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Automated optimization of retaining walls for excavation pit

For the design of retaining walls, we define a large number of design variables in such a way that the stability and serviceability for the purpose are guaranteed.

In addition, our goal is to optimize the retaining wall for our client in terms of construction time and costs.

Through judicious selection of the structural elements and arranging them in the most efficient way, a lot of time and money can be saved in the course of the project. If, for example, the loads to be carried on by one anchor layer can be reduced, fewer anchors may be required – which will, understandably, have a positive effect on construction costs. If the number of anchors can be reduced, this means not only cost savings but also a shortening of the construction time.

We use mathematical optimization algorithms to facilitate the decision-making process and optimize costs, and these algorithms are linked to a standard statics software program. We create a cost func­tion for each project individually that estimates the costs per linear meter of retaining wall. These costs include, among other factors, the material and manufacturing costs for the individual components of the construction.

Read more about the topic here.

We make use of the automated optimization of excavation pit retaining walls in our standard excavation pit projects and additionally offer this as an in-house service. The basis for this in-house service is often a computer-generated section (not yet optimized) through an excavation pit retaining wall.

Feel free to contact us: laurent[punkt]pitteloud [ät] gruner[punkt]ch or joerg[punkt]meier [ät] gruner[punkt]ch

 


Figure: Site influence of the lowest anchor layer on the cutting variables and anchor forces – judicious selection and design of the excavation pit retaining wall can save a lot of money.

 


Figure: Schematic system section with the combinations examined by the optimization algorithm

 


Figure: We apply a variety of optimization algorithms. Particle swarm optimization (PSO) is just one of the methods employed: PSO is applied here to a cost function F with two variables (X1 and X2)

 


Figure: A particle swarm optimizer in progress (in the diagram below: costs over optimization progress)