| 摘要: |
| 针对1 030组混凝土抗压强度试验数据，通过天牛须搜寻算法（BAS）来训练多层神经网络（MLP），并与混合复杂进化方法（SCE）-MLP、多元宇宙优化算法（MVO）-MLP这2种耦合模型算法进行对比分析，得到可用于预测混凝土抗压强度的算法模型.结果表明：BAS可以显著提高MLP的训练精度和预测精度，该算法比SCE-MLP、MVO-MLP耦合模型算法更快、更准确；与人工神经网络（ANN）和支持向量机（SVM）个体学习算法相比，元启发式算法在混凝土抗压强度预测方便表现出良好的优越性.同时讨论了BAS-MLP模型中与训练数据集数量和输入变量数量相关的因素，发现使用1 030组数据的80%即可获得良好的预测结果. |
| 关键词: 混凝土 抗压强度 耦合 预测 学习算法 训练数据集 |
| DOI：10.3969/j.issn.1007-9629.2023.07.002 |
| 分类号:TU528.1 |
| 基金项目:中国工程院咨询研究基金资助项目（2019-XZ-19） |
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| Prediction of Concrete Compressive Strength Based on Coupled BAS-MLP |
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WANG Shengrui1, HU Pan1, CHEN Sibao2, XIAO Yue3
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1.School of Civil Engineering and Architecture，Wuhan University of Technology，Wuhan 430070，China;2.Changjiang Survey Planning Design and Research，Co.，Ltd.，Wuhan 430010，China;3.Zhongnan Branch，Huajie Engineering Consulting Co.，Ltd.，Wuhan 430000，China
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| Abstract: |
| 1 030 sets of concrete compressive strength test data were used to train the multilayer perceptron（MLP） through beetle antennae search algorithm（BAS） and combined with shuffled complex evolution（SCE）， multi-verse optimizer（MVO） and the results of two MLP coupling algorithms were compared and analyzed， and an algorithm model that can be used to predict the compressive strength was obtained. The results show that BAS can significantly improve the training accuracy and prediction accuracy of MLP， and this model is faster， more accurate and stable than the other two coupled model algorithms. Compared with artifical neural network（ANN） and support vector machine（SVM） individual learning algorithms， the prediction of concrete compressive strength shows the superiority of meta-heuristic algorithm. The factors related to the number of training data sets and the number of input variables in the BAS-MLP model are discussed， and it is found that using 80% of the 1 030 sets of data can obtain good prediction results. |
| Key words: concrete compressive strength coupling prediction learning algorithm training data set |
