Soft Computing-Based Models for Estimating Undrained Bearing Capacity Factor of Open Caisson in Heterogeneous Clay

Open caissons are commonly used in the construction of various underground structures, such as launch and reception shafts for tunnel-boring machines, storage or attenuation tanks, and cofferdams. During the sinking phase, the cutting edge of a caisson wall with a cutting face encounters soil and is...

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Vydané v:	Geotechnical and geological engineering Ročník 42; číslo 6; s. 5335 - 5361
Hlavní autori:	Suppakul, Rungroad, Chavda, Jitesh T., Jitchaijaroen, Wittaya, Keawsawasvong, Suraparb, Rattanadecho, Phadungsak
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cham Springer International Publishing 01.08.2024 Springer Nature B.V
Predmet:	Artificial neural networks Bearing capacity Boring machines Caissons Civil Engineering Clay Cofferdams Earth and Environmental Science Earth Sciences Excavation Finite element method Geotechnical Engineering & Applied Earth Sciences Hydrogeology Hypotheses Limit analysis Neural networks Null hypothesis Regression models Roughness Shear strength Sinking Soft computing Soil bearing capacity Soil investigations Soil strength Tanks Technical Note Terrestrial Pollution Tunnel construction Underground construction Underground structures Variance analysis Waste Management/Waste Technology ANN FELA Cutting edge Cutting angle Heterogenous clay Open caisson
ISSN:	0960-3182, 1573-1529
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Abstract	Open caissons are commonly used in the construction of various underground structures, such as launch and reception shafts for tunnel-boring machines, storage or attenuation tanks, and cofferdams. During the sinking phase, the cutting edge of a caisson wall with a cutting face encounters soil and is subjected to loading to facilitate and control the sinking process. In this regard, the bearing capacity factor ( N ) of the cutting face of a circular open caisson in heterogeneous clay is evaluated via finite element limit analysis, which accounts for the increase in the undrained shear strength with depth. The parameters considered in this study cover practical aspects, including the excavation geometry, soil strength profile, and caisson geometry. This investigation also explored the impacts of the cutting face angle ( β ), roughness ( α ), ratio of the internal embedment depth to the embedment width ( H / B ), ratio of the internal radius to the embedment width ( R / B ), and strength gradient ratio ( ρB / s u 0 ). In particular, when the H / B > R / B ratio, the N value tends to stabilize. Crucially, when H / B > 4, an increasing trend in R / B leads to a rise in N until R / B exceeds 10, i.e., large diameter caissons, stabilizing the N value. Furthermore, the results reveal the significant dependency of the cutting face roughness and strength gradient ratio of clay on N . The artificial neural network model, which is a soft computing-based model, is also developed to present the undrained bearing capacity forecasting equation. Compared with conventional regression, including the multiple linear regression model and the multiple nonlinear regression model, it has excellent performance, as measured by eight indices. In addition, ANOVA and Z-tests can support the research hypothesis and reject the null hypothesis.
