The State of Art in Machine Learning Applications in Civil Engineering

Integrating experimental analysis and machine learning for assessing bond performance and corrosion severity in reinforced concrete structures

“…Many scholarly investigations have been conducted into the use of machine learning techniques within the domain of civil engineering. The findings of these investigations have been advantageous for researchers in the field of civil engineering since they enable prompt and efficient assessment of engineering issues [10]. In the field of civil engineering, the use of computer software, specifically using techniques like the finite element method, is often imperative for the computation of design parameters.…”

“…The use of machine learning enables the attainment of findings in a prompt and dependable manner. Various aspects of structural engineering are investigated through studies, encompassing confinement coefficients, compressive strength, carbonation, chloride diffusion, failure mode, lateral drifts, long-term deflections, behavior under seismic effects, flexural strength, axial capacity, structural damage, shear stress and plastic viscosity, optimum design, moment capacity, and ductility of diverse materials [10,11,12]. Researchers have used parametric and nonparametric machine learning techniques in civil/structural engineering applications for more than only damage identification.…”