Abstract
The adaptive neuro-fuzzy inference systems (ANFIS) are widely used in the concrete technology. In this research, the compressive strength of light weight concrete was determined. To this end, the scoria percentage and curing day variables were used as the input parameters, and compressive strength and tensile strength were used as the output parameters. In addition, 100 patterns were used, 70% of which were used for training and 30% were used for testing. To assess the precision of the neuro-fuzzy system, it was compared using two linear regression models. The comparisons were carried out in the training and testing phases. Research results revealed that the neuro-fuzzy systems model offers more potential, flexibility, and precision than the statistical models.
摘要
目前,自适应神经模糊推理系统(ANFIS)在混凝土技术中得到了广泛的应用。本研究利用神经 模糊系统确定了轻量化混凝土的抗压强度。以废渣百分率和固化天数作为网络的输入参数,以抗压强 度和抗拉强度作为输出参数。实验选用了100 个模式,其中70%用于训练,30%用于测试。为了评估 神经模糊系统的精度,比较了神经模糊系统和统计模型(LR)两种线性回归模型的训练和测试阶段。结 果表明,神经模糊系统模型比统计模型具有更大的潜力、适应性性和精度。
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Razavi Tosee, S.V., Nikoo, M. Neuro-fuzzy systems in determining light weight concrete strength. J. Cent. South Univ. 26, 2906–2914 (2019). https://doi.org/10.1007/s11771-019-4223-3
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DOI: https://doi.org/10.1007/s11771-019-4223-3