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首页> 美国卫生研究院文献>Materials >Modeling of Compressive Strength for Self-Consolidating High-Strength Concrete Incorporating Palm Oil Fuel Ash
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Modeling of Compressive Strength for Self-Consolidating High-Strength Concrete Incorporating Palm Oil Fuel Ash

机译：含棕榈油燃料灰的自固结高强混凝土抗压强度建模

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摘要

Modeling is a very useful method for the performance prediction of concrete. Most of the models available in literature are related to the compressive strength because it is a major mechanical property used in concrete design. Many attempts were taken to develop suitable mathematical models for the prediction of compressive strength of different concretes, but not for self-consolidating high-strength concrete (SCHSC) containing palm oil fuel ash (POFA). The present study has used artificial neural networks (ANN) to predict the compressive strength of SCHSC incorporating POFA. The ANN model has been developed and validated in this research using the mix proportioning and experimental strength data of 20 different SCHSC mixes. Seventy percent (70%) of the data were used to carry out the training of the ANN model. The remaining 30% of the data were used for testing the model. The training of the ANN model was stopped when the root mean square error (RMSE) and the percentage of good patterns was 0.001 and ≈100%, respectively. The predicted compressive strength values obtained from the trained ANN model were much closer to the experimental values of compressive strength. The coefficient of determination (R2) for the relationship between the predicted and experimental compressive strengths was 0.9486, which shows the higher degree of accuracy of the network pattern. Furthermore, the predicted compressive strength was found very close to the experimental compressive strength during the testing process of the ANN model. The absolute and percentage relative errors in the testing process were significantly low with a mean value of 1.74 MPa and 3.13%, respectively, which indicated that the compressive strength of SCHSC including POFA can be efficiently predicted by the ANN.
机译：建模是预测混凝土性能的非常有用的方法。文献中可用的大多数模型都与抗压强度有关，因为它是混凝土设计中使用的主要机械性能。已经进行了许多尝试来开发合适的数学模型，以预测不同混凝土的抗压强度，而不是为包含棕榈油燃料灰分（POFA）的自固结高强度混凝土（SCHSC）做准备。本研究已使用人工神经网络（ANN）来预测结合POFA的SCHSC的抗压强度。使用20种不同SCHSC混合物的混合物比例和实验强度数据，已开发并验证了ANN模型。 70％（70％）的数据用于进行ANN模型的训练。其余30％的数据用于测试模型。当均方根误差（RMSE）和良好模式的百分比分别为0.001和≈100％时，就停止了ANN模型的训练。从训练的ANN模型获得的预测抗压强度值更接近抗压强度的实验值。预测抗压强度与实验抗压强度之间的关系的确定系数（R 2 ）为0.9486，表明网络图形的准确性更高。此外，在ANN模型的测试过程中发现预测的抗压强度非常接近实验抗压强度。测试过程中的绝对和相对误差相对较低，平均值分别为1.74 MPa和3.13％，这表明ANN可以有效预测包括POFA在内的SCHSC的抗压强度。

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