A Machine-Learning Approach for Extracting Modulus of Compacted Unbound Aggregate Base and Subgrade Materials Using Intelligent Compaction Technology

This study presents a rigorous approach for the extraction of the modulus of soil and unbound aggregate base materials for quality management using intelligent compaction (IC) technology. The proposed approach makes use of machine-learning methods in tandem with IC technology and modulus-based spot testing as a local calibration process to estimate the mechanical properties of compacted geomaterials. A calibrated three-dimensional finite element (FE) model that simulates the proof-mapping process of compacted geomaterials was used to develop a comprehensive database of responses of a wide range of single and two-layered geosystems. The database was then used to develop different inverse solvers using artificial neural networks for the estimation of the modulus from the characteristics of the roller and information about the geomaterials. Several instrumented test sites were used for the evaluation and validation of the inverse solvers. The proposed approach was found promising for the extraction of the modulus of compacted geomaterials using IC. The accuracy of the inverse solvers is enhanced if a local calibration process is incorporated as part of a quality management program that includes the use of in situ measurements using modulus-based test devices and laboratory resilient modulus testing. Moreover, compaction uniformity plays a key role in the retrieval of the modulus of geomaterials with certainty. The proposed approach fuses artificial intelligence with mechanistic solutions to position IC as a technology that is well suited for the quality management of compacted materials.

Research on Intelligent Compaction Technology of Subgrade Based on Regression Analysis

A remote monitoring system with the intelligent compaction index CMV as the core is designed and developed to address the shortcomings of traditional subgrade compaction quality evaluation methods. Based on the actual project, the correlation between the CMV and conventional compaction indexes of compaction degree K and dynamic resilient modulus E is investigated by applying the one-dimensional linear regression equation for three types of subgrade fillers, clayey gravel, pulverized gravel, and soil-rock mixed fill, and the scheme of fitting CMV to the mean value of conventional indexes is adopted, which is compared with the scheme of fitting CMV to the single point of conventional indexes in the existing specification. The test results show that the correlation between the CMV and conventional indexes of clayey gravel and pulverized gravel is much stronger than that of soil-rock mixed subgrades, and the correlation coefficient can be significantly improved by fitting CMV to the mean of conventional indexes compared with single-point fitting, which can be considered as a new method for intelligent rolling correlation verification.

Developing Guidelines for Assessing the Effectiveness of Intelligent Compaction Technology

Many factors affect pavement compaction quality, which can vary. Such variability may result in an additional number of passes required, extended working hours, higher energy consumption, and negative environmental impacts. The use of Intelligent Compaction (IC) technology during construction can improve the quality and longevity of pavement structures while reducing risk for contractors and project owners alike. This study develops guidelines for the implementation of IC in the compaction of pavement layers as well as performing a preliminary life-cycle cost analysis (LCCA) of IC technology compared to the conventional compaction approach. The environmental impacts of the improved construction process were quantified based on limited data available from the case studies. The LCCA performed in this study consisted of different scenarios in which the number of operating hours was evaluated to estimate the cost efficiency of the intelligent compaction technique during construction. The analyses showed a reduction in energy consumption and the production of greenhouse gas (GHG) emissions with the use of intelligent compaction. The LCCA showed that the use of IC technology may reduce the construction and maintenance costs in addition to enhancing the quality control and quality assurance (QC/QA) process. However, a more comprehensive analysis is required to fully quantify the benefits and establish more accurate performance indicators. A draft version of the preliminary guidelines for implementation of IC technology and long-term monitoring of the performance of pavement layers compacted thereby is also included in this report.