Faculty of Civil Engineering, University of Zagreb, 10000 Zagreb, Croatia.
Faculty of Civil Engineering and Geosciences, TU Delft, 2628 CN Delft, The Netherlands.
Sensors (Basel). 2022 Apr 9;22(8):2888. doi: 10.3390/s22082888.
To identify the unknown values of the parameters of Burger's constitutive law, commonly used for the evaluation of the creep behavior of the soft soils, this paper demonstrates a procedure relying on the data obtained from multiple sensors, where each sensor is used to its best advantage. The geophysical, geotechnical, and unmanned aerial vehicle data are used for the development of a numerical model whose results feed into the custom-architecture neural network, which then provides information about on the complex relationships between the creep characteristics and soil displacements. By utilizing InSAR and GPS monitoring data, particle swarm algorithm identifies the most probable set of Burger's creep parameters, eventually providing a reliable estimation of the long-term behavior of soft soils. The validation of methodology is conducted for the Oostmolendijk embankment in the Netherlands, constructed on the soft clay and peat layers. The validation results show that the application of the proposed methodology, which relies on multisensor data, can overcome the high cost and long duration issues of laboratory tests for the determination of the creep parameters and can provide reliable estimates of the long-term behavior of geotechnical structures constructed on soft soils.
为了确定 Burger 本构定律参数的未知值，该文提出了一种依赖于多传感器数据的程序，该定律常用于评估软土的蠕变行为，其中每个传感器都被充分利用。利用地球物理、岩土和无人机数据，开发了一个数值模型，其结果被输入到定制架构神经网络中，该网络提供关于蠕变特性和土壤位移之间复杂关系的信息。通过利用 InSAR 和 GPS 监测数据，粒子群算法确定了 Burger 蠕变参数的最可能集合，最终对软土的长期行为进行可靠估计。该方法在荷兰 Oostmolendijk 堤坝上进行了验证，该堤坝建在软黏土和泥炭层上。验证结果表明，该方法依赖于多传感器数据，能够克服实验室测试确定蠕变参数的高成本和长周期问题，并能对软土上建造的岩土结构的长期行为进行可靠估计。
