A Two-Stage Feature Reduction (FIRRE) Framework for Improving Artificial Neural Network Predictions in Civil Engineering Applications

Artificial neural networks (ANNs) are widely used in engineering prediction, but excessive input dimensionality can reduce both accuracy and efficiency. This study proposes a two-stage feature-reduction framework, Feature Importance Ranking and Redundancy Elimination (FIRRE), to optimize ANN inputs by removing weakly informative and redundant variables. In Stage 1, four complementary ranking methods, namely Pearson correlation, recursive feature elimination, random forest importance, and F-test scoring, are combined into an ensemble importance score. In Stage 2, highly collinear features (ρ > 0.95) are pruned while retaining the more informative variable in each pair. FIRRE is evaluated on 32 civil engineering datasets spanning materials, structural, and environmental applications, and benchmarked against Principal Component Analysis, variance-threshold filtering, random feature selection, and K-means clustering. Across the benchmark suite, FIRRE consistently achieves competitive or improved predictive performance while reducing input dimensionality by 40% on average and decreasing computation time by 10–60%. A dynamic modulus case study further demonstrates its practical value, improving R² from 0.926 to 0.966 while reducing inputs from 25 to 7. Overall, FIRRE provides a practical, robust framework for simplifying ANN inputs and improving efficiency in civil engineering prediction tasks.