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
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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
Other publications >>

Ahmad Sharafati

Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Other publications >>

Nadhir Al-Ansari

Luleå tekniska universitet; Geoteknologi
Other publications >>

Zaher Yaseen

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

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    header: 
        identifier: oai:DiVA.org:ltu-77816
        datestamp: 2021-04-19T12:42:33Z
        setSpec: SwePub-ltu
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        mods: 
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                version: 3.7
            recordInfo: 
                recordContentSource: ltu
                recordCreationDate: 2020-02-23
            identifier: 
                http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-77816
                10.1155/2020/7381617
                2-s2.0-85081032712
            titleInfo: 
                @attributes: 
                    lang: eng
                title: Shallow Foundation Settlement Quantification
                subTitle: Application of Hybridized Adaptive Neuro-Fuzzy Inference System Model
            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.

            subject: 
                    @attributes: 
                        lang: eng
                        authority: uka.se
                    topic: 
                        Engineering and Technology
                        Civil Engineering
                        Geotechnical Engineering
                    @attributes: 
                        lang: swe
                        authority: uka.se
                    topic: 
                        Teknik och teknologier
                        Samhällsbyggnadsteknik
                        Geoteknik
                    @attributes: 
                        lang: eng
                    topic: Shallow Foundation
                    @attributes: 
                        lang: eng
                    topic: Settlement Quantification
                    @attributes: 
                        lang: eng
                    topic: Adaptive Neuro-Fuzzy Inference System Model
                    @attributes: 
                        lang: swe
                        authority: ltu
                    topic: Geoteknik
                    genre: Research subject
                    @attributes: 
                        lang: eng
                        authority: ltu
                    topic: Soil Mechanics
                    genre: Research subject
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                languageTerm: eng
            genre: 
                publication/journal-article
                ref
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                Published
                4
                
Validerad;2020;Nivå 2;2020-02-24 (johcin)

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                        Mohammed
                        Mariamme
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                    affiliation: College of Agricultural Engineering Sciences University of Baghdad Baghdad Iraq
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                        Sharafati
                        Ahmad
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                        Al-Ansari
                        Nadhir
                        1947-
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                        roleTerm: aut
                    affiliation: 
                        Luleå tekniska universitet
                        Geoteknologi
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                        0000-0002-6790-2653
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                        Yaseen
                        Zaher
                    role: 
                        roleTerm: aut
                    affiliation: Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City Vietnam
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                dateIssued: 2020
                publisher: Hindawi Publishing Corporation
                place: 
                    placeTerm: UK
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                    type: host
                titleInfo: 
                    title: Advances in Civil Engineering / Hindawi
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                    1687-8086
                    1687-8094
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                                type: volume
                            number: 2020
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                url: http://ltu.diva-portal.org/smash/get/diva2:1395493/FULLTEXT01.pdf
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