为了获得压制生土砖抗压强度预测模型，针对成型压力、含水率、水泥含量及高厚比对压制生土砖抗压强度的影响，基于国内外研究中的 91 组压制生土砖抗压强度试验数据，分别建立了用于预测压制生土砖抗压强度的 BP 神经网络模型和径向基(RBF) 神经网络模型，并将模型预测结果分别与试验结果及回归分析模型进行对比。结果表明: 人工神经网络模型对压制生土砖抗压强度的预测精度显著优于回归分析方法; 压制生土砖抗压强度与其配合比、成型压力及高厚比间存在复杂的非线性关系，回归分析模型不适用于解决此类复杂问题; BP 神经网络模型的整体预测效果较好，但容易陷入局部最优; ＲBF 神经网络模型能可靠地预测压制生土砖抗压强度，预测结果与试验结果比值的平均值为 1. 007，标准差为 0. 085，该预测模型具有较高精确度，能有效解决压制生土砖抗压强度与其影响因素间复杂的非线性关系，可为压制生土砖的配合比设计提供参考。

A BP neural network model and a radial basis (ＲBF) neural network model were set up respectively to predict the compressive strength of compacted earth blocks． The prediction model for the compressive strength of compacted earth blocks was based on 91 groups of experimental research data collected under the action of multiple factors by domestic and foreign scholars，and the influence of compaction pressure，moisture content，cement con-tent and height-thickness ratio on the compressive strength of compacted earth block was considered． The neural network predicted results were compared with the predicted results of regression analysis model and the experimental values． The results show that the prediction accuracy of artificial neural network model is better than that of regres-sion analysis model． And there is a complex nonlinear relationship that the regression analysis model cannot handle between the compressive strength of the compacted earth block and its mixture ratio，compaction pressure and height-to-thickness ratio． The overall prediction effect of BP neural network model is good，but it is easy to fall in-to local optimum． ＲBF neural network model can reliably predict the compressive strength of compacted earth blocks． The mean value of the ratio between the predicted results and the experimental values is 1. 007，with a standard deviation of 0. 085． The prediction model has a high accuracy and can effectively solve the complex non-linear relationship between compressive strength and its influencing factors，and it can provide reference for the de-sign of mix proportion for compacted earth blocks．