Intelligent Compaction for Quality Control and Acceptance for Soil and Base Compaction through Statistical Analysis

Abstract

This paper presents the results of the intelligent compaction data that were collected from various layers including subgrade soil, lime-treated subgrade, cement-treated base and flexible aggregate base layers. Sets of proof-mapping data were collected from each layer upon completion of compaction. The data was then downloaded and analyzed using a computer program. Based on the data analysis and field compaction observation, a new statistical methodology for analyzing intelligent compaction data is proposed. The method is used to assess the uniformity of soil and base compaction quality and this is successfully demonstrated through a case study. A typical normal distribution of an intelligent compaction dataset indicates that a good and uniform compaction is achieved. It is, therefore, possible to assess the compaction quality by evaluating the perfection of normal districtuion of an intelligent compaction (IC) dataset. The compaction uniformity is evaluated by a compaction uniformity index, which is defined as the ratio of the probability within the specified limits in a field compaction data distribution to the probability in a target normal distribution.

Roller-integrated compaction monitoring and assessment of high and low liquid limit silt subgrades using the Green spline interpolation algorithm

“…Nevertheless, notable disparities exist in the physical properties, construction procedures, and quality control indexes of different subgrade material types. Building upon the data obtained through RICM, encompassing various layers such as lime-treated subgrade, Si [24]proposed a new statistical methodology to assess the uniformity of soil and subgrade compaction quality. Using the CCS900-𝐶𝑀𝑉, Wang et al [25] conducted an in-depth investigation on the evaluation of compaction uniformity based on geostatistics for sandy slate subgrade.…”