Similar to its effects on any type of cementitious composite, it is a well-known fact that sulfate attack has also a negative influence on engineering behavior of cement-stabilized soils. However, the level of degradation in engineering properties of the cement-stabilized soils still needs more scientific attention. In the light of this, a database including a total of 260 unconfined compression and chloride ion penetration tests on cement-stabilized kaolin specimens exposed to sulfate attack was constituted. The data include information about cement type (sulfate resistant—SR; normal portland (N) and pozzolanic—P), and its content (0, 5, 10 and 15%), sulfate type (sodium or magnesium sulfate) as well as its concentration (0.3, 0.5, 1%) and curing period (1, 7, 28 and 90 days). Using this database, linear and nonlinear regression analysis (RA), backpropagation neural networks and adaptive neuro-fuzzy inference techniques were employed to question whether these methods are capable of predicting unconfined compressive strength and chloride ion penetration of cement-stabilized clay exposed to sulfate attack. The results revealed that these methods have a great potential in modeling the strength and penetrability properties of cement-stabilized clays exposed to sulfate attack. While the performance of regression method is at an acceptable level, results show that adaptive neuro-fuzzy inference systems and backpropagation neural networks are superior in modeling.

与它对任何类型的水泥复合材料的影响相似，众所周知的事实是硫酸盐侵蚀对水泥稳定的土壤的工程行为也具有负面影响。但是，水泥稳定土的工程性能退化水平仍然需要更多的科学关注。有鉴于此，建立了一个数据库，该数据库包括对暴露于硫酸盐侵蚀的水泥稳定的高岭土样品进行的共260次无限制压缩和氯离子渗透测试。数据包括有关水泥类型（耐硫酸盐–SR；普通硅酸盐（N）和火山灰-P）及其含量（0％，5％，10％和15％），硫酸盐类型（硫酸钠或硫酸镁）及其相关信息。浓度（0.3、0.5、1％）和固化时间（1、7、28和90天）。使用此数据库，进行线性和非线性回归分析（RA），反向传播神经网络和自适应神经模糊推理技术被用来质疑这些方法是否能够预测受到硫酸盐侵蚀的水泥稳定粘土的无侧限抗压强度和氯离子渗透。结果表明，这些方法在模拟遭受硫酸盐侵蚀的水泥稳定粘土的强度和渗透性方面具有巨大潜力。虽然回归方法的性能处于可接受的水平，但结果表明，自适应神经模糊推理系统和反向传播神经网络在建模方面具有优势。