Theoretical Analysis and Development of an Artificial Neural Network Model to Evaluate Earthen Dam Slope Stability

Q3 Environmental Science Tikrit Journal of Engineering Sciences Pub Date : 2022-11-05 DOI:10.25130/tjes.29.4.1
R. Hussain, Asmaa Al-samarrae
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Abstract

In the design of earth dams, it must be considered that the water leakage through the earth dam generates upward and pore pressure, in addition to leakage forces that cause internal erosion, which has a direct influence on the structural stability of this system. Also, the rising and dropping in the water level has a direct effect on the stability of the dam's face slope. One way to solve these issues is the installation of a core or a horizontal water drainage system. The present study relied on the GEO-Studio computer tool to evaluate cross-sectional models of earthen dams by determining the safety factor under different situations represented by a change in filter type, and the flow state as a result of raising and lowering the water level at the dam reservoir and the full fill condition of the dam reservoir. The research found that the existence of a core substantially contributed to improving the safety coefficient for the case of rising the water level (2m) and rapidly rising by assigning it the greatest safety coefficient values. The absence of a filter had an opposite influence on the safety coefficient by decreasing it. Also, the factor of safety for the downstream slope was affected by less than 5% for different flow conditions, compared with the higher effect generated by the upstream slope. Furthermore, an artificial neural network model with an accuracy ratio of more than 97% was developed for the predicted safety factor.
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土坝边坡稳定性评价人工神经网络模型的理论分析与发展
在土坝设计中,必须考虑渗水通过土坝产生的向上和孔隙压力,以及引起内部侵蚀的渗水力,这直接影响到土坝体系的结构稳定性。同时,水位的升降对坝面边坡的稳定性有直接的影响。解决这些问题的一种方法是安装核心或水平排水系统。本研究利用GEO-Studio计算机工具对土坝横截面模型进行了评价,确定了不同情况下的安全系数,包括过滤器类型的变化、坝库水位的高低和坝库满蓄条件下的流量状态。研究发现,在水位上升(2m)和快速上升的情况下,堆芯的存在极大地提高了安全系数,并赋予其最大的安全系数值。没有过滤器对安全系数有相反的影响,因为它降低了。不同流量条件对下游边坡安全系数的影响小于5%,而上游边坡的影响较大。建立了预测安全系数的人工神经网络模型,准确率达97%以上。
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CiteScore
1.50
自引率
0.00%
发文量
56
审稿时长
8 weeks
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