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

Abstract

AbstractConventional 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.Keywords: firehollow core slabmachine learningneuronic networkultimate bearing capacity Disclosure StatementNo potential conflict of interest was reported by the author(s).Data Availability StatementSome or all data, models, or codes generated or used during the study are available from the corresponding author by request.Additional informationFundingThis work was supported by the National Key Research and Development Program of China [grant number 2017YFE0103000]; Science and Technology Plan Project of Shandong Provincial Department of Transportation [grant number 2017B62]; Central Research Institutes of Basic Research and Public Service Special Operations [grant number 2021-9060a].