混凝土强度预测和模拟的智能化方法

早期获得混凝土28d抗压强度值,对于提高工程质量和加快施工进度具有十分重要的意义。本文在国内外早期推定混凝土强度方法的基础上,提出一种基于人工智能的新的预测办法,建立了多层前馈神经网络模型,实现了从新拌混凝土的组份及其特性到硬化后混凝土强度之间的复杂的非线性映射。通过对大量试验数据的学习,智能系统可以早期预测混凝土28d抗压强度。此外,本文还应用该智能系统模拟分析混凝土成份的质和量的变化对其抗压强度的影响,得到的结果符合已知的混凝土强度变化的某些规律,表明系统具有较高的精度和较强的泛化能力。

ARTIFICIAL INTELLIGENCE METHOD FOR PREDICTION AND SIMULATION OF CONCRETE STRENGTH

It has an importnmt significance to improve the quality of engineeing project and to speed up the progress of construction, that the 28-day compression strength value of concrete is realized earlier than planned time. In the paper, on the basis of the methods for prediction of concrete strength at home and abroad in the past decades, hence, a new predication approach based on artificial intelligence is suggested, and establishing the multi-layer feed-forward neural network models to implement the complex non-linear mapping from the grading and characteristic values of fresh concrete to the strength of hardened concrete. Through the study for the plenty experimental data of concrete, that the 28-day compression strength of concrete can be predicted at earlier time by the intelligent system. Furthermore, the system is also used for simulated analyses of the effects of the variations for quality and quantity of concret components on the compression strength of concrete, and the gainable results correspond to some known regularity for the variation of concrete strength, thus the high accuracy and strong generalization ability of system are shown.