Huang Xiaoyu, Wang Shuai, Lu Tong, Li Houmin, Wu Keyang, Deng Weichao
School of Civil Engineering, Architecture and The Environment, Hubei University of Technology, Wuhan 430068, China.
Wuhan Construction Engineering Company Limited, Wuhan 430056, China.
Polymers (Basel). 2023 Jan 7;15(2):308. doi: 10.3390/polym15020308.
The addition of rubber to concrete improves resistance to chloride ion attacks. Therefore, rapidly determining the chloride permeability coefficient () of rubber concrete (RC) can contribute to promotion in coastal areas. Most current methods for determining of RC are traditional, which cannot account for multi-factorial effects and suffer from low prediction accuracy. Machine learning (ML) techniques have good non-linear learning capabilities and can consider the effects of multiple factors compared with traditional methods. However, ML models easily fall into the local optimum due to their parameters' influence. Therefore, a mixed whale optimization algorithm (MWOA) was developed in this paper to optimize ML models. The main strategies are to introduce Tent mapping to expand the search range of the algorithm, to use an adaptive -distribution dimension-by-dimensional variation strategy to perturb the optimal fitness individual to thereby improve the algorithm's ability to jump out of the local optimum, and to introduce adaptive weights and adaptive probability threshold values to enhance the adaptive capacity of the algorithm. For this purpose, data were collected from the published literature. Three machine learning models, Extreme Learning Machine (ELM), Random Forest (RF), and Elman Neural Network (ELMAN), were built to predict the of RC, and the three models were optimized using MWOA. The calculations show that the MWOA is effective with the optimized ELM, RF, and ELMAN models improving the prediction accuracy by 54.4%, 62.9%, and 36.4% compared with the initial model. The MWOA-ELM model was found to be the optimal model after a comparative analysis. The accuracy of the multiple linear regression model (MRL) and the traditional mathematical model is calculated to be 87.15% and 85.03%, which is lower than that of the MWOA-ELM model. This indicates that the ML model that is optimized using the improved whale optimization algorithm has better predictive ability than traditional models, providing a new option for predicting the of RC.
在混凝土中添加橡胶可提高其抗氯离子侵蚀能力。因此，快速测定橡胶混凝土（RC）的氯离子渗透系数（ ）有助于在沿海地区推广应用。目前大多数测定RC的 的方法较为传统，无法考虑多因素影响且预测精度较低。机器学习（ML）技术具有良好的非线性学习能力，与传统方法相比能够考虑多个因素的影响。然而，ML模型容易因其参数的影响而陷入局部最优。因此，本文开发了一种混合鲸鱼优化算法（MWOA）来优化ML模型。主要策略包括引入帐篷映射以扩大算法的搜索范围，采用自适应 -分布逐维变异策略对最优适应度个体进行扰动，从而提高算法跳出局部最优的能力，以及引入自适应权重和自适应概率阈值以增强算法的自适应能力。为此，从已发表的文献中收集了数据。构建了三种机器学习模型，即极限学习机（ELM）、随机森林（RF）和埃尔曼神经网络（ELMAN）来预测RC的 ，并使用MWOA对这三种模型进行了优化。计算结果表明，MWOA是有效的，优化后的ELM、RF和ELMAN模型的预测精度分别比初始模型提高了54.4%、62.9%和36.4%。经过对比分析，发现MWOA-ELM模型是最优模型。计算得出多元线性回归模型（MRL）和传统数学模型的准确率分别为87.15%和85.03%，均低于MWOA-ELM模型。这表明使用改进的鲸鱼优化算法优化后的ML模型比传统模型具有更好的预测能力，为预测RC的 提供了一种新的选择。
