本研究建立類神經網以探索類神經網路預測高性能混凝土坍流度的可行性，並以訓練過的類神經網路進行混凝土坍流度的計算模擬。用變化因子的組合，像水膠比、SP/ 膠結料比、用水量，達到變化混凝土坍流度的效果，產生了坍流度曲線，以探索水膠比、SP/ 膠結料比、用水量的作用。結果發現（1）以類神經網路預測混凝土坍流度很有潛力；（2）在水膠比分別為0.4和0.5下，每增加百分之一的SP/ 膠結料比，可節省的用水量約為15和10 kg /m?；（3）增加SP/ 膠結料比增加了坍流度，然而其效果在高水膠比時遠比低水膠比時來得小。

In this study, n artificial neural network was established to explore the feasibility f using neural networks in predicting the slump-flow of concrete. Computational simulation of concrete slump-flow was performed using the trained neural network. The variation in concrete slump-flow was achieved by varying combinations of factors like the water/binder ratio, SP-binder ratio, and water content. The slump-flow curves under various ratios were generated by the trained neural networks developed in this study to investigate the effects of water/binder ratio, SP-binder ratio, and water content. It was found that (1) the use of a neural network for the modeling of concrete slump-flow looks promising, (2) the water content saved by the use of SP is about 15 and 10 kg/m? for every percent of SP/b, at w/b = 0.4 and 1.5, respectively, and (3) an increasing SP/b ratio increased the slump-flow, while the effect was much smaller at high w/b ratio than that at low w/b ratio.