中文摘要：

分析总结了广东省内软土的类型、形成环境及特点。以粤东地区某高速公路典型软基断面为例系统分析了其软土的成分、厚度、埋深和物理力学指标及软基沉降特征，进一步开展了基于双曲线法、指数法、泊松曲线法和Asaoka法的软土路基沉降预测。研究结果表明，山地型软土、平原型软土、滨海型软土、沼泽相软土在广东省内均有发育，各类型软土因形成环境的差异而各自具有明显的特点。在本研究选择的工程案例中，软土主要为深灰色淤泥质粉质粘土，其分布厚度较大且物理力学性质极差。沉降预测结果表明，双曲线法和指数法的预测结果与实际观测结果中后期沉降放缓并趋于稳定的现象明显不符，而泊松曲线法和Asaoka法的沉降量预测结果与实际沉降观测结果更加吻合。鉴于不同预测方法的考虑因素不同且预测结果具有或多或少的互补效果，选择了与实际情况更加吻合的泊松曲线法和Asaoka法沉降量预测结果的平均值作为最终预测结果。

英文摘要：

Bad engineering properties of soft clay in soft soil roadbed could easily lead to diseases, which will reduce highway driving speed and occupant comfort, as well as contributing to damages of vehicles and highways. However, highway constructions in soft soil areas have become more and more common because of the geological and geographical conditions, regional economic development needs and the limits of highway construction costs and land resources. Therefore, in this study, the types, formation and characteristics of soft soil in Guangdong Province have been analyzed. Taking a typical cross section of a soft ground of a highway in eastern Guangdong as an example, the compositions, thicknesses, buried depths, physical-mechanical indexes and soft foundation settlement characteristics of soft soil have been analyzed, a prediction of soft soil roadbed settlement based on hyperbola method, index method, Poisson curve method and Asaoka method has been carried out. The results have shown that mountain type, plain type, coastal type and swamp type of soft soil have been all distributed in Guangdong Province, different kinds of soft soil have distinct characteristics because of the different formation environments. In the case of this study, the soft soil is mainly dark gray muddy silty clay, which has a large thickness of distribution and poor physical-mechanical properties.The settlement prediction results have indicated that results predicted by the hyperbolic method and index method are different from the observed results, whereas results predicted by Poisson curve method and Asaoka method are consistent with the actual settlement observation results. As a consequence, the average values of the results predicted by both Poisson curve method and Asaoka method have been finally chosen.