Systematic Mapping of Artificial Intelligence Applications in Finite-Element-Based Structural Engineering

This study systematically maps how artificial intelligence (AI) has been applied within finite-element (FE)-based structural engineering. A corpus of 5995 unique English-language publications was compiled and classified by discipline, with 3345 relevant papers further categorized by application group. A representative subset of 372 studies underwent detailed full-text classification across seven analytical dimensions covering AI methods, element formulations, materials, and structural objects. The analysis reveals rapid growth after 2015, including a pronounced expansion of surrogate modeling and data-driven prediction methods. The disciplinary composition of the literature has also evolved, with structural engineering studies becoming more prominent in recent years relative to earlier decades. Optimization & Design remains the largest application area across the full dataset, while Structural Performance Prediction and FEM Acceleration/Surrogate Modeling show the fastest growth, reflecting increasing emphasis on predictive, solver-efficient, and hybrid physics–data approaches. These findings indicate a maturing field in which AI is increasingly embedded across all stages of FE-based analysis and design. This study provides a structured overview of methodological patterns, identifies emerging hybrid strategies, and highlights opportunities for future research and industrial integration.