Journal ArticleDOI
Computational AI prediction models for residual tensile strength of GFRP bars aged in the alkaline concrete environment
TLDR
In this article, a new strategy for finding tensile strength retention (TSR) using empirical models based on the strong non-linear ability of artificial intelligence techniques, i.e., artificial neuro-networking (ANN), gene expression programming (GEP), and adaptive neuro-fuzzy inference system (ANFIS), was presented.About:
This article is published in Ocean Engineering.The article was published on 2021-07-15. It has received 62 citations till now.read more
Citations
More filters
Journal ArticleDOI
Predicting the compaction characteristics of expansive soils using two genetic programming-based algorithms
TL;DR: In this article, gene expression programming (GEP) and multi-expression programming (MEP) are utilized to formulate new prediction models for determining the compaction parameters (ρdmax and wopt) of expansive soils.
Journal ArticleDOI
Compressive strength prediction of basalt fiber reinforced concrete via random forest algorithm
TL;DR: In this article , the strength prediction model of basalt fiber concrete is constructed by random forest method based on experiments to study the strengthening effect of Basalt fiber in concrete composites and explore the impact of different fiber sizes and fiber content on the strength of baseboard concrete.
Journal ArticleDOI
Effective use of recycled waste PET in cementitious grouts for developing sustainable semi-flexible pavement surfacing using artificial neural network
Muhammad Imran Khan,Muslich Hartadi Sutanto,Kaffayatullah Khan,Mudassir Iqbal,Madzlan Napiah,Salah E. Zoorob,Jiří Jaromír Klemeš,Awais Bokhari,Waqas Rafiq +8 more
TL;DR: In this paper , the results achieved from regular and irradiated PET substituted cement gouts were used to develop single hidden layers (SHLs) and two hidden layers neural network models.
Journal ArticleDOI
Evaluation of tensile strength degradation of GFRP rebars in harsh alkaline conditions using non-linear genetic-based models
TL;DR: In this paper, an extensive database comprising 715 tested specimens were collected from literature to develop GEP tree-based model for determining tensile strength retention (TSR) and environment reduction factor (CE).
Journal ArticleDOI
Prediction of rapid chloride penetration resistance of metakaolin based high strength concrete using light GBM and XGBoost models by incorporating SHAP analysis
Anas Abdulalim Alabdullah,Mudassir Iqbal,Muhammad Zahid,Kaffayatullah Khan,Muhammad Nasir Amin,Fazal E. Jalal +5 more
TL;DR: In this article , the authors investigated the non-linear capabilities of two machine learning prediction models, namely Light GBM and XGBoost, for predicting the values of Rapid Chloride Penetration Test (RCPT).
References
More filters
Journal ArticleDOI
ANFIS: adaptive-network-based fuzzy inference system
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Book
An Introduction to Genetic Algorithms
TL;DR: An Introduction to Genetic Algorithms focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.
Journal ArticleDOI
Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance
Cort J. Willmott,Kenji Matsuura +1 more
TL;DR: In this paper, the root-mean-square error (RMSE) and the mean absolute error (MAE) were examined to describe average model-performance error, and it was shown that MAE is a more natural measure of average error than RMSE.
Journal ArticleDOI
Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature
TL;DR: In this article, the root mean square error (RMSE) and the mean absolute error (MAE) are used to evaluate model performance and it is shown that the RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian.
Journal ArticleDOI
Interpretation of the Correlation Coefficient: A Basic Review
TL;DR: The basic aspects of correlation analysis are discussed with examples given from professional journals and the interpretations and limitations of the correlation coefficient are focused on.