Soil is a non-homogeneous material with physical parameters exhibiting significant variability, which could be challenged to accurately predict ground surface settlement caused by underground construction based solely on sample parameters. To enhance the accuracy of predicting ground surface settlement induced by shield tunneling, a method was proposed for inverse analysis of key soil physical parameters in multi-layered soils using evolutionary neural networks. Parameter sensitivity analysis was firstly employed in the method to identify parameters that significantly influence surface settlement. Subsequently, samples were generated based on measured parameters from soil specimens. An artificial bee colony algorithm was then utilized to optimize artificial neural network parameters(artificial-bee-colony-based artificial neural network, ABC-ANN), creating an evolutionary neural network. This approach was compared with a genetic algorithm-optimized neural network (genetic-algorithm-based artificial neural network, GA-ANN) and a standard back-propagation neural network (back-propagation artificial neural network, BP-ANN) for predicting ground surface settlement in shield tunneling sites. The shield tunnel undercrossing of the existing Pinghe Road Station on Line 2 of the Suzhou Metro Line 6 was used as a case study. The computational results show that the compression modulus and internal friction angle are the key parameters influencing ground surface settlement. Measured surface displacements is used as input, the soil layer parameters are predicted by the trained neural networks. It is shown that ABC-ANN predicted parameters, when used as input for finite element model calculations, yield the smallest error in surface displacement predictions.