The result show that after the adjustment construction parameter deformation of the support in deep foundation pit tends to stably, achieves the deformation control the goal.
计算结果表明，调整施工参数后，基坑变形发展趋势趋于稳定，达到变形控制的目的。
The results show that: loess deep foundation pit have small deformation and small impact to the surrounding environment because the loess 'self-stability is very good.
结果表明：黄土深基坑由于黄土地层的自稳性很好，基坑变形较小，对周围的环境影响也较小。
The study shows that the method is feasible for deformation prediction in deep foundation pit engineering.
提出了深基坑变形预报的人工神经网络法，详细介绍了该方法的建模和应用实例。
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