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Empirical modeling of fresh and hardened properties of self-compacting concretes by genetic programming

机译:通过基因编程对自密实混凝土的新鲜和硬化性能进行经验建模

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This article introduces genetic programming (GP) as a new tool for the formulations of fresh and hardened properties of self-compacting concretes (SCC). There are no well known explicit formulations for predicting fresh and hardened properties of SCCs. Therefore, the objective of the paper presented herein is to develop robust formulations based on the experimental data and to verify the use of GP for generating the formulations for slump flow diameter, V-funnel flow time, compressive strength, ultrasonic pulse velocity and electrical resistivity of SCCs. To generate a database for the training and testing sets, a total of 44 SCC mixtures with and without mineral admixtures were cast at 0.32 and 0.44 water/binder ratios. The mineral admixtures used were fly ash, silica fume and granulated blast furnace slag. Of all 44 concrete mixtures, the training and testing sets consisted of randomly selected 28 and 16 mixtures, respectively. The paper showed that the GP based formulation appeared to well agree with the experimental data and found to be quite reliable, especially for hardened concrete properties.
机译:本文介绍了基因编程(GP)作为配制自密实混凝土(SCC)的新鲜和硬化特性的新工具。没有众所周知的明确公式可预测SCC的新鲜和硬化特性。因此,本文提出的目标是根据实验数据开发可靠的配方,并验证GP在坍落流直径,V漏斗流动时间,抗压强度,超声脉冲速度和电阻率生成配方中的应用SCC。为了生成用于训练和测试集的数据库,以水和粘合剂的比率分别为0.32和0.44浇铸了44种含矿物质和不含矿物质的SCC混合物。所使用的矿物掺合料是粉煤灰,硅粉和高炉矿渣颗粒。在所有44种混凝土混合物中,训练和测试集分别由随机选择的28种和16种混合物组成。该论文表明,基于GP的配方似乎与实验数据完全吻合,并且被认为是非常可靠的,特别是对于硬化混凝土而言。

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