用新型SatinBowerbird优化算法分析混凝土单轴抗压强度
Analyzing Uniaxial Compressive Strength of Concrete Using a Novel Satin Bowerbird Optimizer
摘要：
克服混凝土力学参数分析的复杂性需要选择合适的方法。本研究整合了一个人工神经网络（ANN）与一种新的元启发式技术，即缎纹园丁鸟优化（SBO）预测混凝土的单轴抗压强度（UCS）。为此，使用从已发表文献中收集的相对较大的数据集对所创建的混合进行训练和测试。另外三个新的算法，即亨利气体溶解度优化（HGSO），向日葵优化（SFO），涡搜索算法（VSA）也被用作基准。在所有算法都达到适当的种群规模后，利用各种精度指标，结果表明，所提出的ANN-SBO不仅可以出色地分析UCS行为，而且优于所有三种基准混合算法（即，ANN-HGSO、ANN-SFO和ANN-VSA）。在预测阶段，ANN-HGSO、ANN-SFO、ANN-VSA和ANN-SBO的相关指数分别为0.87394、0.87936、0.95329和0.95663，平均绝对百分比误差分别为15.9719、15.3845、9.4970和8.0629%，表现出最好的预测性能为所提出的模型。此外，ANN-VSA也取得了可靠的结果。总之，ANN-SBO可以作为一种有效的非破坏性的方法来预测混凝土的抗压强度。
英文摘要：
Surmounting the complexities in analyzing the mechanical parameters of concrete entails selecting an appropriate methodology. This study integrates an artificial neural network (ANN) with a novel metaheuristic technique, namely satin bowerbird optimizer (SBO) for predicting uniaxial compressive strength (UCS) of concrete. For this purpose, the created hybrid is trained and tested using a relatively large dataset collected from the published literature. Three other new algorithms, namely Henry gas solubility optimization (HGSO), sunflower optimization (SFO), and vortex search algorithm (VSA) are also used as benchmarks. After attaining a proper population size for all algorithms, Utilizing various accuracy indicators, it was shown that the proposed ANN-SBO not only can excellently analyze the UCS behavior, but also outperforms all three benchmark hybrids (i.e., ANN-HGSO, ANN-SFO, and ANN-VSA). In the prediction phase, the correlation indices of 0.87394, 0.87936, 0.95329, and 0.95663, as well as mean absolute percentage errors of 15.9719, 15.3845, 9.4970, and 8.0629%, calculated for the ANN-HGSO, ANN-SFO, ANN-VSA, and ANN-SBO, respectively, manifested the best prediction performance for the proposed model. Also, the ANN-VSA achieved reliable results as well. In short, the ANN-SBO can be used by engineers as an efficient non-destructive method for predicting the UCS of concrete.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
