Next Article in Journal
User Interactions for Augmented Reality Smart Glasses: A Comparative Evaluation of Visual Contexts and Interaction Gestures
Next Article in Special Issue
Bionic Stiffener Layout Optimization with a Flexible Plate in Solar-Powered UAV Surface Structure Design
Previous Article in Journal
Towards Model-Based Online Monitoring of Cyclist’s Head Thermal Comfort: Smart Helmet Concept and Prototype
Previous Article in Special Issue
Influence of Variation/Response Space Complexity and Variable Completeness on BP-ANN Model Establishment: Case Study of Steel Ladle Lining
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 9
Issue 15
10.3390/app9153172
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt

by
Hoang-Long Nguyen
1,
Thanh-Hai Le
1,
Cao-Thang Pham
2,
Tien-Thinh Le
3,*,
Lanh Si Ho
1,4,
Vuong Minh Le
5,
Binh Thai Pham
1,* and
Hai-Bang Ly
1,*
1
University of Transport Technology, Hanoi 100000, Vietnam
2
Le Quy Don Technical University, Hanoi 100000 Vietnam
3
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
4
Department of Civil and Environmental Engineering, Graduate School of Engineering, Hiroshima University, 1-4-1, Kagamiyama, Higashi-Hiroshima, Hiroshima 739-527, Japan
5
Faculty of Engineering, Vietnam National University of Agriculture, Gia Lam, Hanoi 100000, Vietnam
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(15), 3172; https://doi.org/10.3390/app9153172
Submission received: 18 June 2019 / Revised: 2 August 2019 / Accepted: 2 August 2019 / Published: 4 August 2019
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
Browse Figures
Versions Notes

Abstract

The main objective of this study is to develop and compare hybrid Artificial Intelligence (AI) approaches, namely Adaptive Network-based Fuzzy Inference System (ANFIS) optimized by Genetic Algorithm (GAANFIS) and Particle Swarm Optimization (PSOANFIS) and Support Vector Machine (SVM) for predicting the Marshall Stability (MS) of Stone Matrix Asphalt (SMA) materials. Other important properties of the SMA, namely Marshall Flow (MF) and Marshall Quotient (MQ) were also predicted using the best model found. With that goal, the SMA samples were fabricated in a local laboratory and used to generate datasets for the modeling. The considered input parameters were coarse and fine aggregates, bitumen content and cellulose. The predicted targets were Marshall Parameters such as MS, MF and MQ. Models performance assessment was evaluated thanks to criteria such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and correlation coefficient (R). A Monte Carlo approach with 1000 simulations was used to deduce the statistical results to assess the performance of the three proposed AI models. The results showed that the SVM is the best predictor regarding the converged statistical criteria and probability density functions of RMSE, MAE and R. The results of this study represent a contribution towards the selection of a suitable AI approach to quickly and accurately determine the Marshall Parameters of SMA mixtures.
Keywords: adaptive network-based fuzzy inference system; stone matrix asphalt; genetic algorithm; particle swarm optimization; support vector machine adaptive network-based fuzzy inference system; stone matrix asphalt; genetic algorithm; particle swarm optimization; support vector machine

Share and Cite

MDPI and ACS Style

Nguyen, H.-L.; Le, T.-H.; Pham, C.-T.; Le, T.-T.; Ho, L.S.; Le, V.M.; Pham, B.T.; Ly, H.-B. Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt. Appl. Sci. 2019, 9, 3172. https://doi.org/10.3390/app9153172

AMA Style

Nguyen H-L, Le T-H, Pham C-T, Le T-T, Ho LS, Le VM, Pham BT, Ly H-B. Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt. Applied Sciences. 2019; 9(15):3172. https://doi.org/10.3390/app9153172

Chicago/Turabian Style

Nguyen, Hoang-Long, Thanh-Hai Le, Cao-Thang Pham, Tien-Thinh Le, Lanh Si Ho, Vuong Minh Le, Binh Thai Pham, and Hai-Bang Ly. 2019. "Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt" Applied Sciences 9, no. 15: 3172. https://doi.org/10.3390/app9153172

APA Style

Nguyen, H.-L., Le, T.-H., Pham, C.-T., Le, T.-T., Ho, L. S., Le, V. M., Pham, B. T., & Ly, H.-B. (2019). Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt. Applied Sciences, 9(15), 3172. https://doi.org/10.3390/app9153172

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop