Hybrid Fire Testing: Past, Present and Future

Abstract

The world’s present and future challenges are impacted by the climate change, more and more limited access to resources along with the increase and aging of population. The engineering world will adapt to these challenges by developing new materials, new type of structural solutions, or new fire-based evaluation tools. The new developed materials or structural elements must be tested in order to validate and calibrate the existing or new fire-based evaluation tools, and hybrid fire testing is a new emerging promising technique. Generally, the fire tests are performed on single members, under constant mechanical boundary conditions, neglecting the action of the remainder structure. However, full-scale testing of full structural systems revealed different behaviors of the structural members, but the high cost makes the practice uncommon. Hybrid fire testing is keeping the advantage of testing only parts of the structure, but at the same time considering the effect of the remainder structure, thus reproducing the results of the full-scale testing. In the context of new emerging fire-based evaluation tools, i.e., machine learning has the potential to facilitate performance-based fire design of structures. This chapter conceptually describes the overlap of the hybrid fire testing methodology and the new fire-based evaluation tool, machine learning. A detailed description of hybrid fire testing method is presented along with a benchmark case study for exemplification purposes.