Accurate soil classification is essential for pavement design;however,the traditional American Association of State Highway and Transportation Officials(AASHTO)classification system relies on extensive laboratory test...Accurate soil classification is essential for pavement design;however,the traditional American Association of State Highway and Transportation Officials(AASHTO)classification system relies on extensive laboratory testing and subjective judgment.This study presents an artificial intelligence(AI)enhanced framework for AASHTO soil classification.A synthetic dataset of 349,015 samples was generated using parameter ranges for five AASHTO input variables to support model development.Four machine learning models were trained,analyzed,and compared where the random forest(RF)consistently achieved the highest accuracy of 100%among the four models in predicting AASHTO soil groups.Feature importance analysis indicates that percent passing the No.200 sieve is the most influential factor,and under missing input scenarios.Additionally,the models remain reliable under partial input loss,though accuracy is most sensitive to the absence of percent passing the No.200 sieve,dropping to 85.8%,while all other variables maintain accuracies of at least 93.1%.Prediction uncertainty using Monte Carlo simulations shows model performance within a 95%confidence interval.Overall,the proposed AI models can accurately and efficiently predict AASHTO soil groups using incomplete datasets for geotechnical engineering.展开更多
为研究国内外桩基设计差异,结合伊朗德黑兰至伊斯法罕高速铁路建设,开展了中国《建筑桩基技术规范》与美国AASHTO 《Design and Construction of Driven Pile Foundations》的桩基设计区别分析,采用两种标准进行盐湖段预制桩的承载力与...为研究国内外桩基设计差异,结合伊朗德黑兰至伊斯法罕高速铁路建设,开展了中国《建筑桩基技术规范》与美国AASHTO 《Design and Construction of Driven Pile Foundations》的桩基设计区别分析,采用两种标准进行盐湖段预制桩的承载力与沉降计算。结果表明:在承载力计算方面,中国标准和AASHTO标准分别采用安全系数法与作用抗力系数法,但二者的计算原理是相同的;在沉降计算方面,中国标准和AASHTO标准均采用分层总和法,中国标准采用Boussinesq理论且沉降计算为压缩模量法,主要适应于应力历史影响小的地基沉降设计;而AASHTO标准采用应力扩散角法分析附加应力传递规律且沉降计算为e-logp曲线法,其特征在于可有效考虑地层应力历史,采用中国标准和AASHTO计算的盐湖段预制桩承载力与沉降结果差异较小,且反算桩长也基本一致。展开更多
文摘Accurate soil classification is essential for pavement design;however,the traditional American Association of State Highway and Transportation Officials(AASHTO)classification system relies on extensive laboratory testing and subjective judgment.This study presents an artificial intelligence(AI)enhanced framework for AASHTO soil classification.A synthetic dataset of 349,015 samples was generated using parameter ranges for five AASHTO input variables to support model development.Four machine learning models were trained,analyzed,and compared where the random forest(RF)consistently achieved the highest accuracy of 100%among the four models in predicting AASHTO soil groups.Feature importance analysis indicates that percent passing the No.200 sieve is the most influential factor,and under missing input scenarios.Additionally,the models remain reliable under partial input loss,though accuracy is most sensitive to the absence of percent passing the No.200 sieve,dropping to 85.8%,while all other variables maintain accuracies of at least 93.1%.Prediction uncertainty using Monte Carlo simulations shows model performance within a 95%confidence interval.Overall,the proposed AI models can accurately and efficiently predict AASHTO soil groups using incomplete datasets for geotechnical engineering.
文摘为研究国内外桩基设计差异,结合伊朗德黑兰至伊斯法罕高速铁路建设,开展了中国《建筑桩基技术规范》与美国AASHTO 《Design and Construction of Driven Pile Foundations》的桩基设计区别分析,采用两种标准进行盐湖段预制桩的承载力与沉降计算。结果表明:在承载力计算方面,中国标准和AASHTO标准分别采用安全系数法与作用抗力系数法,但二者的计算原理是相同的;在沉降计算方面,中国标准和AASHTO标准均采用分层总和法,中国标准采用Boussinesq理论且沉降计算为压缩模量法,主要适应于应力历史影响小的地基沉降设计;而AASHTO标准采用应力扩散角法分析附加应力传递规律且沉降计算为e-logp曲线法,其特征在于可有效考虑地层应力历史,采用中国标准和AASHTO计算的盐湖段预制桩承载力与沉降结果差异较小,且反算桩长也基本一致。
