The use of custom tools can improve efficiency, especially when handling large structures with thousands of elements. One of the most useful tools I have recently developed is a strength based optimisation program that links with Arup’s in-house structural analysis software Oasys GSA through its API (COM interface).

The COM interface uses Visual Basic (VB) scripting and allows remote access to most of GSA’s functions, enabling the user to drive GSA externally through programs such as Excel. This means the user can automate calculation intensive processes and handle large amounts of data in an efficient manner.

While working on a multi-billion dollar project in Singapore, I was faced with the challenge of analysing and designing several large steel structures with highly complex geometric form and 5000 plus elements in each structural model. I initially developed this tool merely to deal with the sheer number of elements that were required to be analysed, designed and sized.

Later, I saw the potential for expanding its use by incorporating an optimisation routine. This was achieved by running a computer automated numerical algorithm to determine the least steel-weight of the structure while still satisfying the relevant design codes.

The optimisation algorithm will not attempt to modify the geometric form. Instead, through an iterative process, the sizes of the structural elements are continuously substituted (from a user selected pool of sections sizes), analysed, designed and resized based on the results of the analysis, until the structure with the least steel-weight is found.

It is also possible to calibrate the algorithm for different optimisation criteria. For example, an alternative criterion to the least steel-weight solution could be shallowest depth solution, least cost solution or fastest procurement solution, or a combination.

I have used this optimisation tool on two other projects. In these cases it was used for rapid design and member sizing of various scheme options and allowed the design team to quickly assess the most economical design options.

Not only was this an effective and highly efficient method of managing the data from large structural models, it also offered the opportunity to fine tune the steel weight of the structures, resulting in potential cost savings.