This document discusses the need for a larger, different kind of "box" or framework that can empower technology and enable new knowledge and skills. It explores concepts from graph theory, network theory, and complex systems that could provide a starting point for developing robust and optimized structural engineering designs and strategies. Key areas discussed include distributions versus dimensioning of components, alternate load paths, and optimization approaches that can accommodate multiple complex load cases. The goal is to study how these network-based approaches could enable previously unimaginable applications and outcomes.

1. Wanted: a larger, different kind of box
Lina M Achi 1, Gunnar Tibert 2, Mikael Hallgren 3
1 Tyréns AB and Royal Institute of Technology (KTH),
2 KTH, 3 Tyréns AB and KTH

4. “A tool allows one to do things impossible to accomplish with one's body alone”
Bloomfield M. Mankind in Transition: A View of the Distant Past, The Present, and the Far Future

5. x

6. the power of Engineering

7. the Art of structural engineering
the engineering of tools of discovery
Ex

9. >> >>

10. AI
bigger “box”, empowering technology
new knowledge and skills

13. EAI = BI

14. EAI = BI

15. “In practice a great many problems are solved by what is called
judgment. The better a man understands how the stresses follow
through a member or structure, the better his judgment will be.”
Wolfe, W. S., Graphical Analysis: A Text Book on Graphic Statics

16. “.. abstract form of a system, focusing
on the interrelationships among the objects,
and ignoring any features of them that do
not affect how they relate to other objects in
the system.”
Shapiro, S., Structure and Ontology

17. graph theory

18. “Graph theory gives us a language for networks. It allows us to define
networks exactly and to quantify network properties at all different levels.
This quantification is likely to improve further since new graph measures
are described regularly. “
Connected Brains: a website on complex brain network research

19. Node degree, degree distribution and assortativity
The degree of a node is the number of connections that link it to the rest of the network — this is
the most fundamental network measure and most other measures are ultimately linked to node degree. The degrees of all the network's nodes form a degree distribution. Assortativity is the
correlation between the degrees of connected nodes.
Clustering coefficient and motifs
If the nearest neighbours of a node are also directly connected to each other they form a cluster. The clustering coefficient quantifies the number of connections that exist between the nearest
neighbours of a node as a proportion of the maximum number of possible connections. Random networks have low average clustering whereas complex networks have high clustering
(associated with high local efficiency of information transfer and robustness). Interactions between neighbouring nodes can also be quantified by counting the occurrence of small motifs of
interconnected nodes. The distribution of different motif classes in a network provides information about
the types of local interactions that the network can support.
Path length and efficiency
Path length is the minimum number of edges that must be traversed to go from one node to another. Random and complex networks have short mean path lengths (high global efficiency of
parallel information transfer) whereas regular lattices have long mean path lengths.
Hubs, centrality and robustness
Hubs are nodes with high degree, or high centrality. The centrality of a node measures how many of the shortest paths between all other node pairs in the network pass through it. A node with
high centrality is thus crucial to efficient communication. The importance of an individual node to network efficiency can be assessed by deleting it and estimating the efficiency of the 'lesioned'
network. Robustness refers either to the structural integrity of the network following deletion of
nodes or edges or to the effects of perturbations on local or global network states.
Modularity
Many complex networks consist of a number of modules. Each module contains several densely interconnected nodes, and there are relatively few connections between nodes in different
modules. Hubs can therefore be described in terms of their roles in this community structure. Provincial hubs are connected mainly to nodes in their own modules, whereas connector hubs are
connected to nodes in other modules.

21. https://en.wikipedia.org/wiki/Network_theory

26. - STUDY THE CONSEQUENCES OF THE DISTRIBUTION
VERSUS THE DIMENSIONING OF COMPONENTS?
- STARTING-POINT FOR THE DEVELOPMENT OF
ROBUST, AS WELL AS OPTIMISED STRUCTURES?
- DEVELOPING A DESIGN STRATEGY
OF ALTERNATE LOAD PATHS?
- ENABLING OPTIMISATION STRATEGY
ACCOMODATING FOR MULTIPLE,
COMPLEX LOAD CASES?

27. “.. as we find new ways to use computers, they won’t just
get better at the kind of things people already do; they’ll
help us to do what was previously unimaginable.”
Thiel P., Zero to One – Notes on Startups, or, How to Build the Future

28. E Xvs

29. x ICT
INDUSTRY

30. ureka!