The compressive strength of concrete is a crucial parameter in the design and evaluation of building structures, as it directly impacts the quality and safety of buildings. To solve the current problem that the prediction of concrete compressive strength by the existing machine learning models suffers from issues such as long prediction times and low precision, which cannot meet the real-time and accuracy requirements of the prediction on construction sites, this paper proposed a novel method for predicting concrete compressive strength based on the tuna swarm optimization algorithm(TSO-ELM). The proposed method utilized TSO to optimize the offset value of the connection weight in the initial parameters of the ELM hidden layer, thereby enhancing the prediction accuracy of ELM. In the simulation experiment, it verified the prediction speed of ELM, the optimization ability of TSO, and the generalization of the TSO-ELM model using two sets of concrete data. The results demonstrate that the proposed method can significantly improve prediction speed and accuracy with fewer iterations and better generalization, providing a new approach for the timely prediction of concrete compressive strength in field construction.

[2023-08-03] Accepted Paper /doc/2023.05.0237/v1

[2024-02-05] Printed Article /doc/2023.05.0237/v2