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Prediction of California bearing ratio (CBR) for pavement subgrade soils in saturated and unsaturated conditions
Ng I.-T.; Yuen K.-V.
2015
AbstractPavements are commonly designed by assuming the subgrade soil layer to be in saturated condition in which the California Bearing Ratio (CBR) is used as an index for assessing the strength of the subgrade. However, most subgrade soils are in unsaturated condition and the assumption of saturated condition leads to overdesign of the pavement. In other words, it is uneconomical to design under the unsaturated condition regardless of the true status. However, it is difficult to obtain the unsaturated soil parameters for pavement subgrade since it involves expensive sophisticate testing equipment and high skillful operators. As a result, one can only rely on some published saturated CBR-based models for unsaturated condition. Unfortunately, determining CBR under saturated condition in the laboratory is not only uneconomical but also time consuming. Engineers encounter difficulties in obtaining reliable CBR values for pavement design. In this regard, this chapter presents a Bayesian probabilistic framework in establishing an effective and reliable model for CBR prediction based on a database containing basic soil index properties such as liquid limit, plastic limit, plasticity, maximum dry density and optimum water content. The proposed model is believed to have great contribution towards better design and maintenance practice for pavement.
KeywordBayesian model class selection CBR Compaction characteristics Index properties Unsaturated soils
ISBN9781634822466;9781634822459;
URLView the original
Pages153-168
Language英語
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Document TypeBook
专题University of Macau
AffiliationUniversidade de Macau
推荐引用方式
GB/T 7714
Ng I.-T.,Yuen K.-V.. Prediction of California bearing ratio (CBR) for pavement subgrade soils in saturated and unsaturated conditions[M],2015.
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