Year: 2026
Type: article
Abstract: A prediction method based on machine learning neural network model is proposed to address the problem of difficult prediction of concrete strength due to the interaction of multiple factors Historical data containing various influencing factors and corresponding strength values are collected and preprocessed, key features are extracted as input variables. A BP neural network is used to construct a model to handle complex nonlinear problems, the network structure is adjusted to adapt to the task, and weights and thresholds are optimized through forward and backward propagation algorithms. Use the trained model for a new task and input the predicted values of the new data. The experiment shows that the model performs well in predicting the compressive strength of concrete at 7 and 28 days, with an R² of 0.98 at 28 days and 0.95 at 7 days, demonstrating high accuracy and practicality.
Source: Discover Computing
Author: Dali Li
Institution: Shanghai Normal University
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,520
OpenAlex ID: W7133545015