Accurate prediction of the liquefaction-induced settlement ( $${S}_{\mathrm{lc}}$$ ) is an essential requirement for a good design of buildings resting on liquefiable ground and subjected to seismic shake. However, prediction of the $${S}_{\mathrm{lc}}$$ is not straightforward process and it requires advanced soil models and calibrated soil parameters that are not readily available for designers/practitioners. In addition, the available empirical models to estimate the $${S}_{\mathrm{lc}}$$ have been developed using either classical regression analysis or multivariate adaptive regression splines and such techniques produce complicated models. Also, these empirical models have been developed utilizing results of numerical modelling. To overcome these limitations, novel model has been developed in this paper utilizing robust regression analysis driven by artificial intelligence called the evolutionary polynomial regression analysis. The new model has been developed using centrifuge results (real laboratory measurements) and can be easily used to accurately estimate the liquefaction induced settlement. The developed model scored a mean absolute error, root mean square error, mean, standard deviation of the predicted to measured values, coefficient of determination, $$a20 - \mathrm{index}$$ , and EPR coefficient of determination of 2.12 cm, 2.84 cm, 1.06, 0.19, 0.98, 0.77, and 97%, respectively, for the learning data and 1.73 cm, 3.31 cm, 0.99, 0.17, 0.97, 0.75, and 97%, respectively, for the examination data. The developed model has also been used in a parametric study to provide an insight into the sensitivity of the $${S}_{\mathrm{lc}}$$ to the foundation width, building height, pressure applied on the foundation, thickness and relative density of the liquefiable layer, and earthquake intensity. The results obtained from the parametric study are reasonable and in agreement with previous studies in the literature. Thus, the developed model can be employed to optimize designs and to reduce design costs as it does not require complicated analyses and/or expensive computational facilities.

中文翻译：

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EPR-MOGA在地震作用下建筑物液化沉降计算中的应用

准确预测液化引起的沉降 ($${S}_{\mathrm{lc}}$$) 是对位于可液化地面上并受到地震震动的建筑物进行良好设计的基本要求。然而，$${S}_{\mathrm{lc}}$$ 的预测并不是一个简单的过程，它需要先进的土壤模型和校准的土壤参数，而设计人员/从业者并不容易获得这些参数。此外，已经使用经典回归分析或多元自适应回归样条开发了可用的经验模型来估计 $${S}_{\mathrm{lc}}$$，并且此类技术会产生复杂的模型。此外，这些经验模型是利用数值建模的结果开发的。为了克服这些限制，本文利用称为进化多项式回归分析的人工智能驱动的稳健回归分析开发了新模型。新模型是使用离心机结果（真实实验室测量）开发的，可轻松用于准确估计液化引起的沉降。开发的模型对平均绝对误差、均方根误差、平均值、预测值与测量值的标准偏差、决定系数、$$a20 - \mathrm{index}$$ 和 2.12 cm 的 EPR 决定系数进行评分，学习数据分别为 2.84 cm、1.06、0.19、0.98、0.77 和 97%，考试数据分别为 1.73 cm、3.31 cm、0.99、0.17、0.97、0.75 和 97%。开发的模型还用于参数研究，以深入了解 $${S}_{\mathrm{lc}}$$ 对基础宽度、建筑高度、施加在基础上的压力、厚度的敏感性可液化层的相对密度和地震烈度。从参数研究中获得的结果是合理的，并且与文献中的先前研究一致。因此，开发的模型可用于优化设计并降低设计成本，因为它不需要复杂的分析和/或昂贵的计算设施。从参数研究中获得的结果是合理的，并且与文献中的先前研究一致。因此，开发的模型可用于优化设计并降低设计成本，因为它不需要复杂的分析和/或昂贵的计算设施。从参数研究中获得的结果是合理的，并且与文献中的先前研究一致。因此，开发的模型可用于优化设计并降低设计成本，因为它不需要复杂的分析和/或昂贵的计算设施。