Prediction of pull-out behavior of timber glued-in glass fiber reinforced polymer and steel rods under various environmental conditions based on ANN and GEP models

Abstract

This study employs soft computing techniques, including artificial neural network (ANN) models and gene expression programming (GEP), to enhance the prediction of ultimate load in timber pull-out tests under varying environmental conditions. A comprehensive dataset of 202 samples in normal environmental conditions and 324 samples under harsh conditions was gathered. Distinct models were developed for each scenario, achieving commendable accuracies. The ANN performed at 0.91 under normal conditions and 0.99 under harsh conditions, while the GEP performed at 0.91 and 0.94, respectively. The study also predicted free end slip in timber pull-out tests using an ANN model with an accuracy of 0.97. The SHapley values technique was employed to assess the impact of features on the models, revealing specific influential features. In the prediction model for ultimate load under harsh conditions, the rod type was most influential, while under normal conditions, bonded length demonstrated the highest impact. Additionally, the duration of immersion feature had the most substantial effect on predicting free end slip. The final section compared predicted data with experimental values, showing a noteworthy correlation of over 60% between the predicted outputs and established models.

Keywords

• Artificial neural networks
• Cross validation
• SHapley values
• Timber construction
• GFRP and steel bar
• Pull-out strength