Field sampling for evaluation of mechanical behaviour in embankment dams is not easily performed, because the performance and the safety of the structure may be unfavourably affected. A non-destructive method, inverse analysis, is an alternative. In this study, inverse analysis has been utilised to identify values of soil parameters for an embankment dam. An objective function and a genetic search algorithm were combined with finite-element software to perform the analysis. Values of model parameters were calibrated until inclinometer deformations from monitoring and computations corresponded to each other. Errors in field measurements occur, related to – for example – measurement precision, as well as handling and installation of the equipment. Search algorithms in mathematical optimisation might incur numerical problems if they are used against data containing errors. The performance of the genetic algorithm was investigated for the dam studied, when identification was performed against inclinometer data containing known errors of different magnitudes. The results showed that the genetic algorithm can search for solutions without obtaining numerical problems, even though the field data are substantially perturbed. It was found that the genetic algorithm is able to find good solutions for data from field measurements, including the usual errors in practical dam applications.

中文翻译：

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利用嘈杂场数据识别堤坝参数

评估路堤大坝机械性能的现场采样并不容易，因为可能会不利地影响结构的性能和安全性。一种非破坏性方法，逆分析是一种替代方法。在这项研究中，逆分析已被用来识别堤坝的土壤参数值。将目标函数和遗传搜索算法与有限元软件结合起来进行分析。校准模型参数的值，直到监测和计算产生的测斜仪变形相互对应为止。发生现场测量错误，例如，与测量精度以及设备的搬运和安装有关。如果将数学优化中的搜索算法用于包含错误的数据，则可能会引起数值问题。当针对包含不同幅度的已知误差的测斜仪数据进行识别时，针对所研究的大坝研究了遗传算法的性能。结果表明，即使现场数据受到很大干扰，该遗传算法也可以在不获取数值问题的情况下搜索解。人们发现，遗传算法能够为现场测量的数据找到良好的解决方案，包括实际大坝应用中的常见错误。结果表明，即使现场数据受到很大干扰，该遗传算法也可以在不获取数值问题的情况下搜索解。人们发现，遗传算法能够为现场测量的数据找到良好的解决方案，包括实际大坝应用中的常见错误。结果表明，即使现场数据受到很大干扰，该遗传算法也可以在不获取数值问题的情况下搜索解。人们发现，遗传算法能够为现场测量的数据找到良好的解决方案，包括实际大坝应用中的常见错误。