基于改进遗传算法的地震后重建工程造价模型改进设计

传统依据BP神经网络的地震后重建工程造价模型求导运算过程复杂,收敛效率较低,造价结果准确性低。提出基于改进遗传算法的地震后重建工程造价模型,结合地震后重建工程造价的影响因素,通过算法优化造价模型,选择更好的造价模拟数据,模拟数据构建造价函数模型,利用T系数运算分析造价函数模型数据,采用二进制计算规律对造价函数的数据参数实施拟定,通过公式演算获取高精度的数据参数值,得到精确的造价数据。实验结果表明:所设计的模型能够准确、快速的对地震后重建工程造价进行预算。

Improved Construction Cost Model for Post-earthquake Reconstruction Based on Improved Genetic Algorithm

The traditional model used for computing the costs of post-earthquake reconstruction projects is based on the back-propagation neural network. It requires a complex computation process and provides low convergence efficiency and low-accuracy results. In this work, a model based on the improved genetic algorithm for computing the costs of post-earthquake reconstruction projects is proposed. The cost model is optimized in accordance with the factors that influence the cost of post-earthquake reconstruction projects, and the cost function model is established with superior cost simulation data. The data of the cost function model are analyzed on the basis of the T coefficient, and the data parameters of the cost function model are determined on the basis of the binary calculation law. Then, data parameters with high precision are obtained through formula calculus, and accurate cost data are obtained. Experimental results show that the designed model can accurately and quickly estimate the cost of post-earthquake reconstruction projects.