The prediction model is proposed in this paper to predict the displacement of foundation pit. In the model, genetic algorithms is applied to optimize the node function of the neural network (15 node function coefficients are optimized simultaneously). Next, do the further optimization to the model, and GA-transFcn3 Model is established whose fitness evaluation takes into account the multi-step prediction error. Finally, it is verified that the GA-transFcn3 Model created in this article has the desirable prediction accuracy through engineering examples. The establishment of GA-transFcn3 Model can provide researchers and engineers with ideas and methods for the displacement prediction of foundation pit, and can be popularized and applied in practical projects.

It is ascertained that the indicators weight system by the Analytic Hierarchy Process and constructed the model of success-degree evaluation method to execute the comprehensive evaluation for bridge preservation and replacement project based on the existing indicators system of bridge strengthening project with the purpose of success-degree in program evaluation. The success-degree classification of the bridge preservation and replacement project improves the system of post-evaluation for bridge preservation and replacement project through division of great success, success, partial success, most failure and failure. It indicates that the evaluation model is simple to assess and conform to the reality.