Intelligent Prediction of the Frost Resistance of High-Performance Concrete: A Machine Learning Method

Abstract

Frost resistance in very cold areas is an important engineering issue for the durability of concrete, and the efficient and accurate prediction of the frost resistance of concrete is a crucial basis for determining reasonable design mix proportions. For a quick and accurate prediction of the frost resistance of concrete, a Bayesian optimization (BO)-random forest (RF) approach was used to establish a frost resistance prediction model that consists of three phases. A case study of a key national engineering project results show that (1) the RF can be used to effectively screen the factors that influence concrete frost resistance. (2) R2 of BO-RF for the training set and the test set are 0.967 and 0.959, respectively, which are better than those of the other algorithms. (3) Using the test data from the first section of the project for prediction, good results are obtained for the second section. The proposed BO-RF hybrid algorithm can accurately and quickly predict the frost resistance of concrete, and provide a reference basis for intelligent prediction of concrete durability.

Forecasting Mechanical Properties of Steel Structures Through Dynamic Metaheuristic Optimization for Adaptive Machine Learning

“…This process is excessively complicated and impractical for use in most projects. Machine learning (ML), the most successful branch of artificial intelligence (AI), is well suited to make structural engineering more predictable because of its ability to handle complex nonlinear structural systems under extreme actions (Salehi & Burgueño, 2018;Sun et al, 2021;Zhang et al, 2023). Thai (2022) reviewed 474 Scopus-indexed studies from 1989 to 2021 on ML applications in structural engineering, concluding that scholarly interest in these applications has been rising, especially over the last five years.…”