squares support vector regression, Neural Comput. Appl. (2018) [Google Scholar]
  16. D. Prayogo, Metaheuristic-Based Machine Learning System for Prediction of Compressive Strength based on Concrete Mixture Properties and Early- Age Strength Test Results, Civil Engineering Dimension 20, 21–29 (2018) [CrossRef] [Google Scholar]
  17. 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]
  18. K.S. Kulkrni, D.-K. Kim, S.K. Sekar, P. Samui, Model of Least Square Support Vector Machine (LSSVM) for Prediction of Fracture Parameters of Concrete, International Journal of Concrete Structures and Materials 5, 29–33 (2011) [CrossRef] [Google Scholar]
  19. D.-H. Tran, M.-Y. Cheng, D. Prayogo, A novel Multiple Objective Symbiotic Organisms Search (MOSOS) for time-cost-labor utilization tradeoff problem, Knowledge-Based Systems 94, 132–145 (2016) [CrossRef] [Google Scholar]
  20. 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, 04015036 (2016) [CrossRef] [Google Scholar]
  21. D. Prayogo, M.-Y. Cheng, H. Prayogo, A Novel Implementation of Nature-inspired Optimization for Civil Engineering: A Comparative Study of Symbiotic Organisms Search, Civil Engineering Dimension 19, 36–43 (2017) [Google Scholar]
  22. 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, Applied Soft Computing 52, 657–672 (2017) [CrossRef] [Google Scholar]
  23. G.G. Tejani, V.J. Savsani, V.K. Patel, Adaptive symbiotic organisms search (SOS) algorithm for structural design optimization, J. Comput. Des. Eng. 3, 226–249 (2016) [Google Scholar]
  24. 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, 04017085 (2018) [CrossRef] [Google Scholar]
  25. G.G. Tejani, V.J. Savsani, V.K. Patel, S. Mirjalili, Truss optimization with natural frequency bounds using improved symbiotic organisms search, Knowl.-Based Syst. 143, 162–178 (2018) [CrossRef] [Google Scholar]
  26. D. Prayogo, M.-Y. Cheng, F.T. Wong, D. Tjandra, D.-H. Tran, Optimization model for construction project resource leveling using a novel modified symbiotic organisms search, Asian Journal of Civil Engineering (2018) [Google Scholar]
  27. D. Prayogo, R.A. Gosno, R. Evander, S. Limanto, Implementasi Metode Metaheuristik Symbiotic Organisms Search Dalam Penentuan Tata Letak Fasilitas Proyek Konstruksi Berdasarkan Jarak Tempuh Pekerja, Jurnal Teknik Industri 19, 103–114 (2018) [in Indonesian] [CrossRef] [Google Scholar]
  28. C.W. Hsu, C.C. Chang, C.J. Lin, A practical guide to support vector classification (2003) [Google Scholar]
  29. F. Pooya Nejad, M.B. Jaksa, Load-settlement behavior modeling of single piles using artificial neural networks and CPT data, Computers and Geotechnics 89, 9–21 (2017) [CrossRef] [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.