## 1 Introduction  
Numerous monitoring data can be obtained from earth-rockfill dams [1, 2] due to the development of monitoring technology. How to deal with massive amounts of monitoring data has recently become a hot research issue of high rock-fill dams. Generally, the monitoring data can be processed and analyzed for displacement predictions using data fitting methods [3, 4]. [PERSON] et al. [3] proposed a monitoring model of dam safety with the random forest intelligent algorithm. [PERSON] et al. [4] presented the most successfully designed predictive mathematical models and discussed the advantages of using a mathematical model with a separate introduction of the main effective factors into the model. Although monitoring data can be simulated well with data fitting methods, mathematical models without the theoretical basis of mechanics may still be unconvincing. On the other hand, inversion analysis [5, 6] can be carried using processed monitoring data in order to get more accurate calculation parameters. Traditional inversion methods usually consist of optimization methods and forward analysis. Moreover, inversion analysis has been improved with the introduction of artificial intelligence technology. There are many kinds of artificial intelligence methods, such as the bat algorithm [7], the genetic algorithm (GA) [8], the particle swarm optimization (PSO) algorithm [9], and the neural network algorithm [10; 11]. The neural network algorithm [12; 13] has become a relatively popular algorithm with the advantages of separating forward and inversion analysis steps using a training strategy and obtaining multiple parameters quickly after training. The back propagation neural network [14] (BP neural network), as a member of the neural network methods, has been widely applied to deal with inversion analysis problems [15; 16] and can also be adopted in this paper. Material parameters closer to engineering practice can be obtained using traditional inversion methods, but researchers might find that it is still difficult to obtain calculated results (displayed, stress and so on) close enough to the monitoring data with the inversed material parameters due to the complexity of practical engineering, especially in projects during construction periods. As we all know, the finite element method based on the nonlinear elastic or elastic-plastic constitutive model is often applied in the forward analysis [17] of earth-rockfill dams. The Duncan-Chang constitutive model [18], cam-clay model [19], modified cam-clay model [19], PZ model [20], and so on are the universally accepted nonlinear elastic or elastic-plastic constitutive models. The Duncan-Chang constitutive model has been widely used in engineering practices [21; 22; 23], as it can reflect the main deformation laws of rock and soil, and the model parameters can be simply obtained through a conventional triaxial shear test. Numerous works in the literature can be found on increasing the simulation ability of the Duncan-Chang model in soil materials [21; 24; 25]. Among all these achievements, few researchers have studied the feasibility of optimizing the constitutive model by inversion analysis. To sum up, monitoring data prediction has the advantage in processing monitoring data, but the data fitting method is not convincing enough. Inversion analysis has the advantage in obtaining material parameters relevant to the monitoring data, but it is still difficult to obtain calculation results that are highly consistent with the monitoring data by inversed material parameters. The Duncan-Chang model can be modified to obtain results closer to engineering practice, but few researchers have studied the feasibility of optimizing the constitutive model by inversion analysis. Thus, increasing the inversion accuracy of earth-rockfill dams during construction periods and improving the simulation ability of the Duncan-Chang constitutive model by studying the characteristic of inversed material parameters will be the main purpose of this article.