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Open Access

ARTICLE

Research on the Chloride Ion Penetration Resistance of Manufactured Sand Concrete Based on WOA-Adam Hybrid Optimized BPNN

Zhichao Liu1,2, Jun Zhang2,*, Dongling Yu3, Libing Jin1, Bingquan Song3
1 School of Civil Engineering, Henan University of Technology, Zhengzhou, China
2 School of Civil Engineering and Architecture, Ningbo Technology University, Ningbo, China
3 Ningbo Communications Engineering Construction Group Co., Ltd., Ningbo, China
* Corresponding Author: Jun Zhang. Email: email
(This article belongs to the Special Issue: Intelligent Monitoring and Life-Cycle Management for Enhancing Engineering Structural Durability and Safety)

Structural Durability & Health Monitoring https://doi.org/10.32604/sdhm.2026.081186

Received 25 February 2026; Accepted 15 April 2026; Published online 28 May 2026

Abstract

The chloride ion penetration resistance of manufactured sand concrete (MSC) critically determines the durability of marine concrete structures. However, its accurate prediction is challenging due to high uncertainty from complex influencing factors. To address this, a back-propagation neural network model optimized by a hybrid Whale Optimization Algorithm and Adaptive Moment Estimation strategy (WOA-Adam-BPNN) was developed to predict the electrical flux. The model was trained and tested on 245 experimental datasets covering eight key parameters and validated across four typical mix proportions. Results show that the WOA-Adam hybrid strategy effectively combines global search capability with adaptive convergence, significantly enhancing model performance. The proposed model achieved a mean absolute percentage error (MAPE) of 4.01%, a root mean square error (RMSE) of 59.57, and a coefficient of determination (R2) of 0.9879, significantly outperforming both the traditional Adam-BPNN and the genetic algorithm-optimized Adam-BPNN (GA-Adam-BPNN) models and the WOA-XGB hybrid model (WOA-XGB). Moreover, it maintains a stable prediction error within 5% across common engineering parameter ranges. This study provides a reliable reference for the mix proportion design and durability assessment of MSC.

Keywords

Manufactured sand; chloride ion penetration resistance; electric flux; machine learning; WOA-Adam-BPNN

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