Rathish Kumar Pancharathi

Abstract This study examines the mechanical and flexural fatigue behaviour of concrete with reclaimed asphalt pavement (RAP) aggregates with and without steel fibre reinforcement. The use of RAP as partial replacement for natural aggregates represents a sustainable solution for reduction of construction waste and saving of natural resources. Twenty concrete mixes were prepared with RAP replacement levels varying between 0 and 30% and steel fibre contents varying between 0 and 1.25% by volume.

CONFLICT OF INTEREST STATEMENT The authors have no competing interests to declare. DATA AVAILABILITY STATEMENT Data sharing not applicable to this article as no datasets were generated or analyzed during the current study. Supporting Information Filename Description cote70049-sup-0001-Supinfo.docxWord 2007 document , 6 MB Data S1. Supporting Information. REFERENCES 1, , . Flexural strength of corroded reinforced concrete beams. Mag Concr Res. 2010; 62(6): 405-414. 2.

ABSTRACT The incorporation of machine learning techniques with structural health monitoring has demonstrated a significant performance improvement, attributed to the ability of machine learning to effectively handle large datasets. This work primarily concentrates on developing machine learning models that classify different damage types of a three-dimensional frame. Dynamic analysis and classification methods are used to explore a total of eight distinct structural states.