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首页> 外文期刊>Civil and Environmental Research >Developing Artificial Neural Network and Multiple Linear Regression Models to Predict the Ultimate Load Carrying Capacity of Reactive Powder Concrete Columns
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Developing Artificial Neural Network and Multiple Linear Regression Models to Predict the Ultimate Load Carrying Capacity of Reactive Powder Concrete Columns

机译：开发人工神经网络和多元线性回归模型预测活性粉末混凝土柱的极限承载力

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摘要

The study focuses on development a model to predict the ultimate load carrying capacity of Reactive Powder Concrete (RPC) columns. Two different statistical methods regression techniques (RT) and the artificial neural network (ANN) methods were used for determining the RPC columns ultimate load carrying capacity. The data is collected from three experimental studies the first used to develop the model and the other two used as a case study. Experimental results used as input data to develop prediction models. Two different techniques adopted to develop the models the first was Artificial Neural Network (ANN) and the second was multi linear regression techniques (RT). The models use to predict the ultimate load carrying capacity of RPC columns. To predict the ultimate load carrying capacity of RPC columns four input parameters were identified cross-section, micro steel fiber volume fraction content, compressive strength and main steel reinforcement area. Both models build with assistance of MATLAB software. The results exhibit that the cross section area has most significant effect on ultimate load carrying capacity. The performance of ANNs with different architecture was considered to adopt the pest ANN. An ANN with one layer consist of 7 neurons provide the best prediction. The results of this investigation indicate that ANNs have strong potential as statistical method for prediction the ultimate load carrying capacity of RPC columns.
机译：该研究的重点是开发一种模型，以预测活性粉末混凝土（RPC）柱的极限承载力。两种不同的统计方法回归技术（RT）和人工神经网络（ANN）方法用于确定RPC柱的极限承载力。数据是从三项实验研究中收集的，第一项用于开发模型，另两项用于案例研究。实验结果用作输入数据以开发预测模型。采用两种不同的技术来开发模型，第一种是人工神经网络（ANN），第二种是多线性回归技术（RT）。该模型用于预测RPC色谱柱的最终承载能力。为了预测RPC柱的极限承载力，确定了四个输入参数：横截面，微钢纤维体积分数含量，抗压强度和主钢筋面积。两种模型均在MATLAB软件的帮助下构建。结果表明，横截面积对极限承载能力有最大的影响。人们认为具有不同架构的人工神经网络的性能采用了有害生物人工神经网络。一层由7个神经元组成的ANN提供最佳预测。研究结果表明，人工神经网络具有强大的潜力，可以作为预测RPC柱最终承载力的统计方法。

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