Work

Machine learning approach for predicting fire resistance of FRP ‐strengthened concrete beams

Year: 2024

Type: article

Abstract: This paper presents a data‐driven machine learning (ML)‐based approach for predicting the fire resistance of fiber‐reinforced polymer (FRP)‐strengthened reinforced concrete beams. To this end, a comprehensive database of fire tests on FRP‐strengthened concrete beams reported in literature was compiled. The database comprised of varying geometric and material properties, sectional dimensions, steel reinforcement, FRP layers, and thermal insulation, as well as the load level. The compiled database was analyzed using three different ML algorithms including, support vector regression, random forest regressor, and deep neural network. The outcome of the analysis is a computational‐based ML approach for predicting fire resistance of FRP‐strengthened concrete beams. The results predicted from the ML approach were compared with those measured in fire tests to show reliability of the proposed approach. The results infer that the proposed ML approach can effectively predict the fire resistance of FRP‐strengthened concrete beams with varying cross sections, properties of constituent materials, and applied loading.

Source: Structural Concrete

DOI: 10.1002/suco.202400466

Authors: P. P. Bhatt, N. Sharma, V. K. R. Kodur, M. Z. Naser

Institutions: Michigan State University, University of South Carolina, Clemson University

Language: English

Topic: Fire effects on concrete materials

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,710