ABSTRACT

This study presents the analytical models to predict the compressive strength of roller-compacted concrete pavement (RCCP) containing steel slag aggregate and fly ash. Based on the experimental results, three models were established in this study including multiple regression analysis (MRA), artificial neural networks (ANN) and fuzzy logic (FL). In the RCCP mixtures, cement was partially substituted by fly ash at four levels: 10%, 20%, 30%, and 40%; natural coarse aggregate was replaced by steel slag aggregate at ratio of 50% and 100%. The compressive strength was determined at 3-, 7-, 28-, 56- and 91-day ages. 75 sets of testing data were collected to build the target values set. With same seven input variables, the MRA model is less reliable than the ANN model in terms of predicting the compressive strength of RCCP. Besides, the use of triangular membership functions with three input variables (fly ash content, steel slag aggregate content and age) in the FL algorithm is sufficient to obtain accurate results. The performance of the FL model is as good as the ANN model. Additionally, a total of 33 fuzzy rules found for building the FL model can be applied to predict the compressive strength of RCCP.

Highlights

• MRA, ANN, and FL were used to construct the models for predicting the compressive strength of RCCP containing steel slag aggregate and fly ash.

• The ANN model and FL model created reliable results in predicting the strength of RCCP.

• The MRA model is less reliable than the ANN and FL models in terms of predicting of RCCP compressive strength.

• The best model is the FL model because of its friendly and efficiency.

摘要

本研究提出了预测含有钢渣骨料和粉煤灰的碾压混凝土路面 (RCCP) 的抗压强度的分析模型。根据实验结果，本研究建立了三种模型，包括多元回归分析（MRA）、人工神经网络（ANN）和模糊逻辑（FL）。在 RCCP 混合物中，水泥部分被粉煤灰替代，分为四个水平：10%、20%、30% 和 40%；天然粗骨料以50%和100%的比例被钢渣骨料替代。抗压强度在 3 天、7 天、28 天、56 天和 91 天时测定。收集了 75 组测试数据来构建目标值集。在相同的七个输入变量的情况下，MRA 模型在预测 RCCP 的抗压强度方面不如 ANN 模型可靠。除了，在 FL 算法中使用具有三个输入变量（粉煤灰含量、钢渣骨料含量和年龄）的三角隶属函数足以获得准确的结果。FL模型的性能与ANN模型一样好。此外，共有 33 条用于构建 FL 模型的模糊规则可用于预测 RCCP 的抗压强度。

强调

• 使用 MRA、ANN 和 FL 构建了预测含钢渣骨料和粉煤灰的 RCCP 抗压强度的模型。

• ANN 模型和 FL 模型在预测 RCCP 强度方面创造了可靠的结果。

• 在预测 RCCP 抗压强度方面，MRA 模型不如 ANN 和 FL 模型可靠。

• 最好的模型是 FL 模型，因为它友好且高效。