Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro-Fuzzy Inference System Model
Joint Authors
Mohammed, Mariamme
Sharafati, Ahmad
Al-Ansari, Nadhir
Yaseen, Zaher Mundher
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-02-22
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Settlement simulating in cohesion materials is a crucial issue due to complexity of cohesion soil texture.
This research emphasis on the implementation of newly developed machine learning models called hybridized Adaptive Neuro-Fuzzy Inference System (ANFIS) with Particle Swarm Optimization (PSO) algorithm, Ant Colony optimizer (ACO), Differential Evolution (DE), and Genetic Algorithm (GA) as efficient approaches to predict settlement of shallow foundation over cohesion soil properties.
The width of footing (B), pressure of footing (qa), geometry of footing (L/B), count of SPT blow (N), and ratio of footing embedment (Df/B) are considered as predictive variables.
Nonhomogeneity and inconsistency of employed dataset is a major concern during prediction modeling.
Hence, two different modeling scenarios (i) preprocessed dataset (PP) and (ii) nonprocessed (initial) dataset (NP) were inspected.
To assess the accuracy of the applied hybrid models and standalone one, multiple statistical metrics were computed and analyzed over the training and testing phases.
Results indicated ANFIS-PSO model exhibited an accurate and reliable prediction data intelligent and had the highest predictability performance against all employed models.
In addition, results demonstrated that data preprocessing is highly essential to be performed prior to building the predictive models.
Overall, ANFIS-PSO model showed a robust machine learning for settlement prediction.
American Psychological Association (APA)
Mohammed, Mariamme& Sharafati, Ahmad& Al-Ansari, Nadhir& Yaseen, Zaher Mundher. 2020. Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro-Fuzzy Inference System Model. Advances in Civil Engineering،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1122722
Modern Language Association (MLA)
Mohammed, Mariamme…[et al.]. Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro-Fuzzy Inference System Model. Advances in Civil Engineering No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1122722
American Medical Association (AMA)
Mohammed, Mariamme& Sharafati, Ahmad& Al-Ansari, Nadhir& Yaseen, Zaher Mundher. Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro-Fuzzy Inference System Model. Advances in Civil Engineering. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1122722
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1122722