Name
Title | ||
|---|---|---|
High-performance concrete compressive strength prediction using Genetic Weighted Pyramid Operation Tree (GWPOT) |
Abstract | ||
|---|---|---|
This study uses the Genetic Weighted Pyramid Operation Tree (GWPOT) to build a model to solve the problem of predicting high-performance concrete compressive strength. GWPOT is a new improvement of the genetic operation tree that consists of the Genetic Algorithm, Weighted Operation Structure, and Pyramid Operation Tree. The developed model obtained better results in benchmark tests against several widely used artificial intelligence (AI) models, including the Artificial Neural Network (ANN), Support Vector Machine (SVM), and Evolutionary Support Vector Machine Inference Model (ESIM). Further, unlike competitor models that use ''black-box'' techniques, the proposed GWPOT model generates explicit formulas, which provide important advantages in practical application. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.engappai.2013.11.014 | Eng. Appl. of AI |
Keywords | Field | DocType |
genetic weighted pyramid operation,support vector machine,developed model,proposed gwpot model,high-performance concrete compressive strength,genetic algorithm,vector machine inference model,evolutionary support,competitor model,pyramid operation tree,weighted operation structure,prediction | Inference,Computer science,Support vector machine,Compressive strength,Pyramid,Artificial intelligence,Artificial neural network,High performance concrete,Machine learning,Genetic algorithm | Journal |
Volume | ISSN | Citations |
29, | 0952-1976 | 3 |
PageRank | References | Authors |
0.40 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min-Yuan Cheng | 1 | 174 | 19.84 |
| Pratama Mahardika Firdausi | 2 | 3 | 0.40 |
| Doddy Prayogo | 3 | 64 | 4.66 |