中文摘要：

针对岩土工程中由于数据较少导致无法精确研究模型不确定性这一问题，基于数理统计理论和贝叶斯统计方法，提出了岩土工程数据处理的贝叶斯优化方法。收集了南非地区29根无黏性土中和59根黏性土中桩的承载力资料，并将承载力实测值和理论计算值的比定义为承载力的模型因子。利用本文提出的方法将收集的数据分为“好数据”、“一般数据”和“坏数据”，剔除了对计算结果造成较大误差的“坏数据”，并对“一般数据”进行贝叶斯优化。利用中心点法、验算点法和蒙特卡洛模拟法计算出桩的承载力的可靠度。计算结果表明：数据的分类和优化对可靠度指标和抗力系数计算结果有显著的影响，其中利用“好数据”和“更新后的数据”的计算结果大于利用其他类型数据的计算结果。最后，根据计算结果和美国的桥梁荷载抗力设计规范给出了打入桩抗力系数的建议值。本文的研究成果可为相关的研究人员以及相关规范的编制提供参考。

英文摘要：

According to the mathematical statistics theory and Bayesian technique, a method for data processing and optimization in geotechnical engineering is put forward to solve the problem caused by model uncertainty due to lack of enough accurate field data. Meanwhile, the data of bearing capacity of piles in non-cohesive soils （29 piles） and in cohesive soils （59 piles） in South Africa are collected. The model factor of the bearing capacity is defined as the ratio of the measured capacity to the predicted one. By means of the proposed method, the collected data are sorted into three categories, which are good data, ordinary data and abnormal data. The abnormal data are discarded because of their adverse influence on calculation, and the ordinary data are optimized. The first order second moment method, the advanced first order second moment method and the Monte Carlo simulation are employed to calculate the reliability of the beating capacity. The calculated results show that the sorting and the optimization of data have great influence on the calculated results of reliability and resistance factors. For instance, the calculated results using the good data and the optimized data are larger than those using other data. Finally, the recommended values of resistance factors of driven piles are suggested according to the calculated results and American specifications for load and resistance factor design. The proposed method can offer references to the researchers and for the amendment of relevant specifications.