# Field Evaluation and Application of Intelligent Quality Control Systems
## 1 Introduction  
Compacting the subgrade and roadbed at every stage of road construction is critical for ensuring road quality and lifespan. Therefore, quality during construction is very important. Repetitive compaction work is essential to achieve the required compaction level after filling. Currently, the plate load test (PLT) and field density test (FDT) are the most common methods for assessing post-compaction quality. Firstly, performing on-site PLT and FDT measurements requires considerable effort, as it necessitates engineers to conduct each test individually. Secondly, only a few measurements are taken at specific locations, which are then used to evaluate the compaction quality across the entire road construction site. This method raises concerns about the accuracy of these assessments due to the sparse distribution of their measurements. Thirdly, only a limited number of inspectors are available, who are typically called in to perform these measurements only after the compaction process has been completed. The absence of immediate feedback and the inability to adjust processes in real time indicate missed opportunities to enhance the quality of compaction and expedite projects by ensuring optimal compaction on the first attempt, thus preventing the need for any rework [1]. Moreover, the repeated construction and inspection of road constructions and estate developments underscore the need for technological developments to resolve inefficiencies. With quality verification methods relying on manpower, ensuring uniform quality across all sections is impossible, leading to decreased accuracy and excessive time consumption. Thus, a technology capable of automating quality monitoring is required. Currently, the sand cone test and PLT are typically used for compaction quality inspection in civil engineering. However, these methods depend on the tester's judgment regarding test location selection, and their accuracy varies depending on the tester's expertise. Therefore, current testing methods cannot quantitatively verify compaction quality, and cost and time constraints limit their inspection coverage. To overcome these limitations, intelligent quality control technologies have been developed for use during compaction work [2; 3; 4]. Following the development of IT technologies and the Federal Highway Administration (FHWA)'s 2004 IC Roadmap, the development and field application of intelligent compaction (IC) technologies have accelerated. Original equipment manufacturers (OEMs) initially led the development of compaction rollers, resulting in different assessment metrics across equipment. Recently, a commercial retrofit kit using the compaction meter value (CMV) was developed, allowing for module usage irrespective of the OEM and its application in IC rollers, broadening the technology's adoption. The continuous compaction control (CCC) method allowed for operators to assess the compaction quality on-site simultaneously with compaction work [5; 6; 7]. In the U.S., IC technology was first used in highway construction for Minnesota's TH-64 project, which was recognized as the first U.S. project requiring IC technology [8]. Several states are now applying IC technology for quality control (QC) and quality assurance (QA) [9; 10]. The CCC method involves fitting various sensors on compaction rollers to measure the ground stiffness through the relationship between roller vibrations and the ground's reaction (rebound). Rollers equipped with CCC technology are called IC rollers, and many standard specifications have been established internationally [11]. In earthworks, applying new technologies requires reliable public standards, and the U.S. and a few European countries are leading in establishing such IC standards. Austria first introduced IC standards in 1990, followed by Germany, Sweden, and Switzerland. The International Society for Soil Mechanics and Geotechnical Engineering (ISSMGE) issued related guidelines in 2005 based on Austrian standards.