Applications of Machine Learning to Predict the Flexural Bearing Capacity of Hollow Core Slabs After Fire Exposure

Conventional evaluation of the overall mechanical properties and ultimate flexural capacity of prestressed hollow core slabs after a fire exposure depends heavily on the inversion of fire scene temperature. To avoid this drawback, this paper presents a new methodology which combines a generalized regression neural network (GRNN) with conventional non-destructive testing technology. Thereby, a neural network model for predicting the material performance parameters after fire exposure is obtained based on conventional testing indices. A hollow core slab bridge is used as an example, and the applicability of the trained network model is confirmed using numerical simulation and a field failure test. Results show that the overall relative error of GRNN in predicting the key performance parameters of the bridge after fire exposure is less than 10%. Further, because of the good thermal inertia of the concrete, the relative error in predicting the material performance parameters of steel after a fire is less than 5%. Moreover, the ultimate flexural capacity of the prestressed hollow core slab after a fire can be accurately evaluated by feeding the material performance parameters predicted by GRNN neural network into the finite element (FE) model.

Automated summary

This paper presents a new methodology for predicting the material performance parameters of prestressed hollow core slabs after a fire. By combining a generalized regression neural network with conventional non-destructive testing technology, a neural network model is obtained based on conventional testing indices.