AbstractList	Open caissons are commonly used in the construction of various underground structures, such as launch and reception shafts for tunnel-boring machines, storage or attenuation tanks, and cofferdams. During the sinking phase, the cutting edge of a caisson wall with a cutting face encounters soil and is subjected to loading to facilitate and control the sinking process. In this regard, the bearing capacity factor ( N ) of the cutting face of a circular open caisson in heterogeneous clay is evaluated via finite element limit analysis, which accounts for the increase in the undrained shear strength with depth. The parameters considered in this study cover practical aspects, including the excavation geometry, soil strength profile, and caisson geometry. This investigation also explored the impacts of the cutting face angle ( β ), roughness ( α ), ratio of the internal embedment depth to the embedment width ( H / B ), ratio of the internal radius to the embedment width ( R / B ), and strength gradient ratio ( ρB / s u 0 ). In particular, when the H / B > R / B ratio, the N value tends to stabilize. Crucially, when H / B > 4, an increasing trend in R / B leads to a rise in N until R / B exceeds 10, i.e., large diameter caissons, stabilizing the N value. Furthermore, the results reveal the significant dependency of the cutting face roughness and strength gradient ratio of clay on N . The artificial neural network model, which is a soft computing-based model, is also developed to present the undrained bearing capacity forecasting equation. Compared with conventional regression, including the multiple linear regression model and the multiple nonlinear regression model, it has excellent performance, as measured by eight indices. In addition, ANOVA and Z-tests can support the research hypothesis and reject the null hypothesis. Open caissons are commonly used in the construction of various underground structures, such as launch and reception shafts for tunnel-boring machines, storage or attenuation tanks, and cofferdams. During the sinking phase, the cutting edge of a caisson wall with a cutting face encounters soil and is subjected to loading to facilitate and control the sinking process. In this regard, the bearing capacity factor (N) of the cutting face of a circular open caisson in heterogeneous clay is evaluated via finite element limit analysis, which accounts for the increase in the undrained shear strength with depth. The parameters considered in this study cover practical aspects, including the excavation geometry, soil strength profile, and caisson geometry. This investigation also explored the impacts of the cutting face angle (β), roughness (α), ratio of the internal embedment depth to the embedment width (H/B), ratio of the internal radius to the embedment width (R/B), and strength gradient ratio (ρB/su0). In particular, when the H/B > R/B ratio, the N value tends to stabilize. Crucially, when H/B > 4, an increasing trend in R/B leads to a rise in N until R/B exceeds 10, i.e., large diameter caissons, stabilizing the N value. Furthermore, the results reveal the significant dependency of the cutting face roughness and strength gradient ratio of clay on N. The artificial neural network model, which is a soft computing-based model, is also developed to present the undrained bearing capacity forecasting equation. Compared with conventional regression, including the multiple linear regression model and the multiple nonlinear regression model, it has excellent performance, as measured by eight indices. In addition, ANOVA and Z-tests can support the research hypothesis and reject the null hypothesis.
Author	Rattanadecho, Phadungsak Keawsawasvong, Suraparb Chavda, Jitesh T. Suppakul, Rungroad Jitchaijaroen, Wittaya
Author_xml	– sequence: 1 givenname: Rungroad surname: Suppakul fullname: Suppakul, Rungroad organization: Research Unit in Sciences and Innovative Technologies for Civil Engineering Infrastructures, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University – sequence: 2 givenname: Jitesh T. orcidid: 0000-0003-0396-5759 surname: Chavda fullname: Chavda, Jitesh T. organization: Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology – sequence: 3 givenname: Wittaya surname: Jitchaijaroen fullname: Jitchaijaroen, Wittaya organization: Research Unit in Sciences and Innovative Technologies for Civil Engineering Infrastructures, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University – sequence: 4 givenname: Suraparb orcidid: 0000-0002-1760-9838 surname: Keawsawasvong fullname: Keawsawasvong, Suraparb email: ksurapar@engr.tu.ac.th organization: Research Unit in Sciences and Innovative Technologies for Civil Engineering Infrastructures, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University – sequence: 5 givenname: Phadungsak orcidid: 0000-0002-9766-617X surname: Rattanadecho fullname: Rattanadecho, Phadungsak organization: Department of Mechanical Engineering, Center of Excellence in Electromagnetic Energy Utilization in Engineering (C.E.E.E.), Faculty of Engineering, Thammasat School of Engineering, Thammasat University
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Keywords	ANN FELA Cutting edge Cutting angle Heterogenous clay Open caisson
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SubjectTerms	Artificial neural networks Bearing capacity Boring machines Caissons Civil Engineering Clay Cofferdams Earth and Environmental Science Earth Sciences Excavation Finite element method Geotechnical Engineering & Applied Earth Sciences Hydrogeology Hypotheses Limit analysis Neural networks Null hypothesis Regression models Roughness Shear strength Sinking Soft computing Soil bearing capacity Soil investigations Soil strength Tanks Technical Note Terrestrial Pollution Tunnel construction Underground construction Underground structures Variance analysis Waste Management/Waste Technology
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Title	Soft Computing-Based Models for Estimating Undrained Bearing Capacity Factor of Open Caisson in Heterogeneous Clay
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