Structural Steel Reuse Analysis

Abstract

The Dutch government was mandated by the national court in May 2019 to address high nitrogen-oxide levels, threatening construction permits to be canceled if target values are not met. As the agrarian sector contributed significantly to nitrogen-oxide emissions, reductions in this sector were necessary. Ongoing protests by farmers indicate the unresolved crisis. A government report suggests that around 500-600 high-emission agricultural businesses must cease operations for a substantial impact. Consequently, large cattle farms, known as "mega barns," will become vacant. Traditionally demolished or recycled, their materials may have the potential for repurposing, especially given the projected availability of similar buildings in the next 10-20 years.

Both the European Union and the Dutch Government have set themselves targets to convert their economies from linear to circular before the year 2050. A circular economy means a system in which products and materials are kept within the loop as long as possible, reducing the need for new raw materials and production – reducing energy needs and CO2-emissions in the process. Recycling is currently coming up as a mainline strategy, however, it is considered to be less circular than other strategies, especially compared to reuse. The steel sector is seen as an exemplary industry that recycles a lot but would pose major environmental benefits when shifting the chain towards reuse. Yet, making this shift happen is withheld by certain barriers, especially on the designer’s side. The fact that information on availability, quality and quantity of reusable components is scarce in the critical early phases of the design process, is one of these main barriers.

This thesis attempts to introduce a tool that employs reverse-engineering techniques to analyze and predict the availability of structural steel components in industrial buildings, providing designers with knowledge about potentially available materials as early in the process as possible. By utilizing publicly available data, the tool enables an accurate estimation of the length, type, quantity and quality of the elements. This is done by the use of parametric design software such as Rhino3D, Grasshopper and Karamba3D. The research explores ways of making use of existing structures’ geometry and design requirements in order to predict the structural properties of the load-bearing components. The tool has been tested and evaluated on a series of cases, all of which are industrial farm halls situated in the Netherlands. This case testing has been used to improve and finetune the output results of the tool. In the end, the developed tool is able to predict steel profiles within a +/- 1 profile class range. Additional analyses are incorporated to assess cost savings, environmental benefits, and element quality.