<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper a finite-element analysis was carried out using Plaxis<sup>2D</sup> software to model a vertical cut reinforced by nails. Optimization of the effect of three input parameters on stability design is a key element of the analysis. We compare results obtained by three techniques of design optimization; Taguchi’s Design of Experiment (DOE), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The effect of three input factors on stability design was considered: nail length to wall height ratio (A), nail inclination (B) and vertical spacing (C). By altering the parameter variables, the design served to build and acquire a statistically significant mathematical model for optimizing soil nailing wall parameters. The aim is to minimize a single objective function of safety factor and identify the optimal parameters of design among all possibilities. DOE method and result analysis were carried out using MINITAB 18 software, while GA and PSO algorithm analysis were implemented by coding in MATLAB. According to the results, it was found that 9m of length, 2m of vertical spacing and 10° of inclination is the optimal combination minimizing safety factor. The results produced from this study show that all three techniques arrive at the same optimal combination of minimum response.</p>
</abstract>

In this paper a finite-element analysis was carried out using Plaxis2D software to model a vertical cut reinforced by nails. Optimization of the effect of three input parameters on stability design is a key element of the analysis. We compare results obtained by three techniques of design optimization; Taguchi’s Design of Experiment (DOE), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The effect of three input factors on stability design was considered: nail length to wall height ratio (A), nail inclination (B) and vertical spacing (C). By altering the parameter variables, the design served to build and acquire a statistically significant mathematical model for optimizing soil nailing wall parameters. The aim is to minimize a single objective function of safety factor and identify the optimal parameters of design among all possibilities. DOE method and result analysis were carried out using MINITAB 18 software, while GA and PSO algorithm analysis were implemented by coding in MATLAB. According to the results, it was found that 9m of length, 2m of vertical spacing and 10° of inclination is the optimal combination minimizing safety factor. The results produced from this study show that all three techniques arrive at the same optimal combination of minimum response.

Techniques for Optimizing Parameters of Soil Nailed Vertical Cut

Department of Civil Engineering, Faculty of sciences and applied sciences

School of Computing, Engineering and Physical Sciences

Department of Civil Engineering, Faculty of technology

Selected Scientific Papers - Journal of Civil Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Techniques for Optimizing Parameters of Soil Nailed Vertical Cut"}

In this paper a finite-element analysis was carried out using Plaxis2D software to model a vertical cut reinforced by nails. Optimization of the effect of...

Techniques for Optimizing Parameters of Soil Nailed Vertical Cut

Online veröffentlicht: 12. Juli 2021

Seitenbereich: 131 - 145

DOI: https://doi.org/10.1515/sspjce-2021-0010

Schlüsselwörter
optimization, Taguchi method, genetic algorithm, particle swarm optimization

© 2021 Fadila Benayoun et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Techniques for Optimizing Parameters of Soil Nailed Vertical Cut

Online veröffentlicht: 12. Juli 2021

Seitenbereich: 131 - 145

DOI: https://doi.org/10.1515/sspjce-2021-0010

Schlüsselwörteroptimization, Taguchi method, genetic algorithm, particle swarm optimization

© 2021 Fadila Benayoun et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
optimization, Taguchi method, genetic algorithm, particle swarm optimization