Shallow Foundation Settlement Quantification

Application of Hybridized Adaptive Neuro-Fuzzy Inference System Model

Document identifier: oai:DiVA.org:ltu-77816
Access full text here:10.1155/2020/7381617
Keyword: Engineering and Technology, Civil Engineering, Geotechnical Engineering, Teknik och teknologier, Samhällsbyggnadsteknik, Geoteknik, Shallow Foundation, Settlement Quantification, Adaptive Neuro-Fuzzy Inference System Model, Soil Mechanics
Publication year: 2020
Relevant Sustainable Development Goals (SDGs):
SDG 9 Industry, innovation and infrastructureSDG 3 Good health and wellbeing
The SDG label(s) above have been assigned by OSDG.ai

Abstract:

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.

Authors

Mariamme Mohammed

College of Agricultural Engineering Sciences, University of Baghdad, Baghdad, Iraq
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Ahmad Sharafati

Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
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Nadhir Al-Ansari

Luleå tekniska universitet; Geoteknologi
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Zaher Yaseen

Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